diff --git a/sw_lab9-10_1.ipynb b/sw_lab9-10_1.ipynb
index 5632c1c..0c40fcc 100644
--- a/sw_lab9-10_1.ipynb
+++ b/sw_lab9-10_1.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 41,
    "id": "2fe63b50",
    "metadata": {},
    "outputs": [],
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 45,
    "id": "cc941c5a",
    "metadata": {},
    "outputs": [],
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 46,
    "id": "25040ac9",
    "metadata": {},
    "outputs": [],
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 47,
    "id": "a1fe47e6",
    "metadata": {},
    "outputs": [],
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,133 +298,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import keras,os\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "import numpy as np\n",
-    "\n",
-    "model = keras.models.Sequential([\n",
-    "    keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
-    "    keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Flatten(),\n",
-    "    keras.layers.Dense(units = 4096, activation='relu'),\n",
-    "    keras.layers.Dense(units = 4096, activation='relu'),\n",
-    "    keras.layers.Dense(units = 5, activation='softmax')\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.optimizers import Adam\n",
-    "opt = Adam(lr=0.001)\n",
-    "model.compile(optimizer=opt, loss=keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential_1\"\n",
+      "Model: \"model_3\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " conv2d_13 (Conv2D)          (None, 224, 224, 64)      1792      \n",
+      " input_4 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
       "                                                                 \n",
-      " conv2d_14 (Conv2D)          (None, 224, 224, 64)      36928     \n",
+      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
       "                                                                 \n",
-      " max_pooling2d_4 (MaxPooling  (None, 112, 112, 64)     0         \n",
-      " 2D)                                                             \n",
+      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
       "                                                                 \n",
-      " conv2d_15 (Conv2D)          (None, 112, 112, 128)     73856     \n",
+      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
       "                                                                 \n",
-      " conv2d_16 (Conv2D)          (None, 112, 112, 128)     147584    \n",
+      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
       "                                                                 \n",
-      " max_pooling2d_5 (MaxPooling  (None, 56, 56, 128)      0         \n",
-      " 2D)                                                             \n",
+      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
       "                                                                 \n",
-      " conv2d_17 (Conv2D)          (None, 56, 56, 256)       295168    \n",
+      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
       "                                                                 \n",
-      " conv2d_18 (Conv2D)          (None, 56, 56, 256)       590080    \n",
+      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
       "                                                                 \n",
-      " conv2d_19 (Conv2D)          (None, 56, 56, 256)       590080    \n",
+      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
       "                                                                 \n",
-      " max_pooling2d_6 (MaxPooling  (None, 28, 28, 256)      0         \n",
-      " 2D)                                                             \n",
+      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
       "                                                                 \n",
-      " conv2d_20 (Conv2D)          (None, 28, 28, 512)       1180160   \n",
+      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
       "                                                                 \n",
-      " conv2d_21 (Conv2D)          (None, 28, 28, 512)       2359808   \n",
+      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
       "                                                                 \n",
-      " conv2d_22 (Conv2D)          (None, 28, 28, 512)       2359808   \n",
+      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
       "                                                                 \n",
-      " max_pooling2d_7 (MaxPooling  (None, 14, 14, 512)      0         \n",
-      " 2D)                                                             \n",
+      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
       "                                                                 \n",
-      " conv2d_23 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
+      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
       "                                                                 \n",
-      " conv2d_24 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
+      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
       "                                                                 \n",
-      " conv2d_25 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
+      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
       "                                                                 \n",
-      " flatten_2 (Flatten)         (None, 100352)            0         \n",
+      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
       "                                                                 \n",
-      " dense_4 (Dense)             (None, 4096)              411045888 \n",
+      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
       "                                                                 \n",
-      " dense_5 (Dense)             (None, 4096)              16781312  \n",
+      " flatten_3 (Flatten)         (None, 25088)             0         \n",
       "                                                                 \n",
-      " dense_6 (Dense)             (None, 5)                 20485     \n",
+      " dense_3 (Dense)             (None, 5)                 125445    \n",
       "                                                                 \n",
       "=================================================================\n",
-      "Total params: 442,562,373\n",
-      "Trainable params: 442,562,373\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
+      "Total params: 14,840,133\n",
+      "Trainable params: 125,445\n",
+      "Non-trainable params: 14,714,688\n",
+      "_________________________________________________________________\n",
       "Epoch 1/25\n"
      ]
     },
@@ -432,124 +363,158 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_2029/3158629982.py:4: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
-      "  hist_vgg = model.fit_generator(steps_per_epoch=len(train_ds), generator=train_ds, validation_data= validation_ds, validation_steps=len(validation_ds), epochs=25, callbacks=[checkpoint,early])\n"
+      "/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_11345/3456911324.py:75: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+      "  vggr = model.fit_generator(\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25/25 [==============================] - ETA: 0s - loss: 1.6264 - accuracy: 0.1900 \n",
-      "Epoch 1: val_accuracy improved from -inf to 0.18229, saving model to vgg16_1.h5\n",
-      "25/25 [==============================] - 854s 34s/step - loss: 1.6264 - accuracy: 0.1900 - val_loss: 1.6109 - val_accuracy: 0.1823\n",
+      "25/25 [==============================] - 117s 5s/step - loss: 1.4384 - accuracy: 0.4363 - val_loss: 0.8596 - val_accuracy: 0.6719\n",
       "Epoch 2/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6108 - accuracy: 0.1950 \n",
-      "Epoch 2: val_accuracy improved from 0.18229 to 0.18750, saving model to vgg16_1.h5\n",
-      "25/25 [==============================] - 897s 36s/step - loss: 1.6108 - accuracy: 0.1950 - val_loss: 1.6098 - val_accuracy: 0.1875\n",
+      "25/25 [==============================] - 121s 5s/step - loss: 0.6040 - accuracy: 0.7975 - val_loss: 0.6615 - val_accuracy: 0.7552\n",
       "Epoch 3/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6097 - accuracy: 0.2062 \n",
-      "Epoch 3: val_accuracy improved from 0.18750 to 0.19792, saving model to vgg16_1.h5\n",
-      "25/25 [==============================] - 870s 35s/step - loss: 1.6097 - accuracy: 0.2062 - val_loss: 1.6102 - val_accuracy: 0.1979\n",
+      "25/25 [==============================] - 126s 5s/step - loss: 0.3955 - accuracy: 0.9000 - val_loss: 0.5536 - val_accuracy: 0.7969\n",
       "Epoch 4/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6097 - accuracy: 0.2037 \n",
-      "Epoch 4: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 692s 28s/step - loss: 1.6097 - accuracy: 0.2037 - val_loss: 1.6106 - val_accuracy: 0.1979\n",
+      "25/25 [==============================] - 124s 5s/step - loss: 0.3278 - accuracy: 0.9237 - val_loss: 0.5154 - val_accuracy: 0.8438\n",
       "Epoch 5/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6095 - accuracy: 0.1963 \n",
-      "Epoch 5: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 634s 26s/step - loss: 1.6095 - accuracy: 0.1963 - val_loss: 1.6114 - val_accuracy: 0.1823\n",
+      "25/25 [==============================] - 124s 5s/step - loss: 0.2700 - accuracy: 0.9350 - val_loss: 0.5352 - val_accuracy: 0.7969\n",
       "Epoch 6/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6094 - accuracy: 0.1925 \n",
-      "Epoch 6: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 643s 26s/step - loss: 1.6094 - accuracy: 0.1925 - val_loss: 1.6112 - val_accuracy: 0.1719\n",
+      "25/25 [==============================] - 119s 5s/step - loss: 0.2109 - accuracy: 0.9538 - val_loss: 0.3983 - val_accuracy: 0.8854\n",
       "Epoch 7/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6095 - accuracy: 0.2025 \n",
-      "Epoch 7: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 697s 28s/step - loss: 1.6095 - accuracy: 0.2025 - val_loss: 1.6115 - val_accuracy: 0.1823\n",
+      "25/25 [==============================] - 117s 5s/step - loss: 0.1713 - accuracy: 0.9812 - val_loss: 0.3841 - val_accuracy: 0.8802\n",
       "Epoch 8/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6097 - accuracy: 0.1762 \n",
-      "Epoch 8: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 667s 27s/step - loss: 1.6097 - accuracy: 0.1762 - val_loss: 1.6106 - val_accuracy: 0.1979\n",
+      "25/25 [==============================] - 115s 5s/step - loss: 0.1519 - accuracy: 0.9850 - val_loss: 0.3871 - val_accuracy: 0.8854\n",
       "Epoch 9/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6096 - accuracy: 0.2025 \n",
-      "Epoch 9: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 656s 26s/step - loss: 1.6096 - accuracy: 0.2025 - val_loss: 1.6103 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 117s 5s/step - loss: 0.1412 - accuracy: 0.9800 - val_loss: 0.4005 - val_accuracy: 0.8958\n",
       "Epoch 10/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6095 - accuracy: 0.1950 \n",
-      "Epoch 10: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 651s 26s/step - loss: 1.6095 - accuracy: 0.1950 - val_loss: 1.6104 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 116s 5s/step - loss: 0.1176 - accuracy: 0.9900 - val_loss: 0.3657 - val_accuracy: 0.9062\n",
       "Epoch 11/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6094 - accuracy: 0.2062 \n",
-      "Epoch 11: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 646s 26s/step - loss: 1.6094 - accuracy: 0.2062 - val_loss: 1.6105 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 116s 5s/step - loss: 0.1200 - accuracy: 0.9825 - val_loss: 0.3862 - val_accuracy: 0.8646\n",
       "Epoch 12/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6095 - accuracy: 0.2062 \n",
-      "Epoch 12: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 648s 26s/step - loss: 1.6095 - accuracy: 0.2062 - val_loss: 1.6103 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 116s 5s/step - loss: 0.0958 - accuracy: 0.9912 - val_loss: 0.3412 - val_accuracy: 0.9010\n",
       "Epoch 13/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6096 - accuracy: 0.2025 \n",
-      "Epoch 13: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 636s 26s/step - loss: 1.6096 - accuracy: 0.2025 - val_loss: 1.6108 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 113s 5s/step - loss: 0.0914 - accuracy: 0.9925 - val_loss: 0.3484 - val_accuracy: 0.8854\n",
       "Epoch 14/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6097 - accuracy: 0.2050 \n",
-      "Epoch 14: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 664s 27s/step - loss: 1.6097 - accuracy: 0.2050 - val_loss: 1.6110 - val_accuracy: 0.1875\n",
+      "25/25 [==============================] - 115s 5s/step - loss: 0.0799 - accuracy: 0.9950 - val_loss: 0.3406 - val_accuracy: 0.8906\n",
       "Epoch 15/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6096 - accuracy: 0.1775 \n",
-      "Epoch 15: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 657s 27s/step - loss: 1.6096 - accuracy: 0.1775 - val_loss: 1.6105 - val_accuracy: 0.1875\n",
+      "25/25 [==============================] - 118s 5s/step - loss: 0.0714 - accuracy: 0.9975 - val_loss: 0.3355 - val_accuracy: 0.8958\n",
       "Epoch 16/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6095 - accuracy: 0.2000 \n",
-      "Epoch 16: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 664s 27s/step - loss: 1.6095 - accuracy: 0.2000 - val_loss: 1.6102 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 121s 5s/step - loss: 0.0728 - accuracy: 0.9950 - val_loss: 0.3384 - val_accuracy: 0.9062\n",
       "Epoch 17/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6094 - accuracy: 0.1937 \n",
-      "Epoch 17: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 676s 27s/step - loss: 1.6094 - accuracy: 0.1937 - val_loss: 1.6104 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 120s 5s/step - loss: 0.0674 - accuracy: 0.9962 - val_loss: 0.3627 - val_accuracy: 0.8958\n",
       "Epoch 18/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6093 - accuracy: 0.1975 \n",
-      "Epoch 18: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 673s 27s/step - loss: 1.6093 - accuracy: 0.1975 - val_loss: 1.6103 - val_accuracy: 0.1823\n",
+      "25/25 [==============================] - 118s 5s/step - loss: 0.0580 - accuracy: 0.9962 - val_loss: 0.3231 - val_accuracy: 0.9115\n",
       "Epoch 19/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6094 - accuracy: 0.2050 \n",
-      "Epoch 19: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 681s 27s/step - loss: 1.6094 - accuracy: 0.2050 - val_loss: 1.6111 - val_accuracy: 0.1771\n",
+      "25/25 [==============================] - 118s 5s/step - loss: 0.0509 - accuracy: 0.9987 - val_loss: 0.3387 - val_accuracy: 0.8958\n",
       "Epoch 20/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6093 - accuracy: 0.2050 \n",
-      "Epoch 20: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 672s 27s/step - loss: 1.6093 - accuracy: 0.2050 - val_loss: 1.6108 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 119s 5s/step - loss: 0.0492 - accuracy: 0.9987 - val_loss: 0.3076 - val_accuracy: 0.8906\n",
       "Epoch 21/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6094 - accuracy: 0.2050 \n",
-      "Epoch 21: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 663s 27s/step - loss: 1.6094 - accuracy: 0.2050 - val_loss: 1.6110 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 1405s 58s/step - loss: 0.0458 - accuracy: 0.9987 - val_loss: 0.3350 - val_accuracy: 0.8854\n",
       "Epoch 22/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6096 - accuracy: 0.1850 \n",
-      "Epoch 22: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 675s 27s/step - loss: 1.6096 - accuracy: 0.1850 - val_loss: 1.6111 - val_accuracy: 0.1927\n",
+      "25/25 [==============================] - 1635s 68s/step - loss: 0.0458 - accuracy: 0.9975 - val_loss: 0.3148 - val_accuracy: 0.9062\n",
       "Epoch 23/25\n",
-      "25/25 [==============================] - ETA: 0s - loss: 1.6092 - accuracy: 0.1963 \n",
-      "Epoch 23: val_accuracy did not improve from 0.19792\n",
-      "25/25 [==============================] - 664s 27s/step - loss: 1.6092 - accuracy: 0.1963 - val_loss: 1.6110 - val_accuracy: 0.1823\n",
-      "Epoch 23: early stopping\n"
+      "25/25 [==============================] - 103s 4s/step - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.3446 - val_accuracy: 0.8750\n",
+      "Epoch 24/25\n",
+      "25/25 [==============================] - 106s 4s/step - loss: 0.0387 - accuracy: 0.9987 - val_loss: 0.2885 - val_accuracy: 0.9062\n",
+      "Epoch 25/25\n",
+      "25/25 [==============================] - 109s 4s/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.2845 - val_accuracy: 0.8958\n"
      ]
     }
    ],
    "source": [
-    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
-    "checkpoint = ModelCheckpoint(\"vgg16_1.h5\", monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
-    "early = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=20, verbose=1, mode='auto')\n",
-    "hist_vgg = model.fit_generator(steps_per_epoch=len(train_ds), generator=train_ds, validation_data= validation_ds, validation_steps=len(validation_ds), epochs=25, callbacks=[checkpoint,early])"
+    "import keras,os\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "import numpy as np\n",
+    "from keras.applications import VGG16\n",
+    "from keras.layers import Input, Lambda, Dense, Flatten\n",
+    "from keras.models import Model\n",
+    "from keras.preprocessing import image\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.models import Sequential\n",
+    "import numpy as np\n",
+    "from glob import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ssl\n",
+    "ssl._create_default_https_context = ssl._create_unverified_context\n",
+    "\n",
+    "# model = keras.models.Sequential([\n",
+    "#     keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
+    "#     keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
+    "#     keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
+    "#     keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
+    "#     keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
+    "#     keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
+    "#     keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
+    "#     keras.layers.Flatten(),\n",
+    "#     keras.layers.Dense(units = 4096, activation='relu'),\n",
+    "#     keras.layers.Dense(units = 4096, activation='relu'),\n",
+    "#     keras.layers.Dense(units = 5, activation='softmax')\n",
+    "# ])\n",
+    "\n",
+    "# re-size all the images to this\n",
+    "IMAGE_SIZE = [224, 224]\n",
+    "\n",
+    "# add preprocessing layer to the front of resnet\n",
+    "vgg2 = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
+    "\n",
+    "# don't train existing weights\n",
+    "for layer in vgg2.layers:\n",
+    "  layer.trainable = False\n",
+    "  \n",
+    "  # useful for getting number of classes\n",
+    "classes = 5\n",
+    "  \n",
+    "\n",
+    "# our layers - you can add more if you want\n",
+    "x = Flatten()(vgg2.output)\n",
+    "# x = Dense(1000, activation='relu')(x)\n",
+    "prediction = Dense(5, activation='softmax')(x)\n",
+    "\n",
+    "# create a model object\n",
+    "model = Model(inputs=vgg2.input, outputs=prediction)\n",
+    "\n",
+    "# view the structure of the model\n",
+    "model.summary()\n",
+    "# tell the model what cost and optimization method to use\n",
+    "model.compile(\n",
+    "  loss='sparse_categorical_crossentropy',\n",
+    "  optimizer='adam',\n",
+    "  metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "#train_ds_vgg_sw, test_ds_vgg_sw, validation_ds_vgg_sw\n",
+    "# fit the model\n",
+    "vggr = model.fit_generator(\n",
+    "  train_ds,\n",
+    "  validation_data=validation_ds,\n",
+    "  epochs=25,\n",
+    "  steps_per_epoch=len(train_ds),\n",
+    "  validation_steps=len(validation_ds)\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -560,10 +525,10 @@
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "plt.plot(hist_vgg.history[\"accuracy\"])\n",
-    "plt.plot(hist_vgg.history['val_accuracy'])\n",
-    "plt.plot(hist_vgg.history['loss'])\n",
-    "plt.plot(hist_vgg.history['val_loss'])\n",
+    "plt.plot(vggr.history[\"accuracy\"])\n",
+    "plt.plot(vggr.history['val_accuracy'])\n",
+    "plt.plot(vggr.history['loss'])\n",
+    "plt.plot(vggr.history['val_loss'])\n",
     "plt.title(\"Model accuracy\")\n",
     "plt.ylabel(\"Value\")\n",
     "plt.xlabel(\"Epoch\")\n",
@@ -573,23 +538,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8/8 [==============================] - 35s 4s/step - loss: 1.6097 - accuracy: 0.1953\n"
+      "8/8 [==============================] - 29s 4s/step - loss: 0.3817 - accuracy: 0.8633\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[1.6096564531326294, 0.1953125]"
+       "[0.38167834281921387, 0.86328125]"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -603,12 +568,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ResNet50"
+    "## ResNet101V2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,13 +587,13 @@
     "import matplotlib.pyplot as plt\n",
     "import ssl\n",
     "ssl._create_default_https_context = ssl._create_unverified_context\n",
-    "from keras.applications import ResNet50\n",
+    "from keras.applications import ResNet101V2\n",
     "\n",
     "# re-size all the images to this\n",
     "IMAGE_SIZE = [224, 224]\n",
     "\n",
     "# add preprocessing layer to the front of resnet\n",
-    "resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
+    "resnet = ResNet101V2(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
     "\n",
     "# don't train existing weights\n",
     "for layer in resnet.layers:\n",
@@ -646,37 +611,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"model_1\"\n",
+      "Model: \"model_4\"\n",
       "__________________________________________________________________________________________________\n",
       " Layer (type)                   Output Shape         Param #     Connected to                     \n",
       "==================================================================================================\n",
-      " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
+      " input_5 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
       "                                )]                                                                \n",
       "                                                                                                  \n",
-      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_3[0][0]']                \n",
+      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_5[0][0]']                \n",
       "                                                                                                  \n",
       " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
-      "                                )                                                                 \n",
-      "                                                                                                  \n",
-      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
-      "                                )                                                                 \n",
-      "                                                                                                  \n",
-      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
+      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_conv[0][0]']             \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
       " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
       "                                                                                                  \n",
-      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
+      " conv2_block1_preact_bn (BatchN  (None, 56, 56, 64)  256         ['pool1_pool[0][0]']             \n",
+      " ormalization)                                                                                    \n",
+      "                                                                                                  \n",
+      " conv2_block1_preact_relu (Acti  (None, 56, 56, 64)  0           ['conv2_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4096        ['conv2_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -684,7 +650,10 @@
       " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
+      " conv2_block1_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36864       ['conv2_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -692,22 +661,22 @@
       " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
+      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block1_out (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_conv[0][0]',    \n",
+      "                                                                  'conv2_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block2_preact_bn (BatchN  (None, 56, 56, 256)  1024       ['conv2_block1_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
-      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
+      " conv2_block2_preact_relu (Acti  (None, 56, 56, 256)  0          ['conv2_block2_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
-      "                                                                                                  \n",
-      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
+      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16384       ['conv2_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -715,7 +684,10 @@
       " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
+      " conv2_block2_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36864       ['conv2_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -725,15 +697,17 @@
       "                                                                                                  \n",
       " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block2_out (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
+      "                                                                  'conv2_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
-      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
+      " conv2_block3_preact_bn (BatchN  (None, 56, 56, 256)  1024       ['conv2_block2_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
+      " conv2_block3_preact_relu (Acti  (None, 56, 56, 256)  0          ['conv2_block3_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
+      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16384       ['conv2_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -741,25 +715,32 @@
       " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
+      " conv2_block3_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
       "                                                                                                  \n",
-      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
+      " conv2_block3_2_conv (Conv2D)   (None, 28, 28, 64)   36864       ['conv2_block3_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv2_block3_2_bn (BatchNormal  (None, 28, 28, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
       "                                                                                                  \n",
-      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
+      " conv2_block3_2_relu (Activatio  (None, 28, 28, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
+      " max_pooling2d_3 (MaxPooling2D)  (None, 28, 28, 256)  0          ['conv2_block2_out[0][0]']       \n",
       "                                                                                                  \n",
-      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block3_3_conv (Conv2D)   (None, 28, 28, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
-      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
+      " conv2_block3_out (Add)         (None, 28, 28, 256)  0           ['max_pooling2d_3[0][0]',        \n",
+      "                                                                  'conv2_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
+      " conv3_block1_preact_bn (BatchN  (None, 28, 28, 256)  1024       ['conv2_block3_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
+      " conv3_block1_preact_relu (Acti  (None, 28, 28, 256)  0          ['conv3_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32768       ['conv3_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -767,7 +748,10 @@
       " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
+      " conv3_block1_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -775,22 +759,22 @@
       " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
+      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv3_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block1_out (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_conv[0][0]',    \n",
+      "                                                                  'conv3_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block2_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block1_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
-      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
+      " conv3_block2_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block2_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
-      "                                                                                                  \n",
-      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
+      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -798,7 +782,10 @@
       " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
+      " conv3_block2_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -808,15 +795,17 @@
       "                                                                                                  \n",
       " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block2_out (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
+      "                                                                  'conv3_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
-      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
+      " conv3_block3_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block2_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
+      " conv3_block3_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block3_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
+      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -824,7 +813,10 @@
       " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
+      " conv3_block3_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block3_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -834,15 +826,17 @@
       "                                                                                                  \n",
       " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block3_out (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
+      "                                                                  'conv3_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
-      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
+      " conv3_block4_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block3_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
+      " conv3_block4_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block4_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
+      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block4_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -850,25 +844,32 @@
       " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
+      " conv3_block4_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block4_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
       "                                                                                                  \n",
-      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
+      " conv3_block4_2_conv (Conv2D)   (None, 14, 14, 128)  147456      ['conv3_block4_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv3_block4_2_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
       "                                                                                                  \n",
-      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
+      " conv3_block4_2_relu (Activatio  (None, 14, 14, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
+      " max_pooling2d_4 (MaxPooling2D)  (None, 14, 14, 512)  0          ['conv3_block3_out[0][0]']       \n",
       "                                                                                                  \n",
-      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block4_3_conv (Conv2D)   (None, 14, 14, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
-      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
+      " conv3_block4_out (Add)         (None, 14, 14, 512)  0           ['max_pooling2d_4[0][0]',        \n",
+      "                                                                  'conv3_block4_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
+      " conv4_block1_preact_bn (BatchN  (None, 14, 14, 512)  2048       ['conv3_block4_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
+      " conv4_block1_preact_relu (Acti  (None, 14, 14, 512)  0          ['conv4_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131072      ['conv4_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -876,7 +877,10 @@
       " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
+      " conv4_block1_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -884,25 +888,23 @@
       " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv4_block1_preact_relu[0][0]'\n",
+      "                                )                                ]                                \n",
       "                                                                                                  \n",
       " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block1_out (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_conv[0][0]',    \n",
+      "                                )                                 'conv4_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block2_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block1_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
-      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
+      " conv4_block2_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block2_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
-      "                                )                                                                 \n",
-      "                                                                                                  \n",
-      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
+      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -910,7 +912,10 @@
       " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
+      " conv4_block2_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -921,16 +926,17 @@
       " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block2_out (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
+      "                                )                                 'conv4_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
-      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
+      " conv4_block3_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block2_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block3_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block3_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
+      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -938,7 +944,10 @@
       " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
+      " conv4_block3_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block3_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -949,16 +958,17 @@
       " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block3_out (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
+      "                                )                                 'conv4_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
-      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
+      " conv4_block4_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block3_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block4_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block4_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
+      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block4_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -966,7 +976,10 @@
       " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
+      " conv4_block4_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block4_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block4_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -977,16 +990,17 @@
       " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block4_out (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
+      "                                )                                 'conv4_block4_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
-      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
+      " conv4_block5_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block4_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block5_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block5_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
+      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block5_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -994,7 +1008,10 @@
       " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
+      " conv4_block5_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block5_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block5_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1005,16 +1022,17 @@
       " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block5_out (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
+      "                                )                                 'conv4_block5_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
-      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
+      " conv4_block6_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block5_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block6_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block6_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
+      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block6_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1022,7 +1040,10 @@
       " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
+      " conv4_block6_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block6_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block6_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1033,16 +1054,562 @@
       " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block6_out (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
+      "                                )                                 'conv4_block6_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
-      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
+      " conv4_block7_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block6_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
+      " conv4_block7_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block7_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block7_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block7_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
+      "                                                                                                  \n",
+      " conv4_block7_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block7_1_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block7_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block7_1_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block7_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block7_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block7_2_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block7_2_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block7_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block7_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
+      " conv4_block7_out (Add)         (None, 14, 14, 1024  0           ['conv4_block6_out[0][0]',       \n",
+      "                                )                                 'conv4_block7_3_conv[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block8_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block7_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block8_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block8_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block8_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block8_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
+      "                                                                                                  \n",
+      " conv4_block8_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block8_1_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block8_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block8_1_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block8_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block8_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block8_2_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block8_2_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block8_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block8_2_relu[0][0]']    \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block8_out (Add)         (None, 14, 14, 1024  0           ['conv4_block7_out[0][0]',       \n",
+      "                                )                                 'conv4_block8_3_conv[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block9_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block8_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block9_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block9_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block9_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block9_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
+      "                                                                                                  \n",
+      " conv4_block9_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block9_1_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block9_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block9_1_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block9_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block9_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block9_2_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block9_2_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block9_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block9_2_relu[0][0]']    \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block9_out (Add)         (None, 14, 14, 1024  0           ['conv4_block8_out[0][0]',       \n",
+      "                                )                                 'conv4_block9_3_conv[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block10_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block9_out[0][0]']       \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block10_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block10_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block10_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block10_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block10_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block10_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block10_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block10_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block10_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block10_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block10_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block10_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block10_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block10_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block10_out (Add)        (None, 14, 14, 1024  0           ['conv4_block9_out[0][0]',       \n",
+      "                                )                                 'conv4_block10_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block11_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block10_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block11_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block11_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block11_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block11_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block11_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block11_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block11_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block11_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block11_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block11_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block11_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block11_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block11_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block11_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block11_out (Add)        (None, 14, 14, 1024  0           ['conv4_block10_out[0][0]',      \n",
+      "                                )                                 'conv4_block11_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block12_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block11_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block12_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block12_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block12_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block12_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block12_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block12_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block12_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block12_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block12_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block12_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block12_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block12_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block12_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block12_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block12_out (Add)        (None, 14, 14, 1024  0           ['conv4_block11_out[0][0]',      \n",
+      "                                )                                 'conv4_block12_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block13_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block12_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block13_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block13_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block13_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block13_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block13_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block13_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block13_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block13_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block13_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block13_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block13_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block13_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block13_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block13_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block13_out (Add)        (None, 14, 14, 1024  0           ['conv4_block12_out[0][0]',      \n",
+      "                                )                                 'conv4_block13_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block14_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block13_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block14_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block14_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block14_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block14_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block14_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block14_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block14_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block14_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block14_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block14_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block14_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block14_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block14_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block14_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block14_out (Add)        (None, 14, 14, 1024  0           ['conv4_block13_out[0][0]',      \n",
+      "                                )                                 'conv4_block14_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block15_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block14_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block15_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block15_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block15_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block15_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block15_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block15_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block15_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block15_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block15_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block15_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block15_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block15_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block15_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block15_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block15_out (Add)        (None, 14, 14, 1024  0           ['conv4_block14_out[0][0]',      \n",
+      "                                )                                 'conv4_block15_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block16_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block15_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block16_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block16_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block16_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block16_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block16_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block16_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block16_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block16_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block16_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block16_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block16_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block16_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block16_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block16_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block16_out (Add)        (None, 14, 14, 1024  0           ['conv4_block15_out[0][0]',      \n",
+      "                                )                                 'conv4_block16_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block17_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block16_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block17_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block17_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block17_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block17_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block17_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block17_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block17_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block17_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block17_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block17_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block17_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block17_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block17_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block17_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block17_out (Add)        (None, 14, 14, 1024  0           ['conv4_block16_out[0][0]',      \n",
+      "                                )                                 'conv4_block17_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block18_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block17_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block18_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block18_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block18_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block18_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block18_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block18_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block18_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block18_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block18_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block18_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block18_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block18_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block18_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block18_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block18_out (Add)        (None, 14, 14, 1024  0           ['conv4_block17_out[0][0]',      \n",
+      "                                )                                 'conv4_block18_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block19_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block18_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block19_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block19_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block19_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block19_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block19_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block19_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block19_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block19_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block19_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block19_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block19_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block19_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block19_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block19_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block19_out (Add)        (None, 14, 14, 1024  0           ['conv4_block18_out[0][0]',      \n",
+      "                                )                                 'conv4_block19_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block20_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block19_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block20_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block20_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block20_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block20_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block20_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block20_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block20_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block20_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block20_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block20_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block20_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block20_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block20_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block20_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block20_out (Add)        (None, 14, 14, 1024  0           ['conv4_block19_out[0][0]',      \n",
+      "                                )                                 'conv4_block20_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block21_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block20_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block21_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block21_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block21_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block21_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block21_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block21_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block21_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block21_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block21_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block21_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block21_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block21_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block21_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block21_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block21_out (Add)        (None, 14, 14, 1024  0           ['conv4_block20_out[0][0]',      \n",
+      "                                )                                 'conv4_block21_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block22_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block21_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block22_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block22_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block22_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block22_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block22_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block22_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block22_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block22_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block22_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block22_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block22_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block22_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block22_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block22_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block22_out (Add)        (None, 14, 14, 1024  0           ['conv4_block21_out[0][0]',      \n",
+      "                                )                                 'conv4_block22_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block23_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block22_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block23_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block23_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block23_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block23_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block23_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block23_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block23_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block23_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block23_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_conv (Conv2D)  (None, 7, 7, 256)    589824      ['conv4_block23_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_bn (BatchNorma  (None, 7, 7, 256)   1024        ['conv4_block23_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_relu (Activati  (None, 7, 7, 256)   0           ['conv4_block23_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " max_pooling2d_5 (MaxPooling2D)  (None, 7, 7, 1024)  0           ['conv4_block22_out[0][0]']      \n",
+      "                                                                                                  \n",
+      " conv4_block23_3_conv (Conv2D)  (None, 7, 7, 1024)   263168      ['conv4_block23_2_relu[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block23_out (Add)        (None, 7, 7, 1024)   0           ['max_pooling2d_5[0][0]',        \n",
+      "                                                                  'conv4_block23_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv5_block1_preact_bn (BatchN  (None, 7, 7, 1024)  4096        ['conv4_block23_out[0][0]']      \n",
+      " ormalization)                                                                                    \n",
+      "                                                                                                  \n",
+      " conv5_block1_preact_relu (Acti  (None, 7, 7, 1024)  0           ['conv5_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524288      ['conv5_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1050,7 +1617,10 @@
       " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
+      " conv5_block1_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1058,22 +1628,22 @@
       " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
+      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv5_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block1_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_conv[0][0]',    \n",
+      "                                                                  'conv5_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block2_preact_bn (BatchN  (None, 7, 7, 2048)  8192        ['conv5_block1_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
-      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
+      " conv5_block2_preact_relu (Acti  (None, 7, 7, 2048)  0           ['conv5_block2_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
-      "                                                                                                  \n",
-      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
+      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1048576     ['conv5_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1081,7 +1651,10 @@
       " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
+      " conv5_block2_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1091,15 +1664,17 @@
       "                                                                                                  \n",
       " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block2_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
+      "                                                                  'conv5_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
-      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
+      " conv5_block3_preact_bn (BatchN  (None, 7, 7, 2048)  8192        ['conv5_block2_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
+      " conv5_block3_preact_relu (Acti  (None, 7, 7, 2048)  0           ['conv5_block3_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
+      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1048576     ['conv5_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1107,7 +1682,10 @@
       " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
+      " conv5_block3_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block3_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1117,22 +1695,21 @@
       "                                                                                                  \n",
       " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block3_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
+      "                                                                  'conv5_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
-      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
+      " post_bn (BatchNormalization)   (None, 7, 7, 2048)   8192        ['conv5_block3_out[0][0]']       \n",
       "                                                                                                  \n",
-      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
+      " post_relu (Activation)         (None, 7, 7, 2048)   0           ['post_bn[0][0]']                \n",
       "                                                                                                  \n",
-      " flatten_3 (Flatten)            (None, 100352)       0           ['conv5_block3_out[0][0]']       \n",
+      " flatten_4 (Flatten)            (None, 100352)       0           ['post_relu[0][0]']              \n",
       "                                                                                                  \n",
-      " dense_7 (Dense)                (None, 5)            501765      ['flatten_3[0][0]']              \n",
+      " dense_4 (Dense)                (None, 5)            501765      ['flatten_4[0][0]']              \n",
       "                                                                                                  \n",
       "==================================================================================================\n",
-      "Total params: 24,089,477\n",
+      "Total params: 43,128,325\n",
       "Trainable params: 501,765\n",
-      "Non-trainable params: 23,587,712\n",
+      "Non-trainable params: 42,626,560\n",
       "__________________________________________________________________________________________________\n"
      ]
     }
@@ -1147,7 +1724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1161,7 +1738,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_2029/3602206220.py:10: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+      "/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_11345/3602206220.py:10: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
       "  r = model.fit_generator(\n"
      ]
     },
@@ -1169,55 +1746,55 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25/25 [==============================] - 38s 1s/step - loss: 6.4809 - accuracy: 0.1950 - val_loss: 2.9878 - val_accuracy: 0.2344\n",
+      "25/25 [==============================] - 59s 2s/step - loss: 3.6952 - accuracy: 0.7675 - val_loss: 1.0397 - val_accuracy: 0.9427\n",
       "Epoch 2/25\n",
-      "25/25 [==============================] - 35s 1s/step - loss: 2.2159 - accuracy: 0.2338 - val_loss: 2.3206 - val_accuracy: 0.2396\n",
+      "25/25 [==============================] - 55s 2s/step - loss: 0.2606 - accuracy: 0.9688 - val_loss: 0.6033 - val_accuracy: 0.9479\n",
       "Epoch 3/25\n",
-      "25/25 [==============================] - 35s 1s/step - loss: 1.9435 - accuracy: 0.2237 - val_loss: 1.8788 - val_accuracy: 0.2292\n",
+      "25/25 [==============================] - 55s 2s/step - loss: 0.0624 - accuracy: 0.9887 - val_loss: 0.7021 - val_accuracy: 0.9323\n",
       "Epoch 4/25\n",
-      "25/25 [==============================] - 35s 1s/step - loss: 2.1113 - accuracy: 0.2350 - val_loss: 1.5820 - val_accuracy: 0.2604\n",
+      "25/25 [==============================] - 55s 2s/step - loss: 0.0150 - accuracy: 0.9987 - val_loss: 0.4405 - val_accuracy: 0.9688\n",
       "Epoch 5/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.7911 - accuracy: 0.2975 - val_loss: 1.6257 - val_accuracy: 0.3229\n",
+      "25/25 [==============================] - 56s 2s/step - loss: 0.0123 - accuracy: 0.9975 - val_loss: 0.3344 - val_accuracy: 0.9740\n",
       "Epoch 6/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.8471 - accuracy: 0.2975 - val_loss: 1.6844 - val_accuracy: 0.3542\n",
+      "25/25 [==============================] - 56s 2s/step - loss: 1.9117e-07 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9844\n",
       "Epoch 7/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.7567 - accuracy: 0.3075 - val_loss: 1.4758 - val_accuracy: 0.3281\n",
+      "25/25 [==============================] - 56s 2s/step - loss: 4.4405e-08 - accuracy: 1.0000 - val_loss: 0.2787 - val_accuracy: 0.9844\n",
       "Epoch 8/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.6541 - accuracy: 0.3550 - val_loss: 1.6412 - val_accuracy: 0.2708\n",
+      "25/25 [==============================] - 56s 2s/step - loss: 3.5911e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 9/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.4498 - accuracy: 0.3762 - val_loss: 1.3539 - val_accuracy: 0.3958\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 2.7716e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 10/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.5093 - accuracy: 0.3525 - val_loss: 1.4342 - val_accuracy: 0.3385\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 2.2948e-08 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9896\n",
       "Epoch 11/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.4125 - accuracy: 0.4062 - val_loss: 1.6245 - val_accuracy: 0.3438\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 2.0563e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 12/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.5308 - accuracy: 0.3650 - val_loss: 1.6150 - val_accuracy: 0.2292\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 1.7583e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 13/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.4149 - accuracy: 0.4263 - val_loss: 1.5404 - val_accuracy: 0.3906\n",
+      "25/25 [==============================] - 60s 2s/step - loss: 1.5646e-08 - accuracy: 1.0000 - val_loss: 0.2775 - val_accuracy: 0.9844\n",
       "Epoch 14/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.4894 - accuracy: 0.3925 - val_loss: 1.8275 - val_accuracy: 0.2292\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 1.4305e-08 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9896\n",
       "Epoch 15/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.5978 - accuracy: 0.3775 - val_loss: 1.3376 - val_accuracy: 0.4375\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 1.3560e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 16/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.4227 - accuracy: 0.4175 - val_loss: 1.5674 - val_accuracy: 0.3958\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 1.1921e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 17/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.4758 - accuracy: 0.3837 - val_loss: 1.5279 - val_accuracy: 0.3698\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 1.1176e-08 - accuracy: 1.0000 - val_loss: 0.1318 - val_accuracy: 0.9896\n",
       "Epoch 18/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.6931 - accuracy: 0.4137 - val_loss: 1.8716 - val_accuracy: 0.2865\n",
+      "25/25 [==============================] - 59s 2s/step - loss: 1.0431e-08 - accuracy: 1.0000 - val_loss: 0.2776 - val_accuracy: 0.9844\n",
       "Epoch 19/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.3378 - accuracy: 0.4500 - val_loss: 1.4395 - val_accuracy: 0.4271\n",
+      "25/25 [==============================] - 58s 2s/step - loss: 9.8347e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 20/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.3173 - accuracy: 0.4825 - val_loss: 1.2535 - val_accuracy: 0.4583\n",
+      "25/25 [==============================] - 58s 2s/step - loss: 9.2387e-09 - accuracy: 1.0000 - val_loss: 0.2775 - val_accuracy: 0.9844\n",
       "Epoch 21/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.2702 - accuracy: 0.4900 - val_loss: 1.4282 - val_accuracy: 0.4896\n",
+      "25/25 [==============================] - 60s 2s/step - loss: 8.7917e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 22/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.2848 - accuracy: 0.4600 - val_loss: 1.3511 - val_accuracy: 0.4115\n",
+      "25/25 [==============================] - 58s 2s/step - loss: 8.3446e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 23/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.4002 - accuracy: 0.4487 - val_loss: 1.5821 - val_accuracy: 0.3281\n",
+      "25/25 [==============================] - 57s 2s/step - loss: 5.5134e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 24/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.2507 - accuracy: 0.4800 - val_loss: 1.2901 - val_accuracy: 0.4635\n",
+      "25/25 [==============================] - 56s 2s/step - loss: 7.5996e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n",
       "Epoch 25/25\n",
-      "25/25 [==============================] - 36s 1s/step - loss: 1.3626 - accuracy: 0.4462 - val_loss: 1.5347 - val_accuracy: 0.3906\n"
+      "25/25 [==============================] - 57s 2s/step - loss: 7.3016e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9844\n"
      ]
     }
    ],
@@ -1242,12 +1819,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1273,23 +1850,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8/8 [==============================] - 9s 1s/step - loss: 1.4028 - accuracy: 0.4141\n"
+      "8/8 [==============================] - 15s 2s/step - loss: 0.7370 - accuracy: 0.9414\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[1.4027552604675293, 0.4140625]"
+       "[0.7369823455810547, 0.94140625]"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/sw_lab9-10_2.ipynb b/sw_lab9-10_2.ipynb
index b01a2e7..bbe0f8f 100644
--- a/sw_lab9-10_2.ipynb
+++ b/sw_lab9-10_2.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,9 +265,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training: 7430\n",
-      "Test: 2323\n",
-      "Validation: 1858\n"
+      "Training: 2990\n",
+      "Test: 935\n",
+      "Validation: 748\n"
      ]
     }
    ],
@@ -292,7 +292,7 @@
      "output_type": "stream",
      "text": [
       "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
-      "/var/folders/_h/ljwht4gd7lb99rm1hm78h7_00000gn/T/ipykernel_23432/2397086753.py:6: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+      "/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_14470/2397086753.py:6: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
       "  alex = model.fit_generator(\n"
      ]
     },
@@ -307,112 +307,112 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-01-06 20:01:38.622228: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-01-09 18:33:27.636772: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "232/232 [==============================] - ETA: 0s - loss: 2.1314 - accuracy: 0.2501\n",
-      "Epoch 1: val_accuracy improved from -inf to 0.44235, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 223s 956ms/step - loss: 2.1314 - accuracy: 0.2501 - val_loss: 1.6157 - val_accuracy: 0.4423\n",
+      "93/93 [==============================] - ETA: 0s - loss: 1.5758 - accuracy: 0.3474\n",
+      "Epoch 1: val_accuracy improved from -inf to 0.38179, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 95s 1s/step - loss: 1.5758 - accuracy: 0.3474 - val_loss: 1.4164 - val_accuracy: 0.3818\n",
       "Epoch 2/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 1.3779 - accuracy: 0.5031\n",
-      "Epoch 2: val_accuracy improved from 0.44235 to 0.60614, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 264s 1s/step - loss: 1.3779 - accuracy: 0.5031 - val_loss: 1.1473 - val_accuracy: 0.6061\n",
+      "93/93 [==============================] - ETA: 0s - loss: 1.4061 - accuracy: 0.3609\n",
+      "Epoch 2: val_accuracy did not improve from 0.38179\n",
+      "93/93 [==============================] - 100s 1s/step - loss: 1.4061 - accuracy: 0.3609 - val_loss: 1.4139 - val_accuracy: 0.3098\n",
       "Epoch 3/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 1.0262 - accuracy: 0.6358\n",
-      "Epoch 3: val_accuracy improved from 0.60614 to 0.67726, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 266s 1s/step - loss: 1.0262 - accuracy: 0.6358 - val_loss: 0.9024 - val_accuracy: 0.6773\n",
+      "93/93 [==============================] - ETA: 0s - loss: 1.3158 - accuracy: 0.3999\n",
+      "Epoch 3: val_accuracy improved from 0.38179 to 0.38995, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 102s 1s/step - loss: 1.3158 - accuracy: 0.3999 - val_loss: 1.2847 - val_accuracy: 0.3899\n",
       "Epoch 4/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.7844 - accuracy: 0.7259\n",
-      "Epoch 4: val_accuracy improved from 0.67726 to 0.72252, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 267s 1s/step - loss: 0.7844 - accuracy: 0.7259 - val_loss: 0.7740 - val_accuracy: 0.7225\n",
+      "93/93 [==============================] - ETA: 0s - loss: 1.2229 - accuracy: 0.4792\n",
+      "Epoch 4: val_accuracy improved from 0.38995 to 0.57201, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 102s 1s/step - loss: 1.2229 - accuracy: 0.4792 - val_loss: 1.1064 - val_accuracy: 0.5720\n",
       "Epoch 5/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.5837 - accuracy: 0.7967\n",
-      "Epoch 5: val_accuracy improved from 0.72252 to 0.79472, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 269s 1s/step - loss: 0.5837 - accuracy: 0.7967 - val_loss: 0.5986 - val_accuracy: 0.7947\n",
+      "93/93 [==============================] - ETA: 0s - loss: 1.0983 - accuracy: 0.5625\n",
+      "Epoch 5: val_accuracy improved from 0.57201 to 0.64946, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 104s 1s/step - loss: 1.0983 - accuracy: 0.5625 - val_loss: 0.9796 - val_accuracy: 0.6495\n",
       "Epoch 6/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.4601 - accuracy: 0.8393\n",
-      "Epoch 6: val_accuracy did not improve from 0.79472\n",
-      "232/232 [==============================] - 273s 1s/step - loss: 0.4601 - accuracy: 0.8393 - val_loss: 0.6495 - val_accuracy: 0.7769\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.9776 - accuracy: 0.6253\n",
+      "Epoch 6: val_accuracy did not improve from 0.64946\n",
+      "93/93 [==============================] - 105s 1s/step - loss: 0.9776 - accuracy: 0.6253 - val_loss: 1.1308 - val_accuracy: 0.5476\n",
       "Epoch 7/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.3825 - accuracy: 0.8679\n",
-      "Epoch 7: val_accuracy improved from 0.79472 to 0.85938, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 274s 1s/step - loss: 0.3825 - accuracy: 0.8679 - val_loss: 0.4127 - val_accuracy: 0.8594\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.8467 - accuracy: 0.6969\n",
+      "Epoch 7: val_accuracy improved from 0.64946 to 0.67663, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 105s 1s/step - loss: 0.8467 - accuracy: 0.6969 - val_loss: 0.9045 - val_accuracy: 0.6766\n",
       "Epoch 8/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.2899 - accuracy: 0.8978\n",
-      "Epoch 8: val_accuracy did not improve from 0.85938\n",
-      "232/232 [==============================] - 273s 1s/step - loss: 0.2899 - accuracy: 0.8978 - val_loss: 0.4238 - val_accuracy: 0.8540\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.7437 - accuracy: 0.7312\n",
+      "Epoch 8: val_accuracy improved from 0.67663 to 0.77853, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 105s 1s/step - loss: 0.7437 - accuracy: 0.7312 - val_loss: 0.5997 - val_accuracy: 0.7785\n",
       "Epoch 9/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.2615 - accuracy: 0.9133\n",
-      "Epoch 9: val_accuracy improved from 0.85938 to 0.87338, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 270s 1s/step - loss: 0.2615 - accuracy: 0.9133 - val_loss: 0.3714 - val_accuracy: 0.8734\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.6769 - accuracy: 0.7638\n",
+      "Epoch 9: val_accuracy improved from 0.77853 to 0.80978, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 105s 1s/step - loss: 0.6769 - accuracy: 0.7638 - val_loss: 0.5234 - val_accuracy: 0.8098\n",
       "Epoch 10/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.2115 - accuracy: 0.9247\n",
-      "Epoch 10: val_accuracy improved from 0.87338 to 0.87500, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 269s 1s/step - loss: 0.2115 - accuracy: 0.9247 - val_loss: 0.3794 - val_accuracy: 0.8750\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.5742 - accuracy: 0.7950\n",
+      "Epoch 10: val_accuracy did not improve from 0.80978\n",
+      "93/93 [==============================] - 106s 1s/step - loss: 0.5742 - accuracy: 0.7950 - val_loss: 1.3374 - val_accuracy: 0.5068\n",
       "Epoch 11/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.1971 - accuracy: 0.9349\n",
-      "Epoch 11: val_accuracy did not improve from 0.87500\n",
-      "232/232 [==============================] - 270s 1s/step - loss: 0.1971 - accuracy: 0.9349 - val_loss: 0.4570 - val_accuracy: 0.8567\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.5694 - accuracy: 0.8041\n",
+      "Epoch 11: val_accuracy improved from 0.80978 to 0.84375, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 107s 1s/step - loss: 0.5694 - accuracy: 0.8041 - val_loss: 0.5118 - val_accuracy: 0.8438\n",
       "Epoch 12/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9500\n",
-      "Epoch 12: val_accuracy improved from 0.87500 to 0.87662, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 270s 1s/step - loss: 0.1495 - accuracy: 0.9500 - val_loss: 0.4067 - val_accuracy: 0.8766\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.4730 - accuracy: 0.8347\n",
+      "Epoch 12: val_accuracy did not improve from 0.84375\n",
+      "93/93 [==============================] - 106s 1s/step - loss: 0.4730 - accuracy: 0.8347 - val_loss: 0.6001 - val_accuracy: 0.7826\n",
       "Epoch 13/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.1206 - accuracy: 0.9634\n",
-      "Epoch 13: val_accuracy improved from 0.87662 to 0.88147, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 269s 1s/step - loss: 0.1206 - accuracy: 0.9634 - val_loss: 0.4036 - val_accuracy: 0.8815\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.4713 - accuracy: 0.8364\n",
+      "Epoch 13: val_accuracy did not improve from 0.84375\n",
+      "93/93 [==============================] - 106s 1s/step - loss: 0.4713 - accuracy: 0.8364 - val_loss: 0.5150 - val_accuracy: 0.8125\n",
       "Epoch 14/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.1667 - accuracy: 0.9593\n",
-      "Epoch 14: val_accuracy did not improve from 0.88147\n",
-      "232/232 [==============================] - 272s 1s/step - loss: 0.1667 - accuracy: 0.9593 - val_loss: 0.5347 - val_accuracy: 0.8292\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.3892 - accuracy: 0.8646\n",
+      "Epoch 14: val_accuracy improved from 0.84375 to 0.86821, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 110s 1s/step - loss: 0.3892 - accuracy: 0.8646 - val_loss: 0.3537 - val_accuracy: 0.8682\n",
       "Epoch 15/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.1315 - accuracy: 0.9588\n",
-      "Epoch 15: val_accuracy did not improve from 0.88147\n",
-      "232/232 [==============================] - 277s 1s/step - loss: 0.1315 - accuracy: 0.9588 - val_loss: 0.7335 - val_accuracy: 0.8163\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.3787 - accuracy: 0.8632\n",
+      "Epoch 15: val_accuracy did not improve from 0.86821\n",
+      "93/93 [==============================] - 109s 1s/step - loss: 0.3787 - accuracy: 0.8632 - val_loss: 0.5223 - val_accuracy: 0.7880\n",
       "Epoch 16/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.0950 - accuracy: 0.9731\n",
-      "Epoch 16: val_accuracy improved from 0.88147 to 0.88308, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 272s 1s/step - loss: 0.0950 - accuracy: 0.9731 - val_loss: 0.4444 - val_accuracy: 0.8831\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.3409 - accuracy: 0.8770\n",
+      "Epoch 16: val_accuracy did not improve from 0.86821\n",
+      "93/93 [==============================] - 110s 1s/step - loss: 0.3409 - accuracy: 0.8770 - val_loss: 0.3797 - val_accuracy: 0.8451\n",
       "Epoch 17/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.0566 - accuracy: 0.9846\n",
-      "Epoch 17: val_accuracy did not improve from 0.88308\n",
-      "232/232 [==============================] - 273s 1s/step - loss: 0.0566 - accuracy: 0.9846 - val_loss: 0.6635 - val_accuracy: 0.8287\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.4428 - accuracy: 0.8508\n",
+      "Epoch 17: val_accuracy did not improve from 0.86821\n",
+      "93/93 [==============================] - 108s 1s/step - loss: 0.4428 - accuracy: 0.8508 - val_loss: 0.9765 - val_accuracy: 0.6304\n",
       "Epoch 18/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.0443 - accuracy: 0.9880\n",
-      "Epoch 18: val_accuracy improved from 0.88308 to 0.88631, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 273s 1s/step - loss: 0.0443 - accuracy: 0.9880 - val_loss: 0.4852 - val_accuracy: 0.8863\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.3638 - accuracy: 0.8740\n",
+      "Epoch 18: val_accuracy improved from 0.86821 to 0.88451, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 108s 1s/step - loss: 0.3638 - accuracy: 0.8740 - val_loss: 0.2889 - val_accuracy: 0.8845\n",
       "Epoch 19/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.0101 - accuracy: 0.9981\n",
-      "Epoch 19: val_accuracy improved from 0.88631 to 0.90248, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 274s 1s/step - loss: 0.0101 - accuracy: 0.9981 - val_loss: 0.4459 - val_accuracy: 0.9025\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.2869 - accuracy: 0.8942\n",
+      "Epoch 19: val_accuracy improved from 0.88451 to 0.89674, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 109s 1s/step - loss: 0.2869 - accuracy: 0.8942 - val_loss: 0.2879 - val_accuracy: 0.8967\n",
       "Epoch 20/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.0031 - accuracy: 0.9995\n",
-      "Epoch 20: val_accuracy improved from 0.90248 to 0.90787, saving model to alex_2.h5\n",
-      "232/232 [==============================] - 274s 1s/step - loss: 0.0031 - accuracy: 0.9995 - val_loss: 0.4574 - val_accuracy: 0.9079\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.2724 - accuracy: 0.9015\n",
+      "Epoch 20: val_accuracy improved from 0.89674 to 0.91168, saving model to alex_2.h5\n",
+      "93/93 [==============================] - 108s 1s/step - loss: 0.2724 - accuracy: 0.9015 - val_loss: 0.2781 - val_accuracy: 0.9117\n",
       "Epoch 21/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 0.0010 - accuracy: 1.0000\n",
-      "Epoch 21: val_accuracy did not improve from 0.90787\n",
-      "232/232 [==============================] - 278s 1s/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.4781 - val_accuracy: 0.9073\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.5926 - accuracy: 0.8021\n",
+      "Epoch 21: val_accuracy did not improve from 0.91168\n",
+      "93/93 [==============================] - 107s 1s/step - loss: 0.5926 - accuracy: 0.8021 - val_loss: 0.3587 - val_accuracy: 0.8709\n",
       "Epoch 22/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 7.0759e-04 - accuracy: 1.0000\n",
-      "Epoch 22: val_accuracy did not improve from 0.90787\n",
-      "232/232 [==============================] - 270s 1s/step - loss: 7.0759e-04 - accuracy: 1.0000 - val_loss: 0.4991 - val_accuracy: 0.9062\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.2875 - accuracy: 0.8978\n",
+      "Epoch 22: val_accuracy did not improve from 0.91168\n",
+      "93/93 [==============================] - 108s 1s/step - loss: 0.2875 - accuracy: 0.8978 - val_loss: 0.2895 - val_accuracy: 0.9035\n",
       "Epoch 23/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 5.5237e-04 - accuracy: 1.0000\n",
-      "Epoch 23: val_accuracy did not improve from 0.90787\n",
-      "232/232 [==============================] - 270s 1s/step - loss: 5.5237e-04 - accuracy: 1.0000 - val_loss: 0.5114 - val_accuracy: 0.9073\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.2233 - accuracy: 0.9267\n",
+      "Epoch 23: val_accuracy did not improve from 0.91168\n",
+      "93/93 [==============================] - 108s 1s/step - loss: 0.2233 - accuracy: 0.9267 - val_loss: 0.3617 - val_accuracy: 0.8723\n",
       "Epoch 24/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 4.5192e-04 - accuracy: 1.0000\n",
-      "Epoch 24: val_accuracy did not improve from 0.90787\n",
-      "232/232 [==============================] - 268s 1s/step - loss: 4.5192e-04 - accuracy: 1.0000 - val_loss: 0.5210 - val_accuracy: 0.9052\n",
+      "93/93 [==============================] - ETA: 0s - loss: 0.2837 - accuracy: 0.9005\n",
+      "Epoch 24: val_accuracy did not improve from 0.91168\n",
+      "93/93 [==============================] - 107s 1s/step - loss: 0.2837 - accuracy: 0.9005 - val_loss: 0.3122 - val_accuracy: 0.8981\n",
       "Epoch 25/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 3.7889e-04 - accuracy: 1.0000\n",
-      "Epoch 25: val_accuracy did not improve from 0.90787\n",
-      "232/232 [==============================] - 268s 1s/step - loss: 3.7889e-04 - accuracy: 1.0000 - val_loss: 0.5333 - val_accuracy: 0.9057\n"
+      "93/93 [==============================] - ETA: 0s - loss: 0.2049 - accuracy: 0.9368\n",
+      "Epoch 25: val_accuracy did not improve from 0.91168\n",
+      "93/93 [==============================] - 109s 1s/step - loss: 0.2049 - accuracy: 0.9368 - val_loss: 0.3776 - val_accuracy: 0.8750\n"
      ]
     }
    ],
@@ -438,7 +438,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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C8MRZtGgRL730Ert37+bWrVsWi0M0bSy7LJrwHD16lEaNGtGoUSMApkyZQqNGjXj33XcBCA8PNyc/APb29mzdupW7d+/StGlTnnvuOQICApg/f75F4s/uZNRJnvrtKV7b9RoZxgxLhyMIgvDESExMZPXq1fzvf/+jV69eLF26NMfrf//9N82aNcPa2hp3d3f69etnfi0tLY233noLX19frKysqF69OosWLQJg6dKlODs757jW+vXrcxS+zpo1i4YNG/LTTz9RpUoVrK2tAaXNSdu2bXF2dsbNzY3evXtz9erVHNcKDQ1l6NChuLq6YmdnR9OmTTl06BDXr19HpVJx9OjRHMd/+eWXVKpUSfSGe0JZtIanY8eOyLJ839fv/UcBULt2bbZu3VqMURVObdfa2GhsiEiKYMv1LfSqKoZtBUEoPrIsk5JR9Et388NGqy7Qapo1a9ZQu3ZtatWqxfDhw5k8eTJTp05FkiQ2bNhAv379mDZtGsuWLSM9PZ2NGzeazw0MDOTAgQPMnz+fBg0aEBwcTHR0dIHivXLlCr///jt//PEHarUaUBrfTpkyhfr165OYmMi7775Lv379CAoKQqVSkZiYSIcOHShfvjx//fUXXl5eHD9+HKPRSOXKlenatStLliyhadOm5vssWbKEUaNGiaaQT6gSVbT8JLNSWzGs9jAWBi3k57M/07NKT7F8VBCEYpOSYaDOu5stcu9z73XDVpf/Hx+LFi1i+PDhAHTv3p24uDh27dpFx44d+fDDD3n22WdztA9p0KABoNRorlmzhq1bt9K1a1cAqlatWuB409PTWbZsGR4eHubnBgwYkOOYxYsX4+Hhwblz56hXrx4rVqwgKiqKI0eOmBvdVq9e3Xz8uHHjeOGFF/j888+xsrLi+PHjnD59mj///LPA8QmPh0hDi9CQWkOwVltzPvY8RyKOWDocQRAEi7t48SKHDx9m6NChAGg0GoYMGWKelgoKCqJLly55nhsUFIRaraZDhw6PFEOlSpVyJDsAly9fZujQoVStWhVHR0dz135TGUVQUBCNGjUyJzv36tu3L2q1mnXr1gHKjESnTp1E9/8nmBjhKULO1s70qd6H1RdXs/TsUpp7N7d0SIIglFI2WjXn3utmsXvn16JFi9Dr9fj4+Jifk2UZKysrFi5c+MAFJw9bjKJSqXKVRWRk5K6htLOzy/VcQEAAlSpV4scff8THxwej0Ui9evXMRc0Pu7dOpyMwMJAlS5bQv39/VqxYwVdfffXAcwTLEiM8RSywTiASEnvC9nD17tWHnyAIglAIkiRhq9NY5JHf6Xq9Xs+yZcuYN2+eeUVuUFAQJ0+exMfHh5UrV1K/fn22b9+e5/n+/v4YjUZ27dqV5+seHh4kJCSQlJRkfs606vdBYmJiuHjxItOnT6dLly74+flx586dHMfUr1+foKAgYmNj73udcePGsW3bNr755hv0en2+N8EWLEMkPEWsomNFOlfsDMCyc8ssHI0gCILl/PPPP9y5c4exY8dSr169HI8BAwawaNEiZs6cycqVK5k5cybnz5/n9OnTfPzxx4CyOfTIkSMZM2YM69evJzg4mJ07d7JmzRoAWrRoga2tLe+88w5Xr15lxYoVeS52uZeLiwtubm788MMPXLlyhf/++48pU6bkOGbo0KF4eXnRt29f9u3bx7Vr1/j99985cOCA+Rg/Pz9atmzJW2+9xdChQ5+I9ijC/YmEpxiMqjsKgL+v/k10SsFWEwiCIJQWixYtomvXrjm645sMGDCAo0eP4urqytq1a/nrr79o2LAhnTt35vDhw+bjvv32WwYOHMiLL75I7dq1GT9+vHlEx9XVleXLl7Nx40b8/f1ZuXIls2bNemhcKpWKVatWcezYMerVq8err77Kp59+muMYnU7Hli1bKFeuHD179sTf35+PPvrIvMrLZOzYsaSnpzNmzJhCfIeEx0mSH7QuvBSKj4/HycmJuLg4HB0di+0+wzcO52TUScb7j+flxi8X230EQSgbUlNTCQ4OztFLRrC8999/n7Vr13LqlGg6WxQe9Pf8UX9+ixGeYjKy7kgA1lxaQ3JGsoWjEQRBEIpSYmIiZ86cYeHChbz00kuWDkfIB5HwFJPOvp2pYF+BuLQ4/rwq+jIIgiCUJpMmTaJJkyZ07NhRTGeVECLhKSZqlZrAuso+YMvOLsNgtExHVEEQBKHoLV26lLS0NFavXp2rrkd4MomEpxj1qdYHR50joYmh7Li5w9LhCIIgCEKZJRKeYmSrtWVIrSEALD271LLBCIIgCEIZJhKeYjbMbxhalZaTUScJuh1k6XAEQRAEoUwSCU8xc7dxp3fV3gD8fPZnC0cjCIIgCGWTSHgeg8A6SvHy9pDthMSHWDgaQRAEQSh7RMLzGFR3qU678u2Qkfnl3C+WDkcQBEEQyhyR8DwmpkaE66+s527qXcsGIwiCUIJ07NiRyZMnm7+uXLkyX3755QPPkSSJ9evXP/K9i+o6guWJhOcxae7VHD9XP1INqay+uNrS4QiCIBS7gIAAunfvnudre/bsQZKkQm3JcOTIESZMmPCo4eUwa9YsGjZsmOv58PBwevToUaT3up+UlBRcXV1xd3cnLS3tsdyzLBEJz2MiSZJ5lGflhZWkGcRfZkEQSrexY8eydetWQkNDc722ZMkSmjZtSv369Qt8XQ8PD2xtbYsixIfy8vLCysrqsdzr999/p27dutSuXdvio0qyLKPX6y0aQ1ETCc9j9HTlp/G09SQmNYYN1zZYOhxBEIRi1bt3bzw8PFi6dGmO5xMTE1m7di1jx44lJiaGoUOHUr58eWxtbc27nj/IvVNaly9fpn379lhbW1OnTh22bt2a65y33nqLmjVrYmtrS9WqVZkxYwYZGRmA0jV59uzZnDx5EkmSkCTJHPO9U1qnT5+mc+fO2NjY4ObmxoQJE0hMTDS/PmrUKPr27ctnn32Gt7c3bm5uTJw40XyvB1m0aBHDhw9n+PDhLFq0KNfrZ8+epXfv3jg6OuLg4EC7du24evWq+fXFixdTt25drKys8Pb2ZtKkSQBcv34dSZIICgoyH3v37l0kSWLnzp0A7Ny5E0mS2LRpE02aNMHKyoq9e/dy9epV+vTpg6enJ/b29jRr1oxt27bliCstLY233noLX19frKysqF69OosWLUKWZapXr85nn32W4/igoCAkSeLKlSsP/Z4UJZHwPEZalZYRdUYAyhJ1o2y0cESCIJRYsgzpSZZ5yHK+QtRoNAQGBrJ06VLkbOesXbsWg8HA0KFDSU1NpUmTJmzYsIEzZ84wYcIERowYweHDh/N1D6PRSP/+/dHpdBw6dIjvvvuOt956K9dxDg4OLF26lHPnzvHVV1/x448/8sUXXwAwZMgQXnvtNerWrUt4eDjh4eEMGTIk1zWSkpLo1q0bLi4uHDlyhLVr17Jt2zZzYmGyY8cOrl69yo4dO/j5559ZunRprqTvXlevXuXAgQMMHjyYwYMHs2fPHm7cuGF+PSwsjPbt22NlZcV///3HsWPHGDNmjHkU5ttvv2XixIlMmDCB06dP89dff1G9evV8fQ+ze/vtt/noo484f/489evXJzExkZ49e7J9+3ZOnDhB9+7dCQgIICQka8VxYGAgK1euZP78+Zw/f57vv/8ee3t7JElizJgxLFmyJMc9lixZQvv27QsV36PQPNa7CfSv0Z9vT37Ltbhr7A3bS/sK7S0dkiAIJVFGMszxscy937kFOrt8HTpmzBg+/fRTdu3aRceOHQHlB96AAQNwcnLCycmJ119/3Xz8Sy+9xObNm1mzZg3Nmzd/6PW3bdvGhQsX2Lx5Mz4+yvdjzpw5uepupk+fbv68cuXKvP7666xatYo333wTGxsb7O3t0Wg0eHl53fdeK1asIDU1lWXLlmFnp7z/hQsXEhAQwMcff4ynpycALi4uLFy4ELVaTe3atenVqxfbt29n/Pjx97324sWL6dGjBy4uLgB069aNJUuWMGvWLAC+/vprnJycWLVqFVqtFoCaNWuaz//ggw947bXXeOWVV8zPNWvW7KHfv3u99957PPXUU+avXV1dadCggfnr999/n3Xr1vHXX38xadIkLl26xJo1a9i6dStdu3YFoGrVqubjR40axbvvvsvhw4dp3rw5GRkZrFixIteoz+MgRngeMwedAwNrDAREI0JBEEq/2rVr07p1axYvXgzAlStX2LNnD2PHjgXAYDDw/vvv4+/vj6urK/b29mzevDnHCMKDnD9/Hl9fX3OyA9CqVatcx61evZo2bdrg5eWFvb0906dPz/c9st+rQYMG5mQHoE2bNhiNRi5evGh+rm7dujk2FPX29ub27dv3va7BYODnn39m+PDh5ueGDx/O0qVLMRqVmYCgoCDatWtnTnayu337Nrdu3aJLly4Fej95adq0aY6vExMTef311/Hz88PZ2Rl7e3vOnz9v/t4FBQWhVqvp0KFDntfz8fGhV69e5j//v//+m7S0NAYNGvTIsRaUGOGxgOF1hvPr+V85HHGYczHnqONWx9IhCYJQ0mhtlZEWS927AMaOHctLL73E119/zZIlS6hWrZr5B+Snn37KV199xZdffom/vz92dnZMnjyZ9PT0Igv3wIEDPPfcc8yePZtu3bqZR0rmzZtXZPfI7t6kRJIkc+KSl82bNxMWFpZrGs1gMLB9+3aeeuopbGxs7nv+g14DUKmUsY3s04r3qynKnswBvP7662zdupXPPvuM6tWrY2Njw8CBA81/Pg+7N8C4ceMYMWIEX3zxBUuWLGHIkCGPreg8OzHCYwFedl48XflpQIzyCIJQSJKkTCtZ4iFJBQp18ODBqFQqVqxYwbJlyxgzZgxS5jX27dtHnz59GD58OA0aNKBq1apcunQp39f28/Pj5s2bhIeHm587ePBgjmP2799PpUqVmDZtGk2bNqVGjRo56mMAdDodBoPhofc6efIkSUlJ5uf27duHSqWiVq1a+Y75XosWLeLZZ58lKCgox+PZZ581Fy/Xr1+fPXv25JmoODg4ULlyZbZv357n9T08PAByfI+yFzA/yL59+xg1ahT9+vXD398fLy8vrl+/bn7d398fo9HIrl277nuNnj17Ymdnx7fffsu///7LmDFj8nXvoiYSHgsxLVHffH0z4YnhDzlaEASh5LK3t2fIkCFMnTqV8PBwRo0aZX6tRo0abN26lf3793P+/Hmef/55IiMj833trl27UrNmTUaOHMnJkyfZs2cP06ZNy3FMjRo1CAkJYdWqVVy9epX58+ezbt26HMdUrlyZ4OBggoKCiI6OzrMPznPPPYe1tTUjR47kzJkz7Nixg5deeokRI0aY63cKKioqir///puRI0dSr169HI/AwEDWr19PbGwskyZNIj4+nmeffZajR49y+fJlfvnlF/NU2qxZs5g3bx7z58/n8uXLHD9+nAULFgDKKEzLli3Nxci7du3KUdP0IDVq1OCPP/4gKCiIkydPMmzYsByjVZUrV2bkyJGMGTOG9evXExwczM6dO1mzZo35GLVazahRo5g6dSo1atTIc8rxcRAJj4XUcatDC68WGGQDy88vt3Q4giAIxWrs2LHcuXOHbt265ai3mT59Oo0bN6Zbt2507NgRLy8v+vbtm+/rqlQq1q1bR0pKCs2bN2fcuHF8+OGHOY555plnePXVV5k0aRINGzZk//79zJgxI8cxAwYMoHv37nTq1AkPD488l8bb2tqyefNmYmNjadasGQMHDqRLly4sXLiwYN+MbEwF0HnV33Tp0gUbGxuWL1+Om5sb//33H4mJiXTo0IEmTZrw448/mqfPRo4cyZdffsk333xD3bp16d27N5cvXzZfa/Hixej1epo0acLkyZP54IMP8hXf559/jouLC61btyYgIIBu3brRuHHjHMd8++23DBw4kBdffJHatWszfvz4HKNgoPz5p6enM3r06IJ+i4qMJMv5XF9YSsTHx+Pk5ERcXByOjo4WjWV36G4mbp+IndaOrQO34qBzsGg8giA8uVJTUwkODqZKlSpYW1tbOhxBKJA9e/bQpUsXbt68+cDRsAf9PX/Un99ihMeC2pZvSzWnaiRlJPH7pd8tHY4gCIIgFKm0tDRCQ0OZNWsWgwYNKvTUX1EQCY8FqSSVuZZn+fnlZBgf3olTEARBEEqKlStXUqlSJe7evcsnn3xi0VhEwmNhvar2ws3ajcjkSDZf32zpcARBEAShyIwaNQqDwcCxY8coX768RWMRCY+F6dQ6hvkNA5Ql6mWspEoQBEEQHguR8DwBBtccjI3GhguxFzgUccjS4QiCIAhCqSMSnieAs7Uzfar1AUQjQkEQBEEoDiLheUIE1glEQmJv2F4u37n88BMEQRAEQcg3kfA8IXwdfelaSdlpdtm5ZRaORhAEQRBKF5HwFBHZaMQQH48xLa3QhceBdQIB2HBtA1HJUUUZniAIgiCUaWK39CJiiIvjcqvW5q8lK6vMhw6VzgrJ2jrrc9PzVtbZPrfC28qaV256cDM9kn3X36bnyJnoKla04LsSBEEQhNLBognP7t27+fTTTzl27Bjh4eGsW7cu33uo7Nu3jw4dOlCvXr187/panOR7NpqT09LMzz14/92c2pg/28+N3WOp9s8/qKysiiJEQRAEixg1ahR3795l/fr1lg5FKMMsmvAkJSXRoEEDxowZQ//+/fN93t27dwkMDKRLly4F2lW3OGk8Pal16qQ50ZHT0pTpLfPn6cjpaRhTU5HT0pXn0zOPSc363JiayqZLf+N3NgHnm6HELlmC+wsvWPrtCYIgCEKJZtGEp0ePHvTo0aPA573wwgsMGzYMtVr9xPzGIEkSkk4HOh04PNomoLYXavDz4vd55S8jUd9+h9Mzz6DNtruwIAhCabFr1y7eeOMNTp48iaurKyNHjuSDDz5Ao1F+PP3222/Mnj2bK1euYGtrS6NGjfjzzz+xs7Nj586dvPnmm5w9exatVkvdunVZsWIFlSpVsvC7Ep5EJa6GZ8mSJVy7do3ly5fna3v7tLQ00rJNN8XHxxdneEWif43+bOy4gXMnjlLnZho3535I1QVfWzosQRCeILIsk6JPsci9bTQ2SJL0yNcJCwujZ8+ejBo1imXLlnHhwgXGjx+PtbU1s2bNIjw8nKFDh/LJJ5/Qr18/EhIS2LNnD7Iso9fr6du3L+PHj2flypWkp6dz+PDhIolLKJ1KVMJz+fJl3n77bfbs2WPO/h9m7ty5zJ49u5gjK1o6tY4vO3/F68GDqL3wFmlb/+Puvj04t2ln6dAEQXhCpOhTaLGihUXufWjYIWy1to98nW+++QZfX18WLlyIJEnUrl2bW7du8dZbb/Huu+8SHh6OXq+nf//+5lEbf39/AGJjY4mLi6N3795Uq1YNAD8/v0eOSSi9SsyydIPBwLBhw5g9ezY1a9bM93lTp04lLi7O/Lh582YxRll0XK1dmf7cj/zXTClYPj/9NYzp6RaOShAEoeicP3+eVq1a5RiVadOmDYmJiYSGhtKgQQO6dOmCv78/gwYN4scff+TOnTsAuLq6MmrUKLp160ZAQABfffUV4eHhlnorQglQYkZ4EhISOHr0KCdOnGDSpEkAGI1GZFlGo9GwZcsWOnfunOs8KysrrEroKqeqzlVpNu0z4p99CefwBDZ+Oone036wdFiCIDwBbDQ2HBpmmb33bDQ2j+U+arWarVu3sn//frZs2cKCBQuYNm0ahw4dokqVKixZsoSXX36Zf//9l9WrVzN9+nS2bt1Ky5YtH0t8QslSYkZ4HB0dOX36NEFBQebHCy+8QK1atQgKCqJFC8sM7Ra3lrW6kjBW2WfLe/UethxbbeGIBEF4EkiShK3W1iKPoqqT8fPz48CBAzmate7btw8HBwcqVKhgfp9t2rRh9uzZnDhxAp1Ox7p168zHN2rUiKlTp7J//37q1avHihUriiQ2ofSx6AhPYmIiV65cMX8dHBxMUFAQrq6uVKxYkalTpxIWFsayZctQqVTUq1cvx/nlypXD2to61/OlTZcX53Bw4z6cr0Vz7KP3Cfq6Fg3LNbR0WIIgCPkWFxeXq2fahAkT+PLLL3nppZeYNGkSFy9eZObMmUyZMgWVSsWhQ4fYvn07Tz/9NOXKlePQoUNERUXh5+dHcHAwP/zwA8888ww+Pj5cvHiRy5cvExgYaJk3KDzxLJrwHD16lE6dOpm/njJlCgAjR45k6dKlhIeHExISYqnwnhiSSkX9uQu48exQ2p028OnP/+P9//1Gefvylg5NEAQhX3bu3EmjRo1yPDd27Fg2btzIG2+8QYMGDXB1dWXs2LFMnz4dUEb2d+/ezZdffkl8fDyVKlVi3rx59OjRg8jISC5cuMDPP/9MTEwM3t7eTJw4keeff94Sb08oASS5sBs/lVDx8fE4OTkRFxeHo6OjpcMpkJvTppL4+3qCPeGnybX4udcvOOgereePIAglQ2pqKsHBwVSpUgVra2tLhyMIxeJBf88f9ed3ianhEcD7tTeQHOypEgmVd17mjV1voDfqLR2WIAiCIDzxRMJTgmhcXSn3ymQAhu4ycurKXj46/FGhd2cXBEEQhLJCJDwljMuzQ7CqVQv7VHh2t8zqi6tZcUGsShAEQRCEBxEJTwkjaTR4zVAK+roGGakSLvPJkU/YHbrbwpEJgiAIwpNLJDwlkG3TpjgGBCDJ8MYeZ2SjgTd2vcHF2IuWDk0QBEEQnkgi4Smhyr3+OipbW9yvxjAqpArJ+mQm/TeJ6JRoS4cmCIIgCE8ckfCUUFrPcrhPfBGAXv/GUFvnS0RSBC//97LFdlAWBEEQhCeVSHhKMNcRI9BVqYIx9g5zLjbEycqJ09GnmbZ3GkbZaOnwhCfQndVriFm02NJhCIIgPHYi4SnBJJ0Oz2nTANCv/YuvKr6GRqVh642tLDyx0MLRCU8aOT2diPfe4/ann6KPFlOfgiCULSLhKeHs27bB4amnwGDA/bv1zG41C4AfT//In1f+tGxwwhNFHxMDBoPy+e3bFo5GEPKvY8eOTJ482fx15cqV+fLLLx94jiRJrF+//pHvXVTXESxPJDylQLm33kKysiL58GE6XtIy3n88ALMOzOJoxFELRyc8KfRRUVmfixEe4TEICAige/fueb62Z88eJEni1KlTBb7ukSNHmDBhwqOGl8OsWbNo2LBhrufDw8Pp0aNHkd7rXkuXLsXZ2blY7yGIhKdU0FUoj9sEJcmJ/PgTXqw5mqcqPYXeqGfyzsmExIsNWIWcSY4+OsaCkQhlxdixY9m6dSuhoaG5XluyZAlNmzalfv36Bb6uh4cHtra2RRHiQ3l5eWFlZfVY7iUUL5HwlBJuY8eirVABfWQksd//yJy2c/B39ycuLY6J2ycSlxZn6RAFC9NHZU94xAiPUPx69+6Nh4cHS5cuzfF8YmIia9euZezYscTExDB06FDKly+Pra0t/v7+rFy58oHXvXdK6/Lly7Rv3x5ra2vq1KnD1q1bc53z1ltvUbNmTWxtbalatSozZswgIyMDUEZYZs+ezcmTJ5EkCUmSzDHfO6V1+vRpOnfujI2NDW5ubkyYMIHExETz66NGjaJv37589tlneHt74+bmxsSJE833KoyQkBD69OmDvb09jo6ODB48mMjISPPrJ0+epFOnTjg4OODo6EiTJk04elQZ3b9x4wYBAQG4uLhgZ2dH3bp12bhxY6FjKck0lg5AKBoqa2s835lK6IsTiVm6FKf+/ZjfeT7DNgzjevx1Xtv1Gt92/RatSmvpUAUL0UdH5fm5UDLJsoycYpkWFJKNDZIkPfQ4jUZDYGAgS5cuZdq0aeZz1q5di8FgYOjQoSQmJtKkSRPeeustHB0d2bBhAyNGjKBatWo0b978ofcwGo30798fT09PDh06RFxcXI56HxMHBweWLl2Kj48Pp0+fZvz48Tg4OPDmm28yZMgQzpw5w7///su2bdsAcHJyynWNpKQkunXrRqtWrThy5Ai3b99m3LhxTJo0KUdSt2PHDry9vdmxYwdXrlxhyJAhNGzYkPHjxz/0/eT1/kzJzq5du9Dr9UycOJEhQ4awc+dOAJ577jkaNWrEt99+i1qtJigoCK1W+b9+4sSJpKens3v3buzs7Dh37hz29vYFjqM0EAlPKWLfqRN27duRtHsPkXPm4vvD9yzovIARm0ZwKPwQnx75lHdavGPpMAULyT6qYxAjPCWenJLCxcZNLHLvWsePIeVzSmnMmDF8+umn7Nq1i44dOwLKdNaAAQNwcnLCycmJ119/3Xz8Sy+9xObNm1mzZk2+Ep5t27Zx4cIFNm/ejI+PDwBz5szJVXczffp08+eVK1fm9ddfZ9WqVbz55pvY2Nhgb2+PRqPBy8vrvvdasWIFqampLFu2DDs7OwAWLlxIQEAAH3/8MZ6engC4uLiwcOFC1Go1tWvXplevXmzfvr1QCc/27ds5ffo0wcHB+Pr6ArBs2TLq1q3LkSNHaNasGSEhIbzxxhvUrl0bgBo1apjPDwkJYcCAAfj7+wNQtWrVAsdQWogprVJEkiQ8p04FrZakPXtI3LGDWq61mNtuLgArL6xkzcU1Fo5SsJTsSU726S1BKE61a9emdevWLF6s9H+6cuUKe/bsYezYsQAYDAbef/99/P39cXV1xd7ens2bNxMSkr/aw/Pnz+Pr62tOdgBatWqV67jVq1fTpk0bvLy8sLe3Z/r06fm+R/Z7NWjQwJzsALRp0waj0cjFi1lb+9StWxe1Wm3+2tvbm9uFXBlpen+mZAegTp06ODs7c/78eQCmTJnCuHHj6Nq1Kx999BFXr141H/vyyy/zwQcf0KZNG2bOnFmoIvHSQozwlDJWVargNmoUMT/+SOScudi1bk2Xil14udHLzD8xn7mH5lLFqQrNvJpZOlThMctRwxMjipZLOsnGhlrHj1ns3gUxduxYXnrpJb7++muWLFlCtWrV6NChAwCffvopX331FV9++SX+/v7Y2dkxefJk0tPTiyzeAwcO8NxzzzF79my6deuGk5MTq1atYt68eUV2j+xM00kmkiRhNBZfM9hZs2YxbNgwNmzYwKZNm5g5cyarVq2iX79+jBs3jm7durFhwwa2bNnC3LlzmTdvHi+99FKxxfOkEiM8pZD7C8+j8fQkIzSUmEWLABjnP44eVXqgl/VM2TmFmwk3LRyl8LjlXKUlRnhKOkmSUNnaWuSRn/qd7AYPHoxKpWLFihUsW7aMMWPGmK+xb98++vTpw/Dhw2nQoAFVq1bl0qVL+b62n58fN2/eJDw83PzcwYMHcxyzf/9+KlWqxLRp02jatCk1atTgxo0bOY7R6XQYMvtUPeheJ0+eJCkpyfzcvn37UKlU1KpVK98xF4Tp/d28mfV/9rlz57h79y516tQxP1ezZk1effVVtmzZQv/+/VmyZIn5NV9fX1544QX++OMPXnvtNX788cdiifVJJxKeUkhlZ4fnW28CEPPDj6SHhiFJEu+1fo+6bnW5m3aXl/97mcT0xIdcSSgtZFnOkeQY4+MxpqVZMCKhLLG3t2fIkCFMnTqV8PBwRo0aZX6tRo0abN26lf3793P+/Hmef/75HCuQHqZr167UrFmTkSNHcvLkSfbs2cO0zA702e8REhLCqlWruHr1KvPnz2fdunU5jqlcuTLBwcEEBQURHR1NWh7/Pp577jmsra0ZOXIkZ86cYceOHbz00kuMGDHCXL9TWAaDgaCgoByP8+fP07VrV/z9/Xnuuec4fvw4hw8fJjAwkA4dOtC0aVNSUlKYNGkSO3fu5MaNG+zbt48jR47g5+cHwOTJk9m8eTPBwcEcP36cHTt2mF8ra0TCU0o59OiBbfPmyGlp3P74YwCsNdZ81ekrPGw8uHL3ClP3TMVgfPBvNELpYExMRE5NVb7IrC0QhcvC4zR27Fju3LlDt27dctTbTJ8+ncaNG9OtWzc6duyIl5cXffv2zfd1VSoV69atIyUlhebNmzNu3Dg+/PDDHMc888wzvPrqq0yaNImGDRuyf/9+ZsyYkeOYAQMG0L17dzp16oSHh0eeS+NtbW3ZvHkzsbGxNGvWjIEDB9KlSxcWLnz0rXwSExNp1KhRjkdAQACSJPHnn3/i4uJC+/bt6dq1K1WrVmX16tUAqNVqYmJiCAwMpGbNmgwePJgePXowe/ZsQEmkJk6ciJ+fH927d6dmzZp88803jxxvSSTJsixbOojHKT4+HicnJ+Li4nB0dLR0OMUq9dIlgvv1B4MB30U/Yd+mDQCno04z6t9RpBvTGVtvLJObTLZsoEKxS7sWzLWePVHZ26NycEAfHk7lNauxKUTTN8EyUlNTCQ4OpkqVKlhbW1s6HEEoFg/6e/6oP7/FCE8pZl2zJi7DhgEQ8/0P5uf9Pfx5r817ACw6s4h/rv1jkfiEx8fUd0fj4YHG3T3zOTHCIwhC2SESnlLObewYUKtJPnyY1GzLJntV7cXYesqy0Jn7ZnIqquwuVSwLTNNXGnf3rIRHLE0XBKEMEQlPKaf18sLh6acAiP3llxyvvdz4ZTpW6Ei6MZ3JOyYTmZT/QkGhZDGN5mg83NG4u2U+J7otC4JQdoiEpwxwHTECgPi//0F/5475eZWk4qP2H1HduTpRKVG8suMVUvSWaVUvFC/TaI7a3R115giPQfTiEQShDBEJTxlg06gR1nXqIKelcXftbzles9PasaDzApytnDkbc5aZ+2ZSxurYywTzCI+7h5jSKuHEv0+hNCvOv98i4SkDJEnCJXOU586KFch6fY7XKzhU4POOn6ORNGy6vokfT5fNplSlmT5HDY9HjueEksG0VUFRdiAWhCeN6e939q05iorYWqKMcOzVk9uffYY+IoKEbdtx7N4tx+vNvJrxTst3eO/Aeyw4sYBqTtXoUqmLhaIVilr2Gh5V5qaPIuEpWTQaDba2tkRFRaHValGpxO+rQuliNBqJiorC1tYWjabo0xOR8JQRKp0OlyGDif7mW2KX/5Ir4QEYVHMQl+9cZuWFlUzdO5VfHH6hlmvxtEsXHi99VOaydHeR8JRUkiTh7e1NcHBwrm0RBKG0UKlUVKxYscDbl+SHSHjKEOchzxL9w4+kHD1G6rlzWGfbh8XkzWZvci3uGofCD/Hyfy+zotcK3GzcLBCtUFRkgwFDbCyQM+GRU1IwJiWhyrbzs/Bk0+l01KhRQ0xrCaWWTqcrttFLkfCUIVrPcjh260b8hg3ELv8Vnzkf5jpGo9Iwr8M8hm0YRkhCCFN2TuGnp39Cq9bmcUWhJDDExoLRCCoValdXJLUaydYWOTkZfXQ0OpHwlCgqlUp0WhaEQhCTwGWM64jhAMT/8w/6zN/67+Vk5cSCLguw19pz/PZxPjj0gVgZUoKZpq7UbkqyA6Bxc8vxmiAIQmknEp4yxrpBA6z9/ZHT07m7Zu19j6vqVJVP2n+CSlLxx+U/+PX8r48xSqEoZV+SbiKWpguCUNaIhKeMkSTJPMpzZ+VK5IyM+x7brkI7pjSZAsCnRz9lf9j+xxKjULRMSY0pycn+uT5GJDyCIJQNIuEpgxy6d0ft7o4+MpKErVsfeGxgnUD6VOuDUTby+q7XCY4LfkxRCkUlew8eE42H2EBUEISyRSQ8ZZCyRH0IALG/LH/gsZIk8W6rd2no0ZCEjARe/u9l4tPjH0eYQhEx75SeLeExby8hEh5BEMoIkfCUUc5DBoNWS8qJE6ScPvPAY3VqHV90+gIvOy+ux1/nq2NfPaYohaJgyNZ00ETjJmp4BEEoW0TCU0Zpy5XDsXt3AO4sf/AoD4C7jTtz2s4BYO2ltZyKOlWs8QlFR3879wiPeUpLbCAqCEIZYdGEZ/fu3QQEBODj44MkSaxfv/6Bx//xxx889dRTeHh44OjoSKtWrdi8efPjCbYUMi9R37gxX7UczbyaEVA1ABmZ9w++j96of+g5guWZl6XnVbQsprQEQSgjLJrwJCUl0aBBA77++ut8Hb97926eeuopNm7cyLFjx+jUqRMBAQGcOHGimCMtnWzq18e6QX3kjAzurFmTr3Nea/oaDjoHLsReYNWFVcUcoVAUHrgsPTpa9FgSBKFMsGjC06NHDz744AP69euXr+O//PJL3nzzTZo1a0aNGjWYM2cONWrU4O+//y7mSEsv1xGBANxduQo5H+3q3WzcmNx4MgALTiwgMimyOMMTHpExJQVjYiIAmnJZCY86s/EgGRkY4+IsEZogCMJjVaJreIxGIwkJCbi6ut73mLS0NOLj43M8hCyOTz+FxsMDfVQU8VsevETdZGDNgdT3qE+yPplPjnxSzBEKj8JUoyNZW+fYM0tlZYXK0VE5RkxrCYJQBpTohOezzz4jMTGRwYMH3/eYuXPn4uTkZH74+vo+xgiffJJOh/PQZwG488sv+TpHJamY0XIGKknFlhtb2Bu2tzhDFB5B9l3S7919OGtaSxQuC4JQ+pXYhGfFihXMnj2bNWvWUK5cufseN3XqVOLi4syPmzdvPsYoSwaXwYORtFpSTp4k5VT+Vl/Vdq3Nc37PAfDhwQ9J1acWZ4hCIeXVdNBEFC4LglCWlMiEZ9WqVYwbN441a9bQtWvXBx5rZWWFo6NjjoeQk8bdHceePYGHNyLMbmLDiZSzLUdoYig/nv6xuMITHkFePXhMNO6mDUSjHmtMgiAIllDiEp6VK1cyevRoVq5cSa9evSwdTqnhMmIEAPH//kvG7dv5OsdOa8dbzd4CYPGZxVyLu1Zs8QmFY2osqM5jhEd0WxYEoSyxaMKTmJhIUFAQQUFBAAQHBxMUFERISAigTEcFBgaaj1+xYgWBgYHMmzePFi1aEBERQUREBHFilckjs6lXF5tGjSAjg7ur87dEHeCpSk/Rtnxb9EY9Hx78UCxxfsJkr+G5l2mZuqjhEQShLLBownP06FEaNWpEo0aNAJgyZQqNGjXi3XffBSA8PNyc/AD88MMP6PV6Jk6ciLe3t/nxyiuvWCT+0sa8i/rq1RjzsUQdlL223mnxDlZqKw5HHGZD8IbiDFEooLx68JiIGh5BEMoSjSVv3rFjxweOCCxdujTH1zt37izegMo4h6eeQlOuHPrbt0n491+cnnkmX+f5Ovgyof4EFpxYwKdHPqVd+XY4WTkVc7RCfugfVMMjdkwXBKEMKXE1PELxkbRaXIYNBSB22S8Fmp4aVXcUlR0rE5say4ITC4orRKGAshKePEZ43NxyHCMIglCaiYRHyMF58GAknY7UM2dIPXky3+fp1DpmtJwBwJqLazgddbq4QhTySZblBy5LNxctx8YiGwyPNTZBEITHTSQ8Qg4aV1ccM1e/FWSJOkBz7+b0rtpbbC76hDDGxUFGBpBtK4lsNK6uIElgNGK4c+dxhycIgvBYiYRHyMW8i/rmzWRE5m+Juolpc9HzsedZfXF1cYQn5JN5l3QnJ1Q6Xa7XJY0Gdea2LGJaSxCE0k4kPEIu1nXqYNO0Cej13F1dsB3R3W3cc2wueju5YAmTUHTMCU8eBcsm5jqeKJHwCIJQuomER8iT63ClEeGdVasxpqUV6NyBNQdS370+SRlJYnNRCzIlMXktSTcRS9MFQSgrRMIj5Mmhaxc0Xl4YYmOJ37ipQOeqJBXTW05HJanYfH0z+8L2FVOUwoM8qOmgiWlpuiFGJDyCIJRuIuER8iRpNLgMGwZA7C/LCtxB2c/Nj2G1lfM/PCQ2F7WEB63QMjGt1BJTWoIglHYi4RHuy3nQQCQrK9LOnSflxIkCnz+x4UTK2ZTjZsJNFp1ZVAwRCg9i2hQ0r6aDJho3MaUlCELZIBIe4b40Li44BvQGIPaXXwp8vr3OnjebvwnAotOLuB53vSjDA0AfG8vVbt25OXFSkV+7pDM8oOmgiei2LAhCWSESHuGBXIcrS9QTtmwlIyKiwOc/Xelp2pRvQ4Yxgw8OfVDkm4vGLFpE+o0bJG7fTtrVq0V67ZIuq2j5ASM8piktUcMjCEIpJxIe4YGsa9fGtlkzMBi4s7JgS9RB2Vx0WvNpWKmtOBR+iI3BG4ssNn10NHd+XWH+On7Tv0V27dLAvCw9HwmPQdTwCIJQyomER3gol8xGhHdXr8aYWvDiY19HX8b7jwfg0yOfEp8eXyRxxfy0CDk1FcnGBoD4TZuKfASppJIzMszdkx80pWXeXiIuDmN6+mOJTRAEwRJEwiM8lEPnzmh8vDHcvUv8hsKN0IyuN5rKjpWJSY1h/vH5jxxTxu3b3Fm5EgCfOR8iabWkX71K2uXLj3zt0kAfG6t8otGgdrr/zvVqJyfQaAAwxMQ8jtAEQRAsQiQ8wkNJGg2upiXqy5cXahRFp9YxveV0QNlc9Ez0mUeKKebHn5DT0rBp1AiH7t2xa9cOUEZ5hGz1O25uSKr7/zOXVKpsu6aLhEcQhNJLJDxCvjgPHIhkbU3a+fMkHzhQqGu08G5Br6q9kJF578B7GIyF26E7IyKCu6uVfbo8XpqEJEk49ugBQMJGMa0FoI9StvR4UMGySVa35ahijUkQBMGSRMIj5Iva2RnnAQMAiJw7FzlzF+6Cer3p6zholc1FV10seBE0QMwPPyCnp2PTtAm2rVoBYN+pE5KVFek3bpB2/nyhrlua5KfpoInYXkIQhLJAJDxCvnm8NAm1iwtpl68Q+8vyQl3D3cadVxq/Aiibi0YmRRbo/Ixbt7i79rfMeF5GkiQA1PZ22LdvD4jVWpDVg+dBG4eaqN3dcpwjCIJQGomER8g3tbMz5V5/DYDohQvJiCxYsmIysOZA/N39ScpIYu7huQU6N/r7H5AzMrBt0QK7Fs1zvObYU5nWEqu18teDx8S0uajYXkIQhNJMJDxCgTj164dNw4YYk5OJ/OijQl1DrVIzs9VMNJKG7SHb2X5je77OSw8N4+7vvwPKaNO97Dt0QLKxISM0lNQzj1YUXdLp89Fl2SSr+aAoWhYEofQSCY9QIJJKhdfMd0GlImHTvyTt31+o69RyrcWoeqMAmHNoDgnpCQ89J/q7b0Gvx651K2ybNs31usrWFvuOHQAxrZVVw5OPhEdsLyEIQhkgEh6hwKz9/Mw7qUe8936hG9Y9X/95KjpU5HbKbb46/tUDj00PCSFu3XoA3Ce9dN/jTKu14v8t29NaWSM8+ZjSMi9LF6u0BEEovUTCIxSKxysvo3Z3J/36dWKXLC3UNaw11rzb6l1A6c0TdDvovsdGf/sdGAzYtWuHbeNG9z3Ovn17VLa26G+FkxJ0/+uVdgVZpaUW20sIglAGiIRHKBS1gwOeb74BQPS335IRFlao67TwbkGfan2QkZm1fxYZhtzL3dOvXyfuzz+BvGt3slNZW2PfuTMACf+WzWktY1IScnIykDV68yCmOh9jcjLGzPMEQRBKG5HwCIXmGBCAbdOmyKmpRMwt2Gqr7F5v+jqu1q5cjbvKojOLcr0e9c03YDRi37EjNvXrPzwu02qtfzcjG42Fjquk0kcpU1MqW1tUdnYPPV5lZ4dkba2cKwqXBUEopUTCIxSaJEl4vjsD1GoSt20ncdeuQl3H2dqZt5q9BcAPp37gWtw182tp164R/88GANwnPXh0x8SubVtUDg7oIyNJOX68UDGVZPoC9OAB5c/RvFJLTGsJglBKiYRHeCTWNWviGhgIQMSHczCmpRXqOj2q9KBN+TZkGDOYvX82RlkZmYn+OnN0p0sXbOrVzde1VDodDl26ABC/seztrVWQFVomonBZEITSTiQ8wiNznzgRTblyZISEEPPjT4W6hiRJzGg5AxuNDcdvH+ePy3+Qdvky8RuV3dk9Jk0s0PUce3QHIH7LFmRD4fbsKqkK0nTQxDQaJHZMFwShtBIJj/DI1PZ2eE59G1D2uUoPCSnUdcrbl2diQyWx+fzo54TO/wJkGYenn8baz69A17Jr1QqVkxOG6GiSjxwtVDwlVUGaDpqIKS1BEEo7kfAIRcKhe3fsWrdCTk8n4sMPC90D5zm/56jjVgeXsHjSt+4AScK9gKM7AJJOh8NTXQFlq4myxDQtVZARHvP2EqL5oCAIpZRIeIQiIUkSntNngFZL0q7dJP73X6Guo1FpmNVqFoP2KglTavvGWNesWahrOXZXVmslbNmCrNcX6holUUGaDppo3E01PCLhEQShdBIJj1BkrKpWwW30aAAiP5yDMSWlUNepEinT4qIRIzCv/k2SMpIKdR27li1Qu7hguHOHpEOHCnWNkshQiBoe85SWSHgEQSilRMIjFCn3F55H4+NNxq1bRH//faGuEbXwawBONLDlpH0s84/PL9R1JI0Gh6efBsrWtJZ5WXohEh6DSHgEQSilRMIjFCmVrS2eU6cCELtoMWnBwQU6P+X0GWU6TKWi+qvTAFh5YSWnok4VKh7T3loJW7chZ+Tu4lzayAaDuXlgQZalq7PV8JTlPcgEQSi9RMIjFDmHrl2xa98OOSODyPc/KNAP0KiFCwBwCgigZcv+BFQNULadODCLDGPBExbbZk1Ru7tjjIsj6cCBAp9f0hju3gWDASQJjatLvs8z1fDI6ekYEx6+c70gCEJJIxIeochJkoTX9OlIOh1J+/eTsHlzvs5LOXmSpF27Qa3G/cX/AfBGszdwtnLm8p3L/Hz254LHolbjaJrWKgNNCM3TWS4uSFptvs9TWVujsrfPcQ1BEITSRCQ8QrHQVayI27hxAETO/Qhj0sMLj6MWLATAqU8fdJUqAeBi7cKbzd4E4Nugb7kRf6PAsZj21krYvh1jenqBzy9JCtN00EQULguCUJqJhEcoNm4TxqOtUAF9ZKSyAegDJB8/TtLevaDR4P6/F3K81rtqb1p5tyLdmM57B94rcI2JTePGaMqVw5iQQNLefQV+HyWJuQdPAZoOmojCZUEQSjOLJjy7d+8mICAAHx8fJEli/fr1Dz1n586dNG7cGCsrK6pXr87SpUuLPU6hcFTW1nhOVwqPY39eRtrly/c9NmqBUrvj3K8fOl/fHK9JksSMVjOwVltzOOIw66+sL1AckkqFQ/duQOlfrWWILvwIj2l7CTHCIwhCaWTRhCcpKYkGDRrw9ddf5+v44OBgevXqRadOnQgKCmLy5MmMGzeOzfmsEREeP4eOHbHv0gX0eiLeez/P0ZnkI0dIPnAQtFrcX3g+z+v4OvjyYsMXAfjs6GdEpxTsh7JptVbi9u0YU1ML+C5KDvOUVgGaDppo3MT2EoIglF4WTXh69OjBBx98QL9+/fJ1/HfffUeVKlWYN28efn5+TJo0iYEDB/LFF18Uc6TCo/CcOhXJ2prkI0eI/2dDrtdNtTvOA/qjLV/+vtcZUWcEtV1rE58ezydHPilQDDYNG6Lx8caYnEzinj0FewMlSGF68JiYa3jEBqKCIJRCJaqG58CBA3Tt2jXHc926dePAA5Ybp6WlER8fn+MhPF66CuXNIzeRn3yMIduy56SDh0g+fBhJq8X9+bxHd0w0Kg2zWs9CJanYFLyJPaH5T1wkScKxm7KDekIpntYybytRgB48JhrzlFZUkcYkCILwJChRCU9ERASenp45nvP09CQ+Pp6U+2xjMHfuXJycnMwP33vqQ4THw3XMGHSVK2OIijbX68iynFW7M3gwWm/vh16nrltdhvsNB+CDgx+QnJGc7xjMq7V27MSYnP/zShJ9VME3DjURq7QEQSjNSlTCUxhTp04lLi7O/Lh586alQyqTVDodntOnA3Bn+a+kXrhA0v79pBw7hqTT4TZhQr6vNbHhRHzsfLiVdIuFQQvzfZ51vXpoK1RATkkhcdeuAr+HkqAwG4eaqDNreAyihkcQhFKoRCU8Xl5eREZG5nguMjISR0dHbGxs8jzHysoKR0fHHA/BMuzbtsGhWzcwGomY/R7RptqdZ4eg9SyX7+vYam2Z0WoGAL+e/5Wz0WfzdZ4kSTj2UKa14jf9W8Don3zGtDSMmVO2hRrhMU1pxcYiG41FGpsgCIKllaiEp1WrVmzfvj3Hc1u3bqVVq1YWikgoKM+pbyPZ2pJy4gQpQUFI1ta4jx9f4Ou0Ld+WnlV6YpSNBdp2wrxaa9cuDImF24X9SWVaki7pdKgKkdhrXF0zL2RQtqgQBEEoRSya8CQmJhIUFERQUBCgLDsPCgoiJCQEUKajAgMDzce/8MILXLt2jTfffJMLFy7wzTffsGbNGl599VVLhC8UgtbLC4+JL5q/dhk6tFBN8gDebPYmTlZOXIi9wC/nfsnXOVZ+fugqVUJOSyNxx45C3fdJpc/Wg0eSpAKfL2m1qF2U/bfE0vTST5Zl0kNDxWaxQplh0YTn6NGjNGrUiEaNGgEwZcoUGjVqxLvvvgtAeHi4OfkBqFKlChs2bGDr1q00aNCAefPm8dNPP9GtWzeLxC8UjmtgIDYNGqDx9MRt3NhCX8fNxo3Xm74OKNtORCRFPPQcSZJwyCxejv+3dE1rmZekF6J+x8S0iahYqVX6xf/9N1e7PkXMjz9ZOhRBeCw0lrx5x44dH/jbRV5dlDt27MiJEyeKMSqhuElaLZVW/AqyjKR5tL+Cfar1Yd3ldRy/fZzPj37OJx0e3p/HsXsPYr79jqTduzEkJKB2cHikGJ4UWftoFW7EDDL791y+IraXKAOSjx8HIPX0KQtHIgiPR4mq4RFKD0mtfuRkB5QRm6ktpiq9ea5v4mjE0YeeY1WzBrpq1ZAzMki4pyasJNM/wrYSJqZkSR8tmg+WdhmhYcrHiMiHHCkIpYNIeIQSr7ZrbQbVHATA3MNz0Rv1DzxeWa2V2ZOnFK3WMm8c+kgJj+jFU1ZkhJkSnnALRyIIj4dIeIRSYVLDSTjqHLl05xK/X/r9oceblqcn7t+PIS6uuMN7LB5lHy0TkfCUDbLRaE54DNExyOnpFo5IEIpfoRIevV7Ptm3b+P7770nI3Cbg1q1bJCYmFmlwgpBfztbOTGo0CYAFQQu4m3r3gcdbVauGVc2akJFBwrZtjyHC4lc0IzyiaLks0EdFZyU5skzGbfHnLZR+BU54bty4gb+/P3369GHixIlEZbay//jjj3n99deLPEBByK9BNQdRw6UGcWlx+erAbNpqIn5j6dhbyxD16DU8pk1HDaKGp1TLCAvN8bU+8uErHAWhpCtwwvPKK6/QtGlT7ty5k6O7cb9+/XI1BRSEx0mj0jC1+VQA1l5ay8XYiw883rG7Mq2VdPAg+jt3ij2+4iTLcrad0gu/SiuraFlMaZVmGaE5E56McJHwCKVfgROePXv2MH36dHQ6XY7nK1euTFjmnLAgWEozr2Z0q9wNo2xk7uG5D2x7oKtcGas6fmAwkLBl62OMsugZExLMUxSPVMOTea7hzh3kjPx1rxZKnvRQMcIjlD0FTniMRiMGgyHX86GhoTiUkn4mQsn2WpPXsFZbcyzyGJuvb37gsabVWvGbSva0lmlERuXoiMrKqtDXUTs7g1qtXDM2tihCE55ApiXpZLaGECM8QlFJTNPzw+6r/LTnmqVDyaXACc/TTz/Nl19+af5akiQSExOZOXMmPXv2LMrYBKFQvO29GeuvdHD+7OhnJGck3/dYU8KTfPhwiZ7G0RdB/Q6ApFKZ99QS20uUXqYpLZu6dQExwiM8ujtJ6Xy+9RJtPvqPORsv8NX2y8SnPlmjxAVOeObNm8e+ffuoU6cOqampDBs2zDyd9fHHHxdHjIJQYKPqjqK8fXkikyNZdGbRfY/TVaiAtb8/GI3Eb9nyGCMsWkWxQsvEtDWFIUYkPKWVaUm6bbOmytdihEcopPC4FN77+xytP/qP+dsvE5eSQVV3O2b0qoO1Rm3p8HIocKvbChUqcPLkSVatWsWpU6dITExk7NixPPfcczmKmAXBkqw11rzR9A0m75zM0jNL6Vu9L74Ovnke69ijB6mnT5OwcROuw4Y95kiLhqEIuiybaNzdSUMULpdWsl5PRoSS4Ng0aQI/LSJDjPCUKDGJaawPusVvx0K5Hp1E+5ru9K7vQxe/ctjqHs+OUdeiEvlu11XWnQgjw6DUStb1ceTFjtXpXs8LtargGxgXt0J9ZzQaDcOHDy/qWAShSHWu2JmW3i05GH6Qz458xledv8rzOMce3bn9ySckHztGRuRttJ7lHnOkj06f2R7iUQqWTTRumc0HxZRWqZQREQEGA5JOh42/P5DVfFC6ZzGK8OTIMBjZceE2vx0L5b8Lt9EbsxZkbD4byeazkdho1XTxK0dAAx861PTAWlv0IyxnwuL4dudVNp4Jx7QmpEUVV17sVJ32NdyRpCcv0TEpcMKzbNmyB74eGBhY6GAEoShJksTbzd9mwF8D+O/mf+wP20/r8q1zHaf19samUSNSTpwgYfNmXANHWCDaR2NKTtRFNMIDYoSntDLV72h9fFC7uSHpdMjp6WTcvo2uQgULRyfc63x4PL8dC2X9iTBikrI6Yjeo4MTApr7U83Fk67lI/j51i5uxKfxzKpx/ToXjYKXhqbqeBNT3oW0Nd7Tqwm+sIMsyh4Nj+XrnVXZfympS2dWvHP/rWJ0mlVwe6T0+LgVOeF555ZUcX2dkZJCcnIxOp8PW1lYkPMITpZpzNYbWHsry88v56MhH/O79O1qVNtdxjj26k3LiBPGbNpXMhCf60XdKNzGNEulFDU+pZE54KlRAkiQ0Xl5khISgj4goXQlP4m2IOA3OFcG9hqWjKZA7Sen8GRTGb8dDORMWb37e3d6K/o3LM7BJBWp6Zq2KblTRhTe61eJUaBx/n7zFhtPhhMel8sfxMP44HoazrZYe9bzoXd+HllXd8j3dJMsy/124zTc7r3LshtKrTCVBQAMf/texGrW9HIv2jRezAic8d/Jo0Hb58mX+97//8cYbbxRJUIJQlP7X8H9sDN5IcFwwK8+vJLBu7qTcoVt3Iud+RMqJE2SEh6P19rZApIVXFDulm5iuYRBTWqVSujnhKa989PQkIySk5BYuyzLEh0H4yZyPBNOmqBL4D4RO08C1ikVDfRC9wcjuy1GsPRrKtvOR5roYrVqiq58nA5tUoENNDzT3GamRJIkGvs408HXmnZ5+HAu5wz8nb7HhdATRiWmsPHyTlYdv4m5vRU9/LwIa+NCkoguqPJIfvcHIhtPhfLvzKhcilO2jdBoVg5pU4Pn21ajoZlt834hiVCTVTTVq1OCjjz5i+PDhXLhwoSguKQhFxlHnyCuNX2Hm/pl8e/JbelbtibtNzsRA61kO2yZNSD56lPh/N+M2epRlgi0kc8JTBDU8ajGlVaqZevCYRnM03l5ACVmabjTCnWAloYk4lZXcJOe1FYoELpXgznU4vRbOroMmo6H9G+Dg+bgjz5JyF26dAJ9GYOPM5cgEfjsWyh8nwohKSDMfVtfHkUFNKvBMw/K42hWstkqlkmhW2ZVmlV15N6Auh67F8PepW2w6oyQ/yw7cYNmBG3g7WdPL35uABj7Ur+BEmt7I78dD+X7XNUJilXYedjo1w1tWYmzbKpRztC7K78RjV2Tl3BqNhlu3bhXV5QShSPWt3pc1F9dwNuYs84/P57027+U6xqFHd5KPHiVh+7YSlfDIej2GzCaBGo8imNISCU+pln1KC0DrqSQ8T9wIj9EA0ZdzjtpEnIK0+NyHShoSHKoRYVebG7rqnKcqpzIqEJqkIqDGbSYaVyBd3Q5HfoSgX6Hli9DmZbB2enzv5/Z5OPwDnFwFGcnoVdbs0LVnQVw7TsnVAHC109G3oTJlVcenaKaL1CqJ1tXdaV3dnff61GPvlWj+ORnOlrMRhMel8tPeYH7aG0xFV1tSMwzczky6XO10jG5dmcBWlXGyzV0GUBIVOOH566+/cnwtyzLh4eEsXLiQNm3aFFlgglCUVJKKt5u/zYhNI1h3ZR2Dag7C38M/xzH2bdsSCaSePIUxJQVVCWmzoI+NVYb11WqlU/IjMiU8xsREjKmpqKxL9m91Qk6mHjza8jlHeCy2NF2WIS4Uoi9C1CXkqIvow8+gjjqLSp+S6/B0tFxRVeaUvhInDZU4Y6zCJbkCaSn3joIkAXAx0gap2ydMbHsLts2GsKOw5zM4ugjaToHm40FbTP/WDXq4tAkOfQ/X95ifvivb4WxM4qnULTxltYXrupok+gdSs8sodLbFt2OBVq2iU61ydKpVjtSMeuy+eJtTh3fgen0DTyUewlpK46htI6zr9aLFU4OwdXAttlgsocAJT9++fXN8LUkSHh4edO7cmXnz5hVVXIJQ5BqWa8gz1Z7hr6t/8dHhj/il5y+opKz5cG3Fimg8PdFHRpJy8iR2LVtaMNr8M/fgcXVFUj/6MlSVg4N55Y4+OgZdZq2HUPIZU1PNLQzMNTxemVNaxT3Co09XpqOiLpqTG6IvQvQVyEgyHyYBpvGEJNmKs3Jlzhorc8ZYhTNyZa7KPuiz/ehysNJQ3sEKDwcryjla42FvRTlHK8o5WBF2J4V5Wy8xb8tFGo5tQZtx2+DCBtj+nnLvrTPg0HfQ8W1oMAzURTTpkRwLx3+GI4sg7mbmG1Nxt9LTTLrajL0ZtennFsqLDrupHrWVyumX4Nh0OPMJNHhWmXrzrFM0sdxLliE8COuz63j67DqevhuSowVxD+MuOLULzkyDym2hVg+o2V2ZHizhCvynazQaiyMOQXgsJjeezLYb2zgVfYq/r/5Nn+p9zK9JkoRti+bE//U3SYcOlZiEx7xLehHU74DyfdC4u5Nx6xaG6CgQCU+pkZFZdqCytTWPBpoSnozIyKK5SVoCRF9SpqOiLiqfR11Ukh2jPu+4ZDXXZS+uyD5ckctzWa5AmHVNUh0q4e5oSzkHKzwdrPA3JTUOSkLj4WD10EZ7N+8ks+ZoKC+vPMGGl9vh5ddb+SF+chXsmAPxofDXS7BvPnSZAX7PQGF7yYSfVKatTv8G+lTlORtXaDKSGL8R9Pr5OhEZqXT182TeiF6oVC9AUowyzXZ0sfI9OvyD8qjYCpqOhTrPgKbw++MBSpITcUqpYzq7TqlrMtHaQa3uULcfWDvDpX+VR8wVuLZDeWx6E8rVURKfWj2gfBNQPVldlPPj8bRkFIQnhIetBy80eIHPj33OF8e+oEvFLtjr7M2v2zVXEp7kw0csGGXBmJsOFsEKLRN1ZsIj6nhKl3uXpANoMhMeQ3Q0xvR0VIVpPnjjAOz+REls4sPue1iaypYbkg+n0724aizPFdmHq7IPYZIX/hXdaV3NnTbV3Xne1xmdpvB9Y7J7r089zoTFcy48nokrjrNqQku0ajU0eg7qDVCmtnZ/BjGXYU0g+DSGrjOhasf83cCQAef/gkM/wM2DWc97N4Dmz0O9/mSorPjfj4eIiE+lqocdXwxpkLU6ys5NqSdqNQmCdyqJz4WNEHJAefzrBg2fg6ajwbVq/t+4LEPk2awkJ/Zq1msaG6jZTUlyajwNumyrrqq0g24fKgnrxU1K8hNyEG6fUx57Pwdb98zkpztU7QRW9rnv/wTKV8IzZcqUfF/w888/L3QwgvA4DPcbzh+X/+B6/HW+P/U9rzV9zfyabfPmAKScKjl1PFkbhz56wbKJKFwundLvKVgGULu4ZE1hFrb54OapysqjTLKdBwn2VQmWKnAsyYPdsS5cNPgQjivKpBX4eTvSppobQ6u707yKK3ZWxfP7t7VWzbfDG9N7wV6O3bjD3I0XeDcgc7pIaw2tJkKjEXBgIexfCLeOw7I+yg/yLu9C+cZ5XzjxNhxbqiQopiXwKg3U6aMkOr7NzSNFH/x5hsPXY7G30vDDiKY4WOdRBKxSQbXOyiP+Fhz/Rbl+wi3YP195VOsMTcdAzR73n367fR7O/KEkOTGXs57XWEONp5Qkp2Z30Nk9+BvnXkN5tHlZmaK7sk1JgK5sg+RoCFquPNQ6qNI+a/TH6cnt5ZSvv2EnTpx4+EHwRLeUFgQTrVrLm83e5MXtL7L83HL61ehHVSflNyetry8ab2/04eGknDiBXevcnZmfNEXZg8ckK+HJa7mv8LjIsszthDTOh8dzISKBC+HxXI1KoqKbLQH1fehYq2DbB5iWpGuzTVPmaD4YHl7whCc+3JzsbGj4LRujy7EjRE9yjCHHYb6uNgytrozitKrmhrv9I07TFEAlNzs+H9yQ8cuOsnhfME0qudCrfrZeW9aO0OkdaDZOGe05ujhrOqdOX+g8Pat5YegxOPy9klAYMjsf25VTRmCajAbHnD281h69yc8HbgDwxZCGVC+Xj9EQRx/o+Ba0ew0ub1biubIdrv6nPBy8ofFIaBwITuWVkTXTSE5UttYwaqtsSU43sCpkQbStK9QfrDz06RCyHy7+qxRk37muJEFXtsHG18HTXxn5qdlDWXqvKpqRuqKQr4Rnx44dxR2HIDxW7Sq0o0OFDuwK3cUnhz/h267fIkkSkiRh17wZcX/+RdLhwyLhiY56yJFCUUnNMHApMoEL4Qmcj4jnQngCFyLiuZOckevY02FxbDgVjr2VhqfreNK7gTdtq3s8dBrINKV1b1KjzUx4TJuK5jfefVeiidu7mP5AkLEaEw86AcqyZjc7Ha2qudG2ujJN5etq2WZ1T9Xx5IUO1fhu11Xe/O0ktbwccicf9uWg5yfQ6kXYMRdOrYZz6+H83+A/SBkxCTuWdXyFZtB8gpIUaXJPBZ68eZdp688AMLlrDZ6qU8D+P2oN1O6lPGKDlRGfE8uVEaVdH8HuT5VO0neCs85RaaF6VyXJqdVDSeaKkkanTPdV7Qjd5yrJ1qVNSgIUehgiTyuP/QvgzeCc02UWJmp4hDLrzWZvsv/Wfvbd2seu0F109O0IgG3zFsT9+RfJhw5bNsB8MiUlRdF00ETt7pZ57TI6pSXLyiqb6MvQcWqR1ijIskzonRTziM2FCCXBuR6dRLb9IM1UElRxt6O2tyN+Xg5U9bDnRMgd/jmVuX3AiTD+OBGGk42W7nWVDrotq7rm2ZH33h48JlrT0vSHJDy3E1L57/xttp2/zd4rUaRmGPlJuxXUsIsmdKrlQZvMBKeWp0OeXXwt6fWnaxJ08w4Hr8Xy4q/HWD+xTd5Fzy6Vof/3ynTO9veVH+inVimvqXVK7U/zCfef7gKiEtJ4/pdjpOuNPFXHk5c7P+L2Fq5V4KnZykjU+b+VUZ8b+5RkR6VRprvq9oNaPcHG+dHulV+SBOVqK4+2r0JSNFzeokx9qXVPVLIDhUx4jh49ypo1awgJCSE9PT3Ha3/88UeRBCYIxa2iY0UC6wSy6MwiPj78Ma18WmGltsK2RWYdz+nTGJOTUdk+Wf9o72XaAqIomg6alOXtJYwJUcStGodL2E4ATh3dzceu72FQW6NVq1CrJDQqFRqVhEYtZX7M/nXmMabXMo81TU1djEggIS3v1Uoutlr8vB2p7eVIbW8H/LwcqeFpn2vaqqe/N1N7+HE85E7m3klKB93VR2+y+uhN3O119KindNBtWilr+4B7e/CYaDzzXpouyzIXIhLYfj6Sredvc/Lm3RyvV3GU6JBxFmT43/OT0JVvUKjv+eOiUauYP7QRvefv5VJkIlP/OM2XQxrevxzDsy4MW6UU7Z5YriRCjUeC/YP/raXrjUz89TgR8alU87Dj88ENii7501gpW2X4D4TbF5TVVJVaK9NOlmbnDg2HKY8nUIETnlWrVhEYGEi3bt3YsmULTz/9NJcuXSIyMpJ+/foVR4yCUGwm1J/A31f/JjQxlF/O/cI4/3Foy5dH4+ON/lY4ycdPYN/2yW6oWTxTWsp/6PqYslPDcy0qkWM71tHp3AzcuUOarEWPivoZpxh7axbPZ0who4gGxbVqiWoe9pnJjYN59MbDwSrftZAqlUTTyq40zXP7gHR+OXiDXw7ewMvRml71veldzRHruDjl/uVzthowj/BERpKmN3DoWizbz0ey7fxtwu7mbP7XoIITXfw86erniV/8XqRV6eDki86nfhF8Z4pfOQdrFg5rzNAfD/Jn0C2aVnJhRKvKDz6pYkvlkU8fbDjH4euxOFhp+CHwPkXKRcE0uiLkS4H/9c6ZM4cvvviCiRMn4uDgwFdffUWVKlV4/vnn8S5hGy4Kgq3WllebvsrUPVP54dQP9K7aGy87L+yatyBu/XqSDx9+ohMeY3IyxiSlaZu6KFdpmXZMj45GluVSuyDhbnI6/5wK589j1+kU/hMvqP9GJclckSvwT80PaF1eQ5M94+hMELt8l3OkyWekyyoMRpkMo4zeYFQ+N8gYjMbMjzIZRiMGg4zeKKM3GtEbZJxstfhljtxUdbcvsmXX8ODtAyLiU1m0N5j/NtziGyDNzoEL8Xrq2GX9uaY6K1OY185epcf720jMNgJlpVHRtro7Xet40rl2OTyz76d09F/lY83uhe9dYwHNq7jydvfafLjxPO/9cw7/Cs409HUukmuvOXqTZdmKlKt5lIwl22VBgROeq1ev0qtXLwB0Oh1JSUlIksSrr75K586dmT17dpEHKQjFqVeVXqy+sJqgqCC+OPYFH7f/GNvmzc0Jz5PMNAIj2digsiu6qTeNm/IDUE5NxZiUhNq+9PynnWEwsutiFL8fD2X7+dt4GsNZoF1IQ43Sp+R65SFUGDyPyaYW/xVWwMpn8bm1hT7uH0Lf756olSf3yrV9wKUo/j4VTvzWcwDc0Drxyvy9VHW3o0MtD86GxRN78joLAO2daBLT9Hg4WNGldjm6+HnStro7Nro8VoIZjUqPFlBW5ZQw49pV4diNO/x7NoKJvx7n75faFniTznsF3bzL9HVKkfKrXWvStaBFykKxKnDC4+LiQkKCsl18+fLlOXPmDP7+/ty9e5fk5OQiD1AQipskSbzd4m2G/jOUjcEbGVJrCPVM/XjOnMGYlITK7iE9Kywke9PBohyFUdnaorK1xZicjD4qqsQnPLIscyYsnt+Ph/LXyVvEJim1h8+o9vOR1SJsScFo5YSqzwIq1+mT8+TqXWDQUlg9Qlm1o7WF3l+UiBENa62ap+t68XRdLyKST3BnP+Dtg06j4lp0EteildFBR2tnAFzSElk/vhn1q3g8vOYk/AQkRoLOHiq3K943UgwkSeKTQfW5GJlAcHQSk1cHsWRUM9SFrLWJSkjjhV+OkW5QipRf6ly9iCMWHlW+f005c0bJWtu3b8/WrVsBGDRoEK+88grjx49n6NChdOnSpXiiFIRiVtetLv1r9Adg5v6Z6D1dlDoHvZ7k4/nrQ2UJWU0Hi65+x8S0VYUhr5VaRgNsngZHlygrmp5QEXGpfLfrKt2+3E3Awr0s3X+d2KR0fO2M/FnhV+brFmJLClRshep/+5SmcXmp3Qv6/wBIcGyJ8t4t8b5P/wZf1IOt7xb83HClOV6rNv4cm96VL4Y0YGhzX2Y/U5cNM3ohWSl9cero0vJXYHsxc3SnWudH3/rAQhyttXw7vDHWWhW7L0Wx4L/LDz8pD8VapCwUmXyP8NSvX59mzZrRt29fBg0aBMC0adPQarXs37+fAQMGMH369GILVBCK2+TGk9kTtofr8df54OAHTGrejLh1YSQfPoR9u7aWDi9P5iXpxZDwaNw9yLgRknfh8rWdSmdagOt74ZkFT8wS1OR0PZvPRvDH8TD2Xok25yVWGhVP1/UisPJdmh55DSn6KkgqaP8mtH/j4RtH+g+EjBT4axIc/FrpVNt5WvG/IVDuu+ktZak8wIFvoM3kAq3Myb4k3cFaS79GFejXKGu11hUvTzJuhJARHo7O1/fhF7y0SflYq0e+Y3gS1fZy5MO+/ry29iRfbb9Mo4oudKhZsHq4x1akLDySfCc8u3btYsmSJcydO5cPP/yQAQMGMG7cON5+++3ijE8QHhtna2c+af8JYzeP5e9rf9O1cj+8gaQnuI7HvEKrCHvwmJibD+a1NP3moazPz/ymNB97NnPZbgGF3U1h85kIElL16I1G0g1GMvRKsW+GwUj6fT7PMMiZH5Wi4PTMz6MT0knJyOry27yyK/0bl6envyeOQT/B1plgzADH8tD/R6hcgKL0xiMyk483lL2jdLZK/5HiFHUJ1o6C22cBCaydIPWu0hCv6Zh8XyYjLDPhuWdJuonWy1tJcPOziWhcKEScVuKp8XS+Y3hSDWhSgWMhd1hxKIRXVimbjJZ3zt+2MqJIueTId8LTrl072rVrx4IFC1izZg1Lly6lQ4cOVK9enbFjxzJy5Ei8MjehE4SSqolnEyY2nMj8E/P5xLCRL4DUM2cxJCahtn/y6nhM003q4hjhcXtA80FTwtNwuNJoLPI0/NARBi5WpjgeQm8w8t+F26w8HMLOS1FFPjtU0dWW/o3L079RBSq62UJiFPz+HFxRpuOp3VsZlSpM75IWEyAjCbbNUh5aW2jxfFGGnyVoJWyYAhnJyvYF/X9Qdr3e+i6cWpvvhEeWZdLz2FYiO62XUmCbEZ6PbssXM0d3fJsrvVdKgXd71+F0aBynw+J48dfjrHm+JVaaB2/bIYqUS5YCFy3b2dkxevRoRo8ezZUrV1iyZAlff/01M2bMoHv37vz111/FEacgPDZj/cdyJOIIB8IPEOOqxS02g5Tjx7Bv397SoeWiL4amgyZZS9Pv2V7CoIfQo8rnLf+ndH5dM0Jpub98AHSZCW1eybOo92ZsMmuO3mTN0ZtExqeZn29Z1ZWqHvboMhv4aTUqtCoJrVqFVqM8p9Oo0KhUaNU5P9eqVZkPpQGgg7WGGuXss4q4r/4HfzwPSbeVDRS7zVEShUcpOm77KqQnK6M8m95Ukp7GIwp/vXulJ8HGNyDoV+XrKu2h/0/g4AnuNZVRqpD9cDdE2VrgIQx37iCnpIAk5erBY6LxUtqK6POzvYR5dVbJns7Kzlqr5pvnlE1GT968y4cbzvNen3r3Pf52Qqq5SPlpUaRcIjxSF63q1avzzjvvUKlSJaZOncqGDRuKKi5BsBiVpGJOuzkM+nsQJytE0jkWpR/Pk5jwFEPTQRPTqJHh3g1Eb5+D9ESwcoRyfqBSw+hNsOE1OPELbJsJ4UHQR6lzyTAY2X5eGc3ZfTlrNMfNTsfAphV4tllFqrgXw+iZPh12fAD7vlK+9vBTRqA86xTN9Tu9o4y8HFgIf70EWhulzudRRZ5TprCiLyo1Rh2nKptIqjJHG5zKQ+W2cH0PnF6rvPYQpvodTblyqHR5L73O7/YSpCVC8G7l85qlJ+EB8HW15YshDRiz9CjLDtygSSUX+jTMnSDeW6Q8TxQplwiFTnh2797N4sWL+f3331GpVAwePJixY8cWZWyCYDHuNu581O4jlh0eS+dTBsJ2b6Hc669bOqxcshKeYhjhMW8ges+Ulmk6q0LTrB/CGitlisinkVJce3Yd6REX+LniB/xwRlmya9K2ujtDm1fkqTqeRdp8L4fYa/DbWLh1XPm66Rh4+sOiLayWJHj6AyXpOboY/pigJD21exXuerIMx5cpI0b6VGVH7AE/KcnNveoPVhKeU2ug7ZSHjlaZC5bvM7oDoPHMnNKKCH9wnNd2KLuEu1QGj1oPPrYE6lxbGa1Z8N8V3v79NH7ejtT0zLnL+AcbznHk+h1RpFzCFOh/m1u3bjFnzhxq1qxJx44duXLlCvPnz+fWrVv8+OOPtGyZ/9bbgvCka+HdgkbdhwOgu3yT4LCzFo4oJ9loNK+gKtai5VwJT2YRt2+LnM9LEhmNR3Ow/c/cVbmiiznP4OMjqJN0GHd7HS90qMauNzqyfFwLetX3Lr5k59Qa+K69kuxYO8HgX5S+OcWxikySoOc8qP8syAZlZObKtoJfJy0B/hgPf7+sJDvVu8ILe/NOdgD8ngG1FURdyCwefrCH1e8AaL1NU1oPKVo21e/U7FEiehEVxuSuNWlb3Z2UDAMvLD+Wo/O0qUhZkuDLZ0WRckmS7/9xevToQaVKlViwYAH9+vXj/Pnz7N27l9GjR2P3iE3Zvv76aypXroy1tTUtWrTg8ENWxXz55ZfUqlULGxsbfH19efXVV0lNTX2kGAQhL6M7v8EddytUMvywfApphrSHn1TUoq9AyKFcTxvi4iAjAwCNa9FvHGhOeGJikI3GrBduHlQ++jY3PxUSk8wn/16g1dz/ePZfeDr5fY4bq+MkJbNU9wkH253k7e61qORWTIXf6UkQtAIWdVMSh/QEqNgaXtgHdZ4pnnuaqFTK9F2dPsrIx6rhcH1f/s8PPwXfd1CmpyQ1dJ0Fw9Y+uBjYxhlqdlM+P73mobcwjfDoKuS9QguyRngMsbEY0+7z99xogEublc9LUf3OvdQqia+ebYiXozXXopJ467dTyLKcq0i5i58oUi5J8p3waLVafvvtN0JDQ/n444+pVatohjJXr17NlClTmDlzJsePH6dBgwZ069aN27dv53n8ihUrePvtt5k5cybnz59n0aJFrF69mnfeeadI4hGE7NQqNV7tugLgfO4mnx759PHcWJ8OZ9fB0t6wsAksfhpu7M95SGaXZbWzM9J96jIehTpzlRZ6vZJcAcSHK4WykooM78ZsOh3OiEWHaP/pDr7ZeZXoxDTc7a0Y2LEpbpO2QpNRSMhodrwPawKVkYyiIssQdhz+ngzzasP6/ynJmKSGDm/DyL/BOR/9ZIqCWqMUFdfoBvoUWDEYQo89+BxZhsM/wk9dIfYqOFZQaqHavpq/rSvqD1E+nv5NSUQeIGtK6/4Jj9rZGcla2SfrvkvTw45BcjRYOSk7dJdibvZWfP1cYzQqiQ2nw/l866UcRcqTOoki5ZIm3zU8xbX66vPPP2f8+PGMHj0agO+++44NGzawePHiPHv87N+/nzZt2jBsmLL9fOXKlRk6dCiHDuX+DVgQioJb6w7cWreBOjdk3rm4muZezXm6cjH1HrkbAseWwvFflFVF2V36N8cPGUMx9uABMKo14OgE8XFs2XOOYAdPnK9v4jngqlSJgI8Okpye9YO2XQ13hjWvSNc6nmjVmT+wA74C74bKiqPzf0H0JXh2BbhVK3xgKXeUJdnHlynL4U1cKkPjQGgwDBwtsJGxRgeDf1aSneDdsLwfjNoAXv65j02NUwqdz/2pfF2zB/T9pmDL5Gs8pUzZJYQrzR+rdrjvoemmHjwPGOGRJAmtpyfpN26QER6BrmIeq79M01nVu4C69NetNKnkwrRefsz++xwL/rsCIIqUS7BHWqX1qNLT0zl27BhTp041P6dSqejatSsHDhzI85zWrVuzfPlyDh8+TPPmzbl27RobN25kxIi8l4SmpaWRlm14Nj4+vmjfhFDq2Wbuq1UtUsImVWbm/pn4ufnh61BEowdGA1zeqhS+Xt4CZC5jsvdUfoBrbWH7bAjek+M0/SP24ElIzeDW3VTC7iYTdjeVW3dTuHU3hbA7yseI+FS+la2oCPzw5xGCPGowTbMfNLA/vRrJegMeDlYMblqBIU0rKv1u8tJ0NHjWVfaiiroAP3SCAT9mTcnkh9EIN/YqieC5P8E0tai2UqasGgdCpbaW39RTawPProTl/ZXi7mV9YfTGnMW9Ycfht9Fw5zqoNPDUe9DyxYLXw2isoG4/JUE+vea+CY9sMJBxSylE1j2ghgdA4+1N+o0b6CPvs1KrFC5Hf5hRrStz9MYdNpwKF0XKJZxFE57o6GgMBgOenjnnQT09Pblw4UKe5wwbNozo6Gjatm2LLMvo9XpeeOGF+05pzZ07V+zgLjwSracnukqVSL9xg94JVVlrHcwbu95gWY9l6NSPMJWUEAknlsGxnyHuZtbzVTpAs7FQq6fyW3RcqJLwhAdBajxYOwLZ99F6+AotWZY5GRrHykMhnAy9S9jdFBJS9Q897661IxUTbtPM0Ui1xhXoHRICidCsXXe2NepAZTdbNOp8JBm+zeH5Xcq01s1DsGIIdJqWudz6AefHh8PJFUqicyc463nPetB4pLIMvDDNA4uTlT08txZ+DoDwk7CsjzJV5VIZDn0HW2YonZ6dK8LApVChSeHv5T9YSXjO/aUUT2utcx2ij4pSar00GjQPaQ6rNa3Uyqv54J3rSksCSa0UVZcRkiTx6cD6+Jd3om11d1GkXIJZNOEpjJ07dzJnzhy++eYbWrRowZUrV3jllVd4//33mTFjRq7jp06dypQpU8xfx8fH45uffWIEIRvb5s1Jv3GD4amN2GIVy9mYs3xx7Aveav5WwS4ky8p0x9HFcOEfMGYmHTYu0PA5aDIa3O+pDXCqAC5VlB/4IQfMIyP56cGTlKbnz6Bb/HroBmdv5R7ddLLRUt7ZBh9nG8o7W1PeRflc+dqGjNk7SNhwhRf8XXDrVxPmXgSgdvOnwKWA//E7eMHIf+Dft+HoIqVHTngQ9P3WnMQBSmPDK1uVKatLm5XVTwA6ByXBaRyoLH9/klcIWTvB8HWwtBdEnYdlzyhJ2sWNyut+AfDMQqX4+FFUbKXU/sSHKqMvdfvmOsRcv+PtjaR+cOdgTWYvnjxHeEybhVZs9eQlmcXMVqfhhQ6PMA0rPBEsmvC4u7ujVquJvKdALjIy8r7bVMyYMYMRI0Ywbtw4APz9/UlKSmLChAlMmzYN1T2/LVpZWWFlVTJ38hWeHLYtWnB37Vqk42f4YOQHvPTfSyw/v5zmXs3pVLHTwy+QHAsnVyqJTsyVrOd9Wyg9Yur0UaZD7qdKOyXhCd6dO+HJo8vyhYh4fj0YwroTYeYltTqNit7+3vSq701FV1u8nW2wt3rwfwGR5pVa0XArSBmZsPcE50oPf8950eig9+fg01BpVHjhH/gps65HUsGJ5cpqq8RsP3B9WypJTt2+yoadJYWdGwT+CUt6KEXJd0NArVP6ATUfXzQJm0oF9QfB3i+U5fh5JDzp+ejBY6LN/H83zxEe82ah3QsdriBYkkUTHp1OR5MmTdi+fTt9+/YFwGg0sn37diZNmpTnOcnJybmSGnXmby1yUW/IIwiZbJs1AyD1/HnaOTUmsE4gy84tY/q+6ax1XYuPvU/uk2RZ2YLh6GI4+4fSXwVAZ6+ssGk6Ou+C1rxUbq+MeFzPquMx75SeWbScmmFg05lwfj0YwtEbd7JOdbPluRaVGNikAi52BZuCM13bEB2d1XDQt/mj/7BuHAjl6ih1PdGX4Ns2WXU5ALbu0HAoNAoEj5qPdi9LcvCEkX8pW24gQb/vlGSvKPkPVhKey1uUxPqe0ZeMfPTgMTFNeWXcO8KTGpe11L6UdVcWyg6LT2lNmTKFkSNH0rRpU5o3b86XX35JUlKSedVWYGAg5cuXZ+7cuQAEBATw+eef06hRI/OU1owZMwgICDAnPoJQ1LSe5dBVrkz69eskHz3K5A6TOXH7BKejT/Pm7jdZ0n0JWlW2QsZbQfDXpJxN4bz8oelYZVrGyiHXPR7I1IAu/JSySsnGxbxKK1prz9cbz7P26E3uJCt9edQqiafreDK8ZSVaVXUr9IoStVu2HdNvhihP3ttwsLAqNIUJO2HtSGWqDklZ/dM4UPmhqin6pfYW4VQB/ndASRKLYxrOsw54+isr1vLYQT0/PXhMzM0H7x3hubJdGd1zq5F7ylUQSgiLJzxDhgwhKiqKd999l4iICBo2bMi///5rLmQOCQnJMaIzffp0JEli+vTphIWF4eHhQUBAAB9++KGl3oJQRti2aKEkPIcO49C5M5+0/4TBfw/mZNRJFpxYwJQmmbViSdGwcigk3FI2q6w3QPkhVL5J4X/gOXqDW3VlOuzGAfQ1upMcfhs1MPqva4Q4JgPg42TN0OYVGdzMF0/H3AWsBZWj2/LNzOTNtwg7qjt4Kv1yrmxTalweV9+cx624V4/VHwRbT+e5g3p+evCYmIqWDXfuYExNRZXZlydrdZaYzhJKLosnPACTJk267xTWzp07c3yt0WiYOXMmM2fOfAyRCUIW2+bNuLt6NUlHlE7gFRwq8F6b93h156ssObOEZp7NaOfTBtY9ryQ77jVh9L9KLUdRqNwOYq5wbNdfvLxW4qdEpQj5rrUDnWp58FyLSnSs5ZG/VVP5ZN4xPeo2JMcoy8C96xfZ9QFlJVoZWuZcLOoNvO8O6ulh+Z/SUjk5IdnYIKekoI+MRFepklJEfnmLcoCYzhJKMAs3rRCEksMusx9P2vkLGO7eBaBrpa4MrT0UgGl7pxG56wNltEJjA4N+LpJkx2iU2XUpiu9vKj+wbML2kxYVq7ym1vDPOz1ZMro5Xet4FmmyA1kjPIa7d5GNKKujNGIRwBPHtIM6KFtUZJLT09Fn7n6enyktU/NByFa4HHpYmUa1di666UxBsACR8AhCPmk8PNBVrQqyTPKxrG0DXmv6Gn6uftxJu8Obl5ajB+j1mVJb8QjiUjL4ac81Os3bycjFh/kxREl46qhu8ElrZUm4zsOdiu7F1xdE7eKiTMfIoE9T5dg/S3jC1B+sfDy1RimYBzIiIkCWkayt892gMtfSdNNS+prdlC00BKGEEgmPIBSAbXNltVZStq1MrNRWfNbsHeyMMsetrfiuVmulp04hXYpMYNq607Scs50PNpznRkwyDtYaAto0IN2lBgDN0pXGnA/qwVMUJLUadebGpIZUFVQswvodoWjlsYN6RrYl6VI+68e0nvcsTTf136kp6neEkk0kPIJQAHYtlCH95MNHsp40Gqm4dTYzo2MA+CE9jAPhBwt0Xb3ByL9nIhj6w0Ge/mI3vx4KISXDQC1PB+b08+fQO12YGVAXXXVl+wD95eNA8Sc8ABpXF+WeqWqoIEZ4nlh57KCe1YMnj7YJ92Ea4cmIjICYqxBzWdkCo3qXIg1XEB43kfAIQgGY+vGkXbxoruNh7zy4+h890mGAbxdkZKbumUp0SvRDr3cnKZ1vd16lw6c7eWH5MQ5ci0ElQfe6Xqwc35J/J7djWIuK2OoypxIqtwNAf/MSkHfTwaKmsVfurVd7gn3x3094BPfsoG7qwZOf+h0T0wiPPjwia7PQSm2U7tGCUIKJCVlBKACNuzu66tVIv3KVpCNHcKxuDTvmKC/2msfb9fpzcsNQrty9wtt73ub7rt+jVuXuD3UmLI5lB67zZ9At0vRGAFxstQxtXpHnWlaivPN9ui5nFqYaomMAu2LbKT07jVZpmKjXltIl46XJPTuoF2RJuonWPMITCZcy93ir1bPIQxWEx00kPIJQQHbNm5N+5SrJ+3bheHINyEalZqfhMKyBeR3m8eyGZzkUfohZB2bxbqt30aq0ZGROW/28/3qOTsj1yjsyslVlAhr4YK19SPNMO3coVwd9qrL7dWF3Si8IjaTEqqds7Z9UImmsoE5fOP4znF5DepgyyqgtwAiPqduyPvwW3FBGEkX/HaE0EAmPIBSQbfPm3FmxkuTtf0OXSPDwg56fmV+v6lyV2a1n8/aet1l/ZT2h8RHU1Uxi7ZEoIuOV7RM0Kome/t6MbF2JxhVd8l1QCkDlduhTfleuU9wJj0GPOiMcsMGQ8eiNDIXHoP4QJeE59xcZN5V+PPnpwWNi2k/LcDcOY4YBlbefstO7IJRwIuERhAIy1/FEpaM32KEZtBR0tjmO6VGlB+F3jXx16l2O3j7IodTrpCSPxsOhHMOaV+S5FhUpV9hOyFXaoU9dB4DGvZhram6fRaNLBWzQJ6YX772EopG5g7oxNgxDrNKvqSA1PCpHRyRbW+TkZPQpanRidEcoJUTRsiAUkCb+LFZOyp5Vyb7joVxt82sGo8zG0+H0/2Yf762GhOAJGPX2qK0j8KnzI0snVODVp2oWPtkB5IqtlRVTKP0Ni1XIITTWBgD0mavQhCdc5g7qGUnK3xGVgwNqp/wXHCvNB8sBkJGsFvU7QqkhEh5BKIiESPhtLLbllKmp5Ehlw9DEND2L9wbT8bMdvPjrcY6H3EWnVtGvTkvmt19MVaeqxGdEM3brKPaH7X+kEIyyFbJBmQLTJF14tPfzMDcPobFWiqr10Q9fdSY8IfwHk56Z8Gh9vAp8utZJScgz9M7KHnCCUAqIKS1ByC+jAf4YB0m3sa1RnTuXk4k7eIglG8+z4nAICal6AFztdAxvWYkRLSvh4aBsw9C04jJe3fkqRyKO8OL2F5nZaib9avQrVBj6qCgAVBojqvBDwNAieXt5unnYPMJjjI/HmJaGykpsLfHE86xDhsoXSEbnUPDTNTplM1q9TQ3IY5WhIJREYoRHEPJr1ycQvBu0doQPnAeA8eoVVm09RUKqnqoedszp58/+tzsz5ama5mQHwMnKie+6fkfvqr0xyAbe3f8u84/PR87cAqAgDJkjLRobI1zfUzTvLS/xtyAuBJWVBBrldyNDjJjWKikydNUB0BJZsBNlGa1B6d+TIXkWdViCYDEi4RGE/Li2E3nXxwAssJtIt7+SCHb0BmCA+jaLRzVl26sdGNai4n2XluvUOua0ncOE+hMA+PH0j0zdO5V0Q8GKgU1TSxprA8Rcgfjwwr6rB7upbJ8hedU1NzgU01olR0a6IwBa401lB/X8ir6ERlISW32ymAQQSg+R8AjCQ6TEhpGyajQSMiv0nZgX0RCNSiLJrz4AExzv0rm2JyrVw5eWS5LES41e4r3W76GRNGy4toEXtr1AXFpcvuPRRylJh9pF+YFWbKM8Nw8rH31boHFTdn0XCU/JkR6pJC1aO32OHdQf6uImtLbKNGZGlPjzFkoPkfAIwn3cjk9l3r/nODN/EDbpsZw3VuQLzVhe6FCNvW91ptvwXgAkHT70kCvl1q9GP77u8jV2WjuORBwhcFMgYYlh+TrXPMLjlbnUuNgSnsz35dvS3O9HL34AlhimLss6e0OOHdQf6uImNJkJjz68mEYPBcECRMIjCPc4Hx7P62tP0vbjHWj2fkIzzpKMNefbzmfn1B683aM2Xk7W2DZtCkD6lavoC1Hb0rp8a37u/jPlbMtxLe4az214jrPRZx96nqloWVMpczl8cDEkPBkpEH5S+dy3uXkLC32MSHhKAkNcHMaEBAC0juocO6g/UFIMhB42j/AY4uIwpqQUZ6iC8NiIhEcQMt26m8KIRYfo8dUefjsWSgs5iJc06wGw7r+Q/k93ws4qq6ZB4+KCVa1aACQfPlyoe9ZyrcWKniuo5VKLmNQYRm8ezc6bOx94jnmEp1ojkNRwJxjiQgt1//sKOw5GPdh7gXNF8xYWBjGlVSJkhCmjhWo3N1R1cu6g/kCXt4BsROVbF8lWaaaZERFRXGEKwmMlEh5BAK5FJTLouwPsuRyNSoJhdbQsdvwRFTI0GY2q/qA8z7Nt0RyApEImPACedp4s7b6UNj5tSNGn8MqOV1h5YeV9jzcnPD6+4NNQebKoR3nM01nNQZLQuIkprZIk3bRpaIXy2XZQ/11prfAgl5Td0aVaPc1bTOgjC7jKSxCeUCLhEcq8s7fiGPz9AcLuplDVw47/Xm3LHMOXaFNjwNMfun9033PtmisJT/LhI48Ug73OngVdFjCgxgCMspE5h+bw2ZHPMMrGXMfqozOntNzdzbunc33vI90/F1PBcsWWWfdCFC2XFBmhygiPrnyFbDuo33rw3xN9GlzZrnxeq7s54ckIFyM8QukgEh6hTDtyPZZnvz9IdGI69co7svb5VlQ+PR9u7AOdAwz+GbT33wbCtmlTkCTSr14119YUllalZWarmbzc6GUAfj73M6/vep1Ufar5GNlgwBCj7I+kdneHyu2VF67vfqR75yDL2UZ4WgBk1fCIhKdEMBUsa8uXz9pBHR48rXV9L6Qngr0neDfK2jU9UiQ8QukgEh6hzNp58TYjFh0iIU1P88qurBjfEreIPbBHaSrIM1+BW7UHXkPt7IxVbaV4OPnIo43ygLJsfXz98XzU7iO0Ki1bb2xl3JZxxKYqSY7hzh0wGpVpJldXZQRGpVH6rNy58cj3B5TePimxoLYCL2XpvXmERzQeLBHSw0xTWpkr+UzTWuf+gozUvE+69K/ysWZ3UKnECI9Q6oiERyiT/jl1i/HLjpKaYaRTLQ9+HtMcx4wY+GMCIEPTsVBvQL6uZddc2T096VDh63ju1atqL75/6nscdA6cjDrJiI0jiEqOMo+wqN3ckDQasLIHn8bKSUW1PN00ulO+MWh0QFbCIycnY0xKKpr7CMXGNKWlrVBeeSJzB3XS4rMSm+xkGS5mPl+rBwAaL6XLckaEWJoulA4i4RHKnJWHQ3hp5QkyDDIBDXz4fkRTbHRq2DwNkmOUUY1uc/J9PdsWyrRPYVdq3U8zr2Ys77Gc8vblCUkI4e09b5N++zaQlYAARV/Hc890FoDKzg7JRtmaXUxrPdlkWTav0tKZRngyd1AHlJ4897p9DuJCQGMNVToAoPVWOonrI0TRslA6iIRHKFO+33WVqX+cRpbhuRYV+XJIQ3QalZIsnPkNJBX0+fqBdTv3sm3SRKnjCQ4mIzMhKSpVnavybddvsdHYcDjiMNtO/Abck/BUaad8DN6T/+ZyD5Ktw3J2onC5ZDBERyOnpoIkmZMWAPwHKx8vb4Hk2JwnXdyofKzaEXTKcnSNp2mER0xpCaWDSHiEMkGWZT7+9wJzN10A4MWO1figbz3UKgkMGbDxDeXApmPAu36Brq12csLazw949NVaeaniVIUZLWcAcOK8soomR8Lj2xJUWogPVXryPIrkWKVJHShL0rPJSnhEHc+TzLQkXePlhaTTZb3gWQc864ExA879mfOke6azIGuExxgXhzE5uVhjFoTHQSQ8QqlnNMpMX3+Gb3deBeDtHrV5s3ttJClz76sjPylD+jau0Glaoe5ha16eXrTTWiYB1QLoV70fTonKCE66s13WizpbqKB0fX7kfjyhR5WPrtXAzj3HS1kJz6OtRhOKV0bYLQB05cvnfrF+5ihP9mmtxNsQdkz5vGZ389Mqe3tU5uaDYlpLKPlEwiOUahkGI5NXB/HroRAkCeb08+eFDtlWXiXehh2Z9TpdZ4Kta6HuY054DhV8X638mtpiKr7p9gBsijuQs0dPUdXxmOp3MvvvZKd2FxuIlgTmJemm+p3s6g0EJAjZn7WD+qXNgAw+jcDBy3yoJEloTHU8Ymm6UAqIhEewDFl+eNfXR5SSbuD5X47x18lbaFQS859txLAWFXMetG2WsnLFpxE0GlHoe9k2bQIqFek3bpBRTJ1pbTQ2NFBXAiDIeJ2fTv+U9WLlzDqe649Yx5O9w/I9NGJ7iRIhIyxbD557OZXPSo5NO6hfVLorU7NHrsO1pjoesTRdKAVEwiM8fknR8FNXmN+o6HrH3CM+NYORiw/z34XbWGtV/DiyKQENfHIedPMwBP2qfN7zM1CpC30/taNjsdbxmGjvKkvC79rB10FfczQicwrKtzmodZAQDjFXC3dxQ0bW1MY9BcsAGncPQNTwPOnSHzTCAzmntTJS4NoO5etauRMejbdoPiiUHiLhER6vpBj4+RkIOwp3b8BvY0CfXqS3iElMY9iPBzl8PRYHKw3LxrSgU61yOQ8yGmDj68rnjYZn1cA8gqzl6cU3rWWaTmrs1wWjbOSt3W8pTQm1NlAhc1SmsF2XI89ARrKyDYF7rVwvi27LJYN5W4kKeYzwAPg9ozSVjLoAB79R/swdK4CXf65DtZ6i+aBQeoiER3h8kmNh2TNw+6zSvt7aSUl8ts0qslvcupvC4O8PcCYsHjc7HSsntKR5lTzqco7/DOEnwcoJuhTN/W1NDQiLqXDZmJqKMSEBgImdp1HFqQq3U27zzp53lHoe01RFYQuXTcvRKzRX+rbcQ+MmaniedLLBQEa40ijwviM8Ns5QM3MH9Z2Z+8TV7AamIv5sTCM8GWKERygFRMIjPB6mZCfyDNiVg5H/QN/vlNcOfg3n/3nkWwRHJzHouwNcjUrCx8maNS+0ol55p7xj2f6e8nnnaWDv8cj3hsx+PCoVGTdCiqV3iWkqSbKyws61HJ91+AwrtRX7bu1j8ZnFWf14ru8tXB1PHg0Hs8veh0cuin4/QpHTR0SAXg9aLZpy5e5/oGlay5A5uprHdBaA1iuzaFmM8AilgEh4/t/efcc3Vb0PHP/crO69WzrYIHsLqICUpfD9CYioKBURBAFRlqJMUVEERJEhyBBlqSjiAhmCimxk79kCnZTukXV/f6QJVLqbNh3n/Xr1RUhu7j0NoXl6znOeRyh7mXdgzf9BzElw8oEXfgafetDgMWg/2nTMj6/AnWslvsTpW8kMWPqPqeO5txPfjuxAbR/nvA/eNcs0Jt9GphYSVqJ0ccG+USOgbLanG+7pki5JEvU86jG57WQAPvv3M45q1KZKuelxkHCh+BeIzD9hGXKalQLodBiTk4t/fqHMac0tJQIDkJQF5KTV7W6aYQVQO91Nev8Ptbm9RBkl4gtCeRIBj1C2MpNgzRMQcwIcvSHiJ/C5Jz8kfAbUaANZyfDtkBLl8xy+lsjTy0wdzx8IcOWbEe0JcnfI++Bbx+DwKtPtxz4CparY1ytIWS5r6eLvBjxm/er247Gaj2GQDUza+zZJwa1MD1wtZh5P8g1T4UJJCUGt8jxEYWeHwtUVEE1EKypLS4mgfJazzO7toF67S76VxVWi+KBQhYiARyg7mUnwVV+IPgaOXqZgx7dh7mOUanhyJdi7w62jsH1asS7x0/FbPLfiAKlZetqEebB++IN4O9vlfbDRmFNRWYYmAyCsYwm+qYI5mROXrdhI1My8HVzpczfgkSSJae2nEeYaRmxGLG87GDFC8RuJmvN3/BubGpLmw7KsFS/yeCqiAmvw/NejU6DtcOj2Tr6HKJ2dUTiZilyKFhNCZScCHqFsZKXA1/1NQYyDJwzeYiptnxf3EOibk89zYAmc2VLo6Y1GmbnbzjNm/b+WjudrXmyHm4M6/yed2AA3DoLGGbrNKsE3VTiHli1BqUQXFYXu1i2rntscZORqKwE4qZ2Y22kuGoWGPzNvssbVpfh5PPn0z/ovkbhcsVlq8BQl4HH2Nc1yetUu8DDL1nQR8AiVnAh4BOszBzs3D4ODBwz+0TRzUJD6vaDDGNPtH0dDYv49odKy9bz89RE+++MSAMMfqcUXEW1MHc/zHVPy3dmjTpPANSD/Y0tB6exsyeOx9rKWOcgw18O5V33P+rzR9g0APvF057ghFeLOFv3kUftNfxYW8PiI9hIVmSWHJyiwkCOLTmxNF6qKChHwLFq0iLCwMOzt7WnXrh0HC/mgSEpKYtSoUQQEBGBnZ0e9evX49ddfy2m0QoGyU2Htk6aZFHt3U7BT1GacXaebPnCzk+HbF0Cffd8hkbcz6Ld4L9vPxKJRKZj/VDPeeqyhqQloQXZ/AOnx4FUX2o0s9rdVHE7tzH21rFuA8G7A453n4wPqDaBHWA/0ksREX2+SL28v2om16RB9wnQ7n4RlM3PiskHk8FRI5iUtTVFmeIpIbE0XqgqbBzwbN25k3LhxTJ8+naNHj9KsWTN69OhBXFxcnsdrtVq6devGtWvX+O677zh//jzLly8nKK8y6kL5yk6DtQNM25vt3WDwZghoVvTnm/N5HDxMeT+/T8n18D+XE/jfor+5EJuGr4sdG4c/SL+WRfjBHnsGDnxuut3rQ1BpCj6+lMqqkagl4PHJO+CRJIkZ7WcQrHIhWqViyuVvirZ9/Na/IBvAJRDcggs81FJtWeTwVDhGrRZ9zs/NIi1pFZF5hkdsTRcqO5sHPPPnz2fYsGEMGTKEBx54gKVLl+Lo6MjKlSvzPH7lypUkJiayefNmOnbsSFhYGJ06daJZs2J8sArWp02HdU9B5D5TMb/nfzD1pyoutxrQd5np9sFlcHozsizz1b5rPL/iIEkZOprVcGPL6IdoEeJR+PlkGX6bZPpAb9gH6nQt/piKydGcx3PjhmXXjDXoE+7fpfVfzhpn5rZ4HbUss9uYwtdnvir8xPf2z8qj+Ny9RA5PxaW7eRNkGcnBAaVnyZrg5kUtZniEKsKmAY9Wq+XIkSOEh4db7lMoFISHh7Nv3748n7Nlyxbat2/PqFGj8PPzo3Hjxrz//vsYDHk3oszOziYlJSXXl2Bl2nRYNxCu7wU7V1Owk8/W5iKp1x06vgaAvGU08zZuY+qPpzEYZZ5oHsjGl9vj75b3Ntr7nNpk2rGksoce75d8TMWgcHLCobEpZyl15y6rnFOWZQzmpGWfggslPlD/CSYkmbYQzz8yn1MJpwo+eWTBBQfvVRbtJdL++otL3Xtw+4svCj9YyNe9LSWkQgLX4lCJGR6hirBpwJOQkIDBYMAvpyOvmZ+fHzH57Ai4cuUK3333HQaDgV9//ZWpU6cyb9483n333TyPnz17Nm5ubpav4OCCp+yFYtJmmIKda3+BxgWe+x5qlCLYMXt0CrqgtkjZqfQ88wZ2kpY3ezXg44HNsVcXsclndtrdZbGHx5t2g5UT53DTTFLsnDmk7thR6vMZU1KQdTrgngKA+VGqecarOeHpGehlAxP2TCBFm0+gbzSa8q2gaAGPeVv6besEPJmnT3Nj7GvoIiOJmzuPO+vXW+W81ZF5NlFdWA2eYro7w1O9iw9mG7JNLVyESsvmS1rFZTQa8fX1ZdmyZbRq1YqBAwfy9ttvs3Tp0jyPnzx5MsnJyZavqKioch5xFabLhA3P5AQ7zvDcJghuY5VTn4nNZEDCMBJlZxorrrGz0e+M6FS7eL+5/vmRqXu4Rxh0eNUq4yoqryFDcO3dG/R6brz2Oinbi5hAnA99TtFBhZsbCk3hOUhSzUeYmXCbINTcTLvJ9L3T887nuX3JVHVa5VCk5PK7ScuJyPnMqhaV7tYtbowYiZyRgcrf9KEa884sUn7/vVTnra6KtSW9GFQ57SWMKSkY09Oteu7K4nziedqva89Hhz6y9VCEUrBpwOPt7Y1SqST2P785xMbG4p/zA/C/AgICqFevHsp7yqY3bNiQmJgYtNr7q/Ta2dnh6uqa60uwAl0mrH8Gruw2laYf9B2EFD5DUBS/nYym/5J/OJbsxAf2rwNQ49I60/JUUSVchH2LTLd7fpBvJdmyIqlUBH4w2xL03Hx9XKk+yAvboXWfsIdxNcrMTbiDSqFiR+QO1p/LY/bEnL8T1NKUNF4IlaenKc/HaMRw505Rh38fQ2oqUS+PQB8fj13dutT6aQvuTz0FssytCRPLrAFrVaa1FB207gYOpbMTCmdTMcrqOsvz+/Xf0Rl1/HzlZzHLU4nZNODRaDS0atWKnTt3Wu4zGo3s3LmT9u3b5/mcjh07cunSJYzGu2+6CxcuEBAQgKYIv/kKVqDLgg2D4MofpmDnue8gNO9/r+IwGmUW7LjAyLVHydQZeLiuN2+PHQsPjTMdsGUs3L5c+InMicpGnalnUL2epR5bSUgqFYEffoBrnz53g55tJQt68is6mK+AZqBxoXFqIuPqDARg7uG5nLl9Jvdxlvo7BW9HN5NUKpQepmTxkubxyDodN8eOJfviRVQ+PgQv+xyliwv+06fhHN4VWavlxqjRZJ0vQT+wakxnqcFj/R2rlmWtnE7s1c2R2CMAJGUncfHORRuPRigpmy9pjRs3juXLl/Pll19y9uxZRo4cSXp6OkOGDAFg8ODBTJ482XL8yJEjSUxMZOzYsVy4cIFffvmF999/n1GjRtnqW6he9Nmw8Tm4vBPUjjDoGwjtUOrTpmfrGbXuKAt2mH6YvNixJqteaIOboxq6vA0hHUCbCt9GmAKugpz7BS7vAqXGNLtjxQTO4pKUStNMz//6gMHAzXHjSNm6rdjnKfYMj1Jl+Xd5zuhIl+Au6Iw6JuyZQJo27e5xRaywfK/StJeQZZnoGTNI/2cfkqMjwZ8vRZ3Tr0lSKgmaOxeHVq0wpqYSNWyYVXe5VXVlUYPHzJK4XA2rLWsNWk7Gn7T8/XDsYRuORigN63ZOLIGBAwcSHx/PtGnTiImJoXnz5mzdutWSyBwZGYlCcTcuCw4OZtu2bbz++us0bdqUoKAgxo4dyxtvvGGrb6H60GfDxufh0nZTzsezGyHsoVKfNioxg2FrDnMuJhWNUsG7fRvzVOt7ksuVKnhyBSx92NRxfdtk6P1x3ifTZcLWnAC5w5hCy+aXB0mpJHD2bCRJIvnHLdwcPx6Qce1Z9JmnomxJv0/Nh+HiNqRrfzPryeU89dNTRKVGMWLHCJr7NMdFUuGafQsXJ0dclQZc4v7FRe2Ci8YFVztX7JX2eeZMqby9yb5woUSJy7eXLiV50/egUFDj4/nYP5C73YjC3p7gxYu4/txzZF+8ROSw4YSu/RqVRxFKEFRjxvR0yxKjtXN44J4ZnmoY8Jy+fRqt8W66xKGYQwxqOMiGIxJKyuYBD8Do0aMZPXp0no/t3r37vvvat2/P/v37y3hUwn22ToaL20xbvJ/dADUfKfUpD15NZMTXR0hM1+LtbMfnz7ekVWgeNURcA6HfMlPLisMrIbQjNHny/uP+XgDJkeAaZNqZVUFISiUB778PkoLkzZu5OX4CyDKuvXoV6fmGQooO5skcjEbuw03lxEedPiLitwiOxx/nePxx02PeOa/13sn3PV2lUOGqccVF45IrEOrKLcKAfad+5YFHWxDqGlqk4SRv2UL8J58C4D9tKs6dOuV5nNLNjeBly7j2zLNor1zhxoiRhKxehcLBoejfezVjbimhcHND6eJi9fNX5xke83JWkHMQN9Nucjj2MEbZiEKy+QKJUEziX0womvQE+DeniN2AL6FW51Kfct2BSJ5dvp/EdC2Ng1zZMrpj3sGOWZ2ud4OYn8ZCwqXcjydehb9zZn56vAcap1KP0ZokpZKA997FrW9f0/LWhImkFLElinn5qNAt6ffyb2qqeJ2dAjHHaerTlK8f/5pXW7zKkEZD6O9Uk27pGTyocqeRVyNCXEJwt3NHKZk2BOiNehKzErmecp1Tt0+xL3of265t44ThOgCnzv/NMz8/w/WU64UOJf3gQW69bSoR4Dn0RTyefrrA49UBAYR8sRyFmxuZx49z8/VxyHp90b/3asa8Q0tTRhXn787wVL+kZXPA83T9p3FUOZKcnSzyeCqpCjHDI1QCR78EgxYCW0L90iUBy7LMrJ/PsnKvqUFo76YBfPRks4Kbf5p1ngyR++H636Z8npd2gDrnN/9tb4Mh2zTz9MATpRpjWZGUSgLenQWSRPL333NzwkRkWcbt8ccLfN7dthIFFx3MRaE0zYSd/xWu/gVBrWjk1YhGXqbmppzcCXEJ0G4atIqwPE2WZTL1maRoU0jVplr+NN92i9oDB/4iWOdCqi6VsbvGsvbxtTip8w4wsy9f5sboMaDT4dKjB77jizbzZlenDsFLFhM55EXSdu8mesYMAmbNsmpRvarCkrBcBstZcO8MT/VKWjYYDRyLOwZA24C2HIg5wN83/+ZQzCHqe9a37eCEYhMzPELhDHo4lNPqo+3wUp/uo23nLcHOxB71WfhMi6IFO2DK5+n/BTh6Q+wp+C0nd+vidjj/CyhU0OsjmyYqF8Yc9Lj16wdGI7cmTiL5518KfE5BndILFPaw6c9rf+W+36CDm6bfXAl5MPf4JAlHtSP+Tv7U9ahLK79WdA7uTJ/afRjUcBAPN+sDQGtNHXwcfLicfJmpe6fmWedHn5BA1PCXMaak4NC8OYEffoCkKPqPHceWLQmaPw8UCpK/20T8p58W/XuvRsqqBo9ZdZ3huZh0kTRdGk5qJ+p71KeNv6nO2MEYUTahMhIBj1C4C79Byg1w9IJGfUt1qi/+usLi3aat5bP7NWFUlzrF/43dNcAU9CCZZp7+/fpu4NNuBPg2KNUYy4OkUJiCnv45Qc+kSST/9HOex8o6HYbERKCYOTxwN4/n+j5TkGMWcxL0maaO9l51i3VKc+K0lJjM/M7zUSlUbL++nZWncve/M2ZmEvXKKHQ3b6IODaHGksUo7ItfD8mla1f8Z0wH4PaSpSSuW1fsc1R1WssMT9ksaZlneIypqRjSqk/xQfNyVnOf5igVStr4tbHcL+rxVD4i4BEKdzCnmWfLwaUq4LfpyA3e/eUsYJrZeaZtKVo91O4CnSaZbv84ChIvg5MvdKo8u/UkhYKAWbNwH/CkKeh54w2St2y57zh9TrCDUonS3b14F/FrbOo+r0uHW8fu3m/Zjt4WijHjAqDMaSBqiE+guW9z3mr3FgCfHP2EvTf3AiAbDNycOJGsEydQursT8vnnpdpp5fHUU3iPMW1siJ31bonrGVVVli3pZZTDo3R2QpGTDK2vRk1EzQFPS7+WADT0aoiT2okUbQoX7og6UZWNCHiEgsWfh6t/gqSA1i+W+DQ7zsQyadMJAF56qCavdLbCdvFOb9xdsgHoPgvsK1clbUmhwH/mTNwHDDAFPW9Ovi/osRQd9PIq1nIQYApmQjuabl/78+79xSw4eC9zHpEhORlZq2VAvQH0r9sfGZlJf04iKjWKuDlzSNuxE0mjocbiRWjCwop9nf/yfuUV3AcOzKnGPEFUY84hy7Il4CmrJS0Atb+5+GD1CHhkWeZo7FEAWvmZ+gOqFCpa+pqCn4PR4v1X2YiARyjYweWmP+s/VuLmmwevJjJq3VEMRpl+LYN467GG1kk8VSih/wrTLEbD/0HTgaU/pw2Ygp4ZptYKRiO33niT5B9/tDxeoho89zKXD7h6Tx5PCQoOmind3EBl2u9gnn16q91bNPVuSoo2hQ2znifxyzUABH4wG8eWLUs27v+QJMm0nT28K7JOJ6ox5zAkJVl6XJVFlWUzc7+z6jLDE5kaye2s26gVahp7N7bcb87jORR7yFZDE0pIBDxC/rJS4HhO/6W2w0p0ijO3Uhi6+hDZeiPhDX35sH9TFAorJhS7+MHIvTDwqwqdqFwYSaHAf8b0uzMYb04m6YfNwN0aPMri5u+YmfN4og6AXgvJNyDlJkhKCCp+Z3tJoUCVs6xlnn3SKDXM7zyfLteceGyLKbHVZ9w4XB97rGRjzu/a5mrMrUU1ZjPzDi2lj3eJcqSKqrrN8JiXs5p4N8FOaWe53xzwHIk9gsFYuga6QvkSAY+Qv+MbQJsG3vWgZt5F4gpy/XY6g1ceJDVbT9swTz57tiVqpXjL5UdSKPCfPg33p01BT/Rbb5H0/Q/FbyvxXz4NTQnnugy4dfRuw1D/JiWuVWQJeHJmnwBcr8Qx4vtMFMD2FhK/diib3nYKe3uCFy3Crm4d9HFxRL40DH0pGplWdndr8JTdchaAyt9U/b66zPCYAx7zcpZZA88GOKudSdWmijyeSkZ8+gh5k2U4lLOc1WZYsWdP4lKyeG7FARLSsmkY4MryiNbYq4u49bwakxQK/KdNw/2Zp01Bz9tvk/yjKaen2FvSzRSKu7M8V/+CyJyApwTLWWbm2SZzMKa9cZOoka8gZWtJaVGbFd0VzD/6MQeiD5T4GgVe382N4OXLUQUEoL16lRsjRmLMzCyTa1V05ZG/A6D2N/U8qy4zPOb8HXPCsplKobLcJ7anVy4i4BHydnUPJFwAjTM0K7gq7n8lZ+gYvPIgUYmZhHo58uWLbXBzUJfRQKsec9Dj8ewzIMtor5pqFhWr6OB/Werx/Hl3hqcECctm5tkmQ0IChuRkol5+GUNCAnYNGtBq+QZ61/0/DLKBiXsmEp1WNsXq1P7+hCxfVu2rMWtvlu2WdDPzDI+uGhQfjE2P5UbaDRSSguY+ze973Lw9/XCMaCRamYiAR8ibOVm52TPF2vmUqTUw9MtDnItJxcfFjq9ebIevS9nlFVRVkiThN3UqHs8+a7lP5e1V8hOaE5ejDppq8MB9BQeLwzzbpIuO4caYV9FevozKz4/gz5eicnZm6oNTaejZkDvZd3ht92tk6QvpcF9CpmrMS5Ds7EzVmKdPz7MAoi3Jej03xozhxuuvIxusn/NhzuEpiy7p9zJ3tddXg+KDR+NMszv1PerjrHG+7/E2ASKPpzISAY9wv6RIUzsCKFayss5gZNS6oxy+fgcXexVrXmxLiJdjGQ2y6jMFPVPwfmUkDs2b4/RgyQMUvOuZ6hTps0A2mJqrupX8A9I8w5O0aRMZBw+icHIieNnnqP1MswD2KnsWdFmAh50HZ26fYdb+WWUWiDi2bEHQx/NN1Zg3fV/hqjGn/v47qdt3kPrbVlJ+zru4ZGmU25JWzr+tMS0NQ1pamV7L1vLL3zFr4JGTx6NL5dydc+U5NKEURMAj3O/wSpCNplkBn6L1izEaZSZ9d4Jd5+KwVytY+UIbGgZUrpo4FZEkSfi8+iphG9YXv+hg7hPdzeOBUi1nwT2zTQYDKJUELViAff3c75VA50A+6vQRCknBlstb2HB+Q6muWRCXRx/Ff+YMwFSNuaIUJpRlmdsr7lagjv/kU4xarfXObzRadqmV5ZZ0AIWTEwpX0//pqt413TzD89/8HTOlQmkJhsSyVuUhAh4hN10WHDXVUClq3yxZlnn3l7P88O9NlAqJxYNa0iasgK7ngm3UvKdIYykSlgFUvr6W2/4zpuP88EN5HtcuoB3jWo0DYM7BOZbfnMuCx4ABeA41FcdM+OwzZKPtS/9nHDxE1unTSHZ2qHx80N26RdL69VY7vz4+AVmrBYXCsm28LJlneapy4vK93dBb+LbI9zhLPZ4YUY+nshABj5Db6R8g4za41oB6vYr0lMW7L1uagc4d0JRHG/iV5QiFkgp75O7tUgY8Ds2a4f7UU/jPmI7HgAEFHjv4gcH0qtkLvaxn3O5xxKaXXQ6I9/DhKJycyL54kbTde8rsOkWVuNI0u+PWr6+lNUbCkqUYUlOtcn5L01B/fyR12W8MUAVU/eKD/8b9C0CYaxjeDvmXgri3Ho/eWP2S5SsjEfAIuZn7ZrV50dSZvBBrD1zno23nAZjW+wH6tijbPAKhFLxqm5q/1ukG/k1LdSpJpSLgnZl4PF34Dj5JkpjZYSb1POqRmJXIuN3j0Bqst6xzL6Wbm2l3G3D7889tmsCcfekSaXv2gCTh9cILuPfrh6ZmTQxJSdxeubLwExRBeeXvmFWHren/bSeRn/oe9XHRuJCmS+N84vnyGJpQSiLgEe66ccRUmE6pgZYRhR7+68lopmw+BcCYR+vw4kM1y3qEQmlIEgxYDc99V6Rg1pocVA4s6LIAV40rJxJO8P6B98vsWp4REUh2dmQeP07GQdstN9xetQoAl/BwNKGhSCoVPq+/BkDi6i/RxcWV+hracg54LFvTq/AMz38bhubn3jwesaxVOYiAR7jLPLvTqB84FVzV9++LCYzd8C+yDM+2C2Fct3rlMEChMgt2CeajR0xJzJsubuLbC9+WyXVU3t649+8HmGZ5bEEXF0fKlp8A8HxxiOV+l27dsG/WFDkzk4QlS0p/nXKqwWNmnuHRV9EZngxdBmdunwEKn+GBu/V4RAHCykEEPIJJegKc/t50u5Bk5eNRSQz/6jA6g8zjTQKY9X+NrdMMVKjyOgR1YEyLMQC8f+B9jsUdK5PreL44FJRK0v/5h8yTp8rkGgW5s3Ydsk6HQ8uWOLa4m/gqSRK+48cDkPTNt2ivXSvVdcqrBo+ZuorP8JxMOIle1uPn6EegU2Chx5vzeI7GHRV5PJWACHgEk6NfgkELgS2hRv6/2VyKS+OFVQfJ0Bp4qI438wc2Q2nNZqBClTe08VC6hXZDb9Qzfvd4EjITrH4NTY0g3Hr3BuD2smVWP39BjOnp3Nlg2oLvdc/sjplT27Y4dXoEDAbiPvmkVNcq7xweVRWf4bm3nURRfomr51EPF40L6bp0ziWKejwVnQh4BDDo4VBOEmUBszu3kjJ5fsUB7mToaBbszufPt8JOJfpjCcUjSRKzOs6itltt4jLjGL97PDqDzurX8Rr2EgCp27eTfemS1c+fn6RN32NMTkYTGopzly55HuM7bhxIEqm/bSXz5MkSXUfW69Hl1MMp6xo8ZuYZHmN6utV2mlUkloKDvoUvZ4Epj6e1X2tALGtVBiLgEeDCb5Byw9RRu1HfPA+JS83iuS8OEJ2cRW0fJ1a90AYnu/JNfBWqDie1E588+gkuaheOxh1lzqE5Vr+GXZ06uHQLB+D28i+sfv68yHo9iV9+CYDnkCFIyrx/IbCvXx+3//UBIG7e/BLtJtPFxIDBgKTRlK7PWjEoHB1RuLkBVa/4oM6o40TCCaBo+Ttmoh5P5SE+sYS7ycotB4P6/r5XielanvviAFcS0glyd+Croe3wdNKU8yCFqibUNZQPHvmA0TtHs+H8Bo7GHcVOaYdaoUatUKNSqu7eVuRxW6nO83FvB2+6BHdBrVTjNXw4qdt3kPzzz3iPGYOmjJN7U3//Hd3Nmyg9PHB74v8KPNZ7zKuk/PobGfv3k773H5wf6lisa1mWswIDkRTl97ur2s+P7ORkdDEx2NWtW27XLWtnb58lU5+Jm50btdxrFfl5ljyeWFMej0ohPlYrKvEvU93Fn4erf4KkgNYv3vdwcoaO5744wIXYNPxd7Vk/7EEC3R1sMFChKnqkxiOMbjGahf8u5MKdC1Y776PBjzKv8zwcmjTBqUN70v/ZR+LKlfhPm2q1a/zXvW0kPAYNQmFfcNNcTY0gPJ59hsQv1xA3bx5OHdoXK3Ap7/wdM1WAP9kXLliW06oKc/5OC98WKKSi/zvU86iHq8aVFG0KZ2+fpYlPk7IaolBKIuCp7sxd0es/Bu4huR5KzdIxeNVBzkSn4O1sx9ph7UQzUMHqhjcdTufgzsRnxKMz6tAb9eiMOtOXQXf/fea/G3T33ac1aNkdtZtdUbuY/s90ZnWchdfwl0n/Zx9Jmzbh/cpIS+NTa7u3jYTHoGcLfwLgNWIESd9tIvvsWVJ+/Q233o8X+Xp3a/CUT/6Omdovp9pyFUtcNufvmHNyikohKWjt15pdUbs4GHNQBDwVmAh4qrOsFDie09fnP13R07P1DFl1iONRSXg4qln7Ujtq+zjbYJBCdVDPox71PKxTy+mPyD94fffrbLm8BReNC5PaTsKhWTMyjx8n8cs1+I4fZ5Xr/Ne9bSRUHh5Feo7KwwOvl4YS/8mnxH/yCa7duyFpirZcrLt5Cyi/Lelm6pz2ElVpa7pRNt5tGOpbcMHBvLTxb8OuqF0cij3E0CZDrT08wUpE0nJ1dnwDaNPAux7U7GS5O0tn4KUvD3P4+h1c7VV8NbQd9f1dbDhQQSi6LiFdmNVxFgBrz65l6YmleL1s2n14Z906DCkpVr9mrjYSEYVXKb+XZ0QESm9vdFFR3Pmm6MUYbbakVQW3pl9OukyKNgUHlQMNvBoU+/nmPJ5/Y/9FZ7T+jkPBOkTAU13JMhzKWc5qM8zUdgDI1ht4+asj7LtyG2c7FV++2JbGQW42HKggFF+f2n2Y3HYyAEuOL+FH35vY1a1rqpGzbp3Vr5erjURYWLGeq3B0xGf0KAASFi/GkJZepOdZAp6gcp7hsRQfLLsmsOXNvJzVzKcZakXxm7DW9aiLm50bGfq7lZqFikcEPNXV1T2QcAE0ztDM1ABSZzAyet2/7LkQj4NayaohbWgRUrSpeUGoaJ5t+Cyjm5s6lH94ZA5X/8+0VJH45RqMmZlWu05+bSSKw71/fzShoRgSE0nMCZ4KYszKQh8fD5R/Do/K35zDE23T5qzWdG/BwZIw5/GA2J5ekYmAp7oyJys3ewbsXdEbjLy24Rjbz8Rip1KwIqI1bcI8bTtGQSil4U2HM/iBwQBMUH2PPsAbw507JH37ndWucefrtaY2Ei1a5GojURySWn23seiqVegTCq4+rbtlyt9RODqidHcv0TV1Bh27IneRqi1eAUF1TsBjzMjAmJZWomtXJLIscySueAUH82Je1jocc9gq4xKsTwQ81VFSJJz/1XS77TCMRplJ353gl5PRqJUSnz/fig51ymYniyCUJ0mSmNB6An3r9EUvyXzZPBmA2ytXImu1pT5/rjYSQ+8v61AcLj16YN+4McaMDBKWLC3w2Hvzd0rSx06WZd78603G/jGWiK0RJGUlFfm5CgcHlDnFB3XR0cW+dkVzM+0mcRlxqBSqUu2wurevlsjjqZhEwFMdHV4JshFqdsLoVY+3fjjJ9//eRKWQWPRsSzrX97X1CAXBaiRJYnr76XQL7cauxkbuOEvoY2JI/unnUp87adP3GFNSCmwjUZxx+k4wNRa98803aCMj8z1WW8qE5Y3nN/L79d8BuHjnIsO3DydFW/RkbsuyVhWoxWPO32nk1QgHVclrjNVxr4O7nTuZ+kxOJ5y21vAEKxIBT3Wjy4IjptL3cpuXmPnTaTYcikIhwYKnm9O9kb+NBygI1qdUKPng4Q9oE9KRn9qaZkSiP1+EbDCU+Jy520i8kG8bieJwevBBnDp2BJ2O+E8+zfc4c5f0kuTvnL592tLK47mGz+Fp78nZxLOM3D6SNG3RlqjMy1pVofigZTt6CfN3zO7N4zkcK5a1KiIR8FQ3p3+AzERk1xp8cLUmX+67jiTB3AHN6N000NajE4Qyo1Fq+Ljzx8SFNyPNHoi8xdUf15f4fLnbSDxhtXGa6wSl/PILmafzninQ3TQFPMWtwZOqTWXC7gnojDq6BHdhUptJLOu2DDc7N04knGDUzlFk6DIKPU9VmuExJyyXJn/HzLysdTBaNBKtiETAU93k9M362/1/fP6Xacr8/b5N6NeyfLe2CoItOKodmf/4Eg4+ZMpRu7xwDjFpxf/QLm4bieKwf+ABXHv3BiB+/sd5HlOSGjyyLDP9n+ncSLtBkHMQszrOQpIk6nvWZ1m3ZZZGrq/uepUsfVaB57IUH4yp3FvTEzITuJZyDQmJ5r7NS30+c8BzLP4YOoPI46loRMBTndw4AreOYpDUjL3QFIAZfR7gmbYhhTxREKoONzs3Bry9mmyNRI1oHfMWPcedrDvFOkdJ2kgUh8/YV0GtJn3vXtL37bvv8ZLU4Fl3bh3br29HpVDx0SMf4WZ3t77WA14PsLTbUhxVjhyIOcBru19Da8g/qVtlbi8RU7mTls2zO+Y6OqVV2702HnYepjye2yKPp6IRAU91kjO7s1nfjkRcmdyrAS90rGnjQQlC+fMNqI3LgP4APLjjJiN3FD1/BUrWRqI4NMHBeAwcCEDc3HnIRqPlMUNaGoZk024zdVDRcnhOJ5xm7uG5AIxvNT7P3UhNfZqyOHwxDioH9t7cy/g94/OdpagqMzylaSeRF4WkoLW/KY/nYIxY1qpoKkTAs2jRIsLCwrC3t6ddu3YcPFi0N8qGDRuQJIknrLh+XmWlxWM4uQmANfruvB5ej5c71bbxoATBdkKGjwa1igeiQH/sFGN2jSl0KQdK10aiOLxHjkDh6EjW6dOkbttmud88u6N0d0fp7FToeVK0KYzfMx69UU94SDiDGg7K99hWfq1Y+OhC7JR27I7azRt/vYHeqL/vOJVfTrXlmJhKXXzQkr/jV/r8HTPzspYoQFjx2Dzg2bhxI+PGjWP69OkcPXqUZs2a0aNHD+Li4gp83rVr15gwYQIPP/xwOY20cjvx00KUso5jxlp07NSDV7vWsfWQBMGm1H5+uPftB8CT+yUOxx5mwp4JhdZQKU0bieJQeXnh+aKptk/cggXIOtO4ipO/I8sy0/ZO42baTYKcg5jZcWahdXvaBbRjQZcFqBVqtl/fztt/v43BmHs3m3mXlpyRgTG1eIULK4pUbSrnEs8Bpd+hda82fjl5PHEij6eisXnAM3/+fIYNG8aQIUN44IEHWLp0KY6OjqzMmTLOi8FgYNCgQcycOZNatWqV42grpy1Hr+N17msArtUaxMQe9UtUrEwQqhqvl4aCQkGzS3rqxqvYc2MPU/6eglE25nm8NdpIFIfnCy+g9PJCdz2SpO9M1aGLU4Nn7dm17IzciVqhZl6nebhqXIt03YeCHmJep3moJBW/Xv2VGftm5HpNFA4OlgrPukraRPRY3DFkZIJdgvF1tF7tsdrutfG09yTLkMWp26esdl6h9Gwa8Gi1Wo4cOUJ4eLjlPoVCQXh4OPvySNQze+edd/D19WXo0KGFXiM7O5uUlJRcX9VFcoaOeb+f57dNqwmSbpOudOf/Bo0SwY4g5NCEhODaqxcAUy82snzAv3/g/TyXaqzRRqI4lM5OeI8cCUD8osUY09MtNXg0hdTgORl/knlH5gEwofUEGnk3Kta1u4R04cNHPkQhKdh8aTPv7X8v12ti2ZoeWzkDHmvn75hJkmSpxyO2p1csNg14EhISMBgM+OWsB5v5+fkRk099h7///psVK1awfPnyIl1j9uzZuLm5Wb6Cg4NLPe6KLiVLxyc7LvLQnF0s3HWJ5xSm9X/HB4cgqUteSVQQqiKv4cMAsP/zKHNqvoaExMbzG1n478Jcx1mzjURxeDw1AHVwMIaEBBLXrLHU4Clohic5O5kJeyagN+rpFtqNZxo8U6Jrdw/rznsPvYeExDcXvmHOoTmWoMdSfLCSzvCYKyxbM3/HzJLHEyvyeCoSmy9pFUdqairPP/88y5cvx9u7aL2eJk+eTHJysuUrKiqqjEdpO2nZehb9cYmHP/yDj3dcIDVLTzfvO3RUnkaWFEhtyu+HtCBUFvb16+PcuTPIMo22XWTKg1MAWH5yOUuOL+FG6g30Rj1JmzZZrY1EcUgaDT5jxwJw+4sVZJ035Z3ktyVdlmWm7p3KrfRbBLsEM7ND4Xk7BeldqzczO8wE4OuzX7Pg6AJkWUblb/pFtTLO8GQbsjmVYFpuKouAp61/WwCOxx0vcHu/UL5Utry4t7c3SqWS2NjcWxtjY2Px97+/xcHly5e5du0affr0sdxnzNmuqVKpOH/+PLVr5955ZGdnh52dXRmMvuLI0Or5at91Pv/zConppv9cdXydmdDJnx6Hh0MaSPV6gbuotyMIefF6eThpu3eT/OMW+o4eTUrLsXxy9BMWH1vM4mOL0chKPl1qwBM40MmPvefWEuwSTLBLMDVcamCvsl7hwby4PtaL2ytXkH3mLMb0dCD/thJrzqzhj6g/UCvUzO00FxeNS6mv37duX3RGHbP2z2LlqZXYKe14yj8AqJwzPCfjT6Iz6vB28CbYxfqz/jXdauJp70liViInE06WSVAlFJ9NAx6NRkOrVq3YuXOnZWu50Whk586djB49+r7jGzRowMmTJ3PdN2XKFFJTU/nkk09sv1z113xwC4amA8rlclk6A1/vv87SPZdJSDMFOjW9nRjbtS59GrqhXNsfoo+Boxd0m1kuYxKEysixRQsc27Yl4+BBElet4qXJk1Er1Gy6uImbqTdpdSoLzyQjKQ4w1/cIusNHcz3f18GXGi41CHENsQRC5i9rFLSTFAp8x40n6qWXLPflVYPnePxxFhxZAMCkNpN4wOuBUl/b7Kn6T5FtyGbOoTksOb6EIH049QBdJSw+aM7faeXXqkxyGiVJoo1/G7Zd28ahmEMi4KkgbBrwAIwbN46IiAhat25N27ZtWbBgAenp6QwZYtoBMXjwYIKCgpg9ezb29vY0btw41/Pdc3YK/Pf+cndpJ+ycCUiQnQJtCk+oLqksnYENByNZvPsycanZAIR4OvJq17o80TwQlVEL656CqP1g7wbPbwbvumU2HkGoCrxeHk7GwYPc+eZbvF5+mYhGEUQ0isBgNHC5fz8MXCC776NEtKxHZEokUalR3Ei9QaoulbjMOOIy4ywfpPdy0bgQ7BJMS9+WRDSKwN+pZA16nTp2wLH9g2Ts24/K1xeFRpPr8eTsZCbumYhe1tMjrAcD6w8s0XUK8vwDz6M1aFlwdAHrE39nOqCvhMUHzfk71k5Yvldb/7Zsu7aNwzGHoVmZXabCkWWZxBUr0NSpg0vnzrYeTi42D3gGDhxIfHw806ZNIyYmhubNm7N161ZLInNkZCQKRSVINarVBdq8BIe+gF/GmYKeh1636iW0eiPfHI5i0R+XiE42FUgLcnfg1a516NeyBmqlAvRa+GYwXN0DGmcYtAkCmlp1HIJQFTl16IB9o0ZknT5N4ldf4ZuTN5N16AiGsxeQ7OzoMHoWj3h6Wp4jyzLJ2clEpUbl+RWfGU+qNpUzt89w5vYZNpzfQN86fRnaZChBzsXrdC5JEn6TJnH9+cE4PZK7/phRNvL2328TnR5NiEsIM9rPKLPdmEObDEVr0PJ94iIAMm/dQJblSrP7U2/UcyzuGFA2+Ttm5orLx+KPoTVo0Sg1hTyj8pN1OqJnziT5u00oHB2p9esvluT2ikCSK3OZzBJISUnBzc2N5ORkXF2LVpOiyGQZds2Cv0xbQXloHHSdBqX8QaAzGNl05AYLd13iZlImAAFu9ozqUoenWgejUeUEhAY9bBoKZzaDyh6e2wRhD5Xq2oJQnaT8/js3Xx2LwtWVOrt2onR2JvLll0nf8yfuzzxNwPTpxTpfpj6TG6k3uJp8lY3nN1raDagkFf+r8z9eavJSsXNIjNnZSBpNrgBj9anVzDsyD41Cw9rH19LAs0GxzllcsiyzcP88ug1ZAcD59VN4okX+FZwrktO3T/P0z0/jonbhr6f/QqlQlsl1ZFmmyzdduJ11m1U9VlkCoKrKkJzMjbGvkbF/PygU+E2ejOfzz1n1GqX9/Lb5DE+VIkmmAMfOFXZMh7/nm2Z6en0EJZil0huM/PDvTT7ddZGoRFOg4+tix6gudRjYJhh79T3/UY1G2DLaFOwo1DBwrQh2BKGYXMLD0dSqhfbKFZI2bMC5UyfS9/xZ4jYSDioH6nrUpa5HXbqHdedI7BGWHl/K/uj9fH/xe3689CO9a/VmWNNhhLqGFumciv9swjgWd4wFRxcA8EbbN8o82AHTbNOYB8dzwvlrNGnZfLHjA5SurvSp3afwJ9vYkRjTclYLvxZlFuzA3Tyerde2cij2UJUOeLRRUUS9PALtlSsoHB0JnD+vwi1nQSXbll5pPPQa9P4YkExLXJtHQBFLjBuMMlfi0/jmUBTdPv6Tid+dICoxE29nDVMeb8ifk7oQ0SEsd7Ajy/DreDi+HiQlDFgNdcPzvYYgCHmTFAq8hpnq8txe/SUJSz8HwCW8q1XaSLTya8Xy7sv5qtdXdAzqiEE28OPlH/nf5v8x+a/JXEm+UqzzJWUlMWHPBAyygV5hvRhQr3w2TIDpA90l2NR82DPFyJS9U/jt6m/ldv2SKquCg3kp675amadOc3vFCkszWVvIOHqUa08NRHvlCip/f0LXra2QwQ6IGZ6y0/pF00zP98PhxEbIToMnV4LatH1VlmXiU7M5F5PK+ZhU05+xKVyMTSNbf7eEu4ejmhGdavN8+1AcNXn8c8ky/D4FDq8EJOj7OTTsXU7fpCBUPW69Hyd+4afob0WT8ssvAJaeVtbS3Lc5S8OXcjL+JEtPLOXPG3/y85Wf+eXKL/Ss2ZOXm75MbfeCm/saZSNv/f0WsRmxhLqGMr3D9HLPo1H7B5B99hzhDi34Vz7Om3+9iYREz5o9y3UcRSXLMv/G/QsUL39He/06sR98iNPDD+H57LNFfp454Dked5xsQzZ2SuuVSEnbs4cbr45Fzs7m9urV+E+dimv37lY7f1Ek//Qz0W+9hazTYd+oETUWL0btZ702HdYmAp6y1ORJ0DghfxOBdP4XYpb+j5U13uNEvJ7zMancych71sdBraSenzPdG/kT0SEMZ7sC/pl2z4Z9n5lu9/mk3LbEC0JVJanVeA0dSuysdwHKtI1EE58mLOq6iNO3T/P58c/5I+oPfrv6G1uvbqVbaDdebvYy9Tzq5fncVadW8dfNv7BT2jGv0zyc1IV3Trc2c/HBno6tuVinJpsvbebNv94ECXqGVbyg52rKVRKzErFT2tHIq2itNnS3bnF9yBD0t6JJ++MPdJFR+E6aiFSENIUw1zC8HbxJyEzgRPwJSwBUWim//srNSW+AXo/k6IghPoGbr44lpVs3/KZOQe1btkGHLMskLF5MwkLTZ49zeFeC5sxB4ehYptctLRHwWJHOYORqQnrOrE1KzsyNHcGZE1iunof/7QP0ih/BRu0kknFGIUGYtxMN/F2o7+dKfX8XGvi7EOzpiFJRhN/U/l4Aez403e75IbQqfo6BIAj3c+/fn4QlSzEkJJRLG4lGXo349NFPOZd4jmUnlrH9+nZ+v/47v1//nfCQcF5u9nKu3JyjsUctrS/ebPsm9T3rl/kY86LOKT6oj4llZgdTr60fL//Im3++CVS8oMe8Hb2pT1PUSnWhx+vj44kc8iL6W9EofbwxxCeQuHo1+oQEAt9/D0lT8M4rcx7Pb1d/41DMIasEPHe+/ZaYadNBlnHt3ZuAmTNIWL6c21+sIHX7dtIPHMDvjUm49etXJjN+Rq2W6ClT7mmi+yK+E8YXKQC0NRHwWMnF2FQe//RvtIb7uyzfoBFj1DP41PgeLbjEHp+5xPxvHWFhtXLn4hTHweWmxGgwJUo/OKIUoxcE4V4Ke3tCVnyB9soVnLt2LbfrNvBswPzO87lw5wLLTizj92u/syNyBzsid9A5uDMjmo4gwDmAiX9OxCAbeLzW4/Sv27/cxvdf6pwZHl1sDApJwcwOM5GR2XJ5C2/+aVre6hHWw2bj+6+jsUXP39HfuUPki0PRXr+OOjCQ0LVfk3HoELfeepuUn3/GkJhI0KefonQueGbt3oCntG6vXk3cB6Zfct0HDsR/2lQkpRLf117DtWdPot+eQtbp00S/PYXkn38m4J130FixIK/+zh1ujB5D5pEjoFTiP20aHgOfstr5y5rYlm4lWToDD0zbiqNGRT0/Z+r7u5pmbvxdqO/ngoeTBmLPwFdPQFoseNaCwT+WrN3Dv1/Dj6NMtx+eAF2nWu37EASh4ricdJllJ5ax9dpWjLLplynzEkmYaxgbe2/EUW27ZYT0AweJjIhAExZG7a2mhGWD0cC0f6ax5fIWlJKSDx/5sMIEPT2+68Gt9Ft83u1zOgR2yPc4Q1oakS8MIevUKVQ+PoSu/RpNiOlnddpff3Nj7FjkjAzsGzUieNnnqLy88j3XteRr9NncB41Cwz/P/lOiPB5Zlkn4bBEJi0y1j7xeGorP+PH3zeDIej2JX64hfuFC5KwsJHt7fMaOxXPw80jK0u1Iy75ylagRI9BFRqJwcaHGJwtw6pD/a1gWSvv5LQIeK4pOzsTf1b7gacTEK7Dm/yApElyDTFWQffJeo8/TqU2w6SWQjfDgK9Dj/VLX+REEoWK7mnyV5SeW88vVXzDKRuyUdqx7fF2++T3lRXv9Opd79ESyt6f+v0ctP/v+G/TMeWQO3cPKN6H2v6LToum+qTtKSck/z/yTb6BozMwkctgwMg8fQenhQehXa7CrUyfXMZknTxI1/GUMd+6gDg0h5Isv8p1JkWWZ8G/DicuMY2WPlcVe1pJlmbgPPiTxyy8B8HntNbxeHl7g54w2MpLoqdPIOHAAAPsmTQh4913s65fs/ZK+/wA3Xn0VY0oK6qAggj9fet9rUh5K+/ld8RfdKpEAN4fC10w9a8GL28C7PqTchFW9IPp40S5w7lfTri/ZCC0jRLAjCNVETbeavP/w+/z0xE+82PhFPn30U5sHOwCqnCq6clYWxnu2RisVSt7p8A59avXBIBuY9Ocktl/fbqthAnAkzpS/09CzYf7BjlZrWrI5fASFiwvBXyzP84PdoUkTwtavQ12jBrrrkVx75lmyzpzJ85ySJFlq8JgLTxaVbDAQPXWqJdjxe/ttvEe8XOjnjCYkhJDVq/Cf9Q4KFxeyTp7kav/+xH/6KUZt8bq3J236nsiXXsKYkoJDs2aEfbPRJsGONYiAxxZcA2HIrxDQDDISYHUfiNxf8HMu7YRvI8CohyZPmer8iGBHEKqVENcQXm/1eoHLMeVJYWeH0sMDAF1M7q7pSoWSWR1nWYKeiXsm2jToseTv+OWdvyPrdNx8fRzpe/ciOToS/PnnODTKfyeXJiyMsPXrsGvQAENCAtefH0z6/rx/jpekHo+s1XJzwgSSv9sECgUBs2cXq3KxJEl4DBhArZ9/xjm8K+j1JCxewtW+/cg4+m/h1zcaiZs3n+i33wa9HtfHehHy5eoCl+8qOhHw2IqTN0T8BCEdIDsZ1jwBl3bkfez1f2DDIDBooWEfeGIJlGGFUEEQhKJSBZhmef4b8MDdoKd3rd6mmZ49k9hxPZ+fc2XMHPDkVX9HNhi4Nfkt0nbuRNJoCF68CMeWhZciUPn4EPrVGhzbtcOYnk7ksOGk/Prrfce19W8LwIn4E2Tpswo9rzEri6jRo0n9bSuo1QR9/DHufZ8o9Hl5Ufv5UmPhQoIWLEDp5YX28mWuDxpEzLvvYUxPz/f6N18fx+3lywHwfmUkgXPnorC3L9EYKgoR8NiSvZup31WdbqDPhHVPw5kfcx9z4wisfcr0eJ1u0H8lKMXmOkEQKga1nyng0ecR8IAp6Hm347s8Xutx9LKeiXsmsvP6zvIcIney7nA5+TJw/w4tWZaJmTGTlJ9/BpWKoE8W4PTgg0U+t9LFheDly3Dp2RN0Om6On0Dimq9yHRPsEoyvoy86o47j8QWnMBjS0ogaNpz0P/9CsrcnePFiXHuULv9JkiRce/ag9i8/49a3L8gyd77+mst9+pD211+5jtXHx3N9cASp27aBWk3AB7PxefXVSrHtvDCV/zuo7DSO8PQ6eOAJMOrg2xfg37Wmx2JOwdf9QJsKYQ/DwK9AVfU77gqCUHmoC5jhMVMqlLzX8T1L0DNhz4RyDXrM7SRqu9XG3d7dcr8pIfgDkr79FhQKgj6ag0uXLsU+v0KjIWjeXDwGDQJZJvb994mbNx/zniBzPR4oeFlLf+cOkS8MIePQIRTOzoSs+ALnh63XE1Hp7k7g7PcJ/uIL1EFB6G9FEzVsOLfeeAP9nTtkXbjA1YEDyTpxAqWbG6ErV+D+xBNWu76tiYCnIlBpTG0nWjxvSkj+8RXYMdO0mysrCWq0gWfWg9rB1iMVBEHIRWUuPhidf8ADd4Oex2o+Vu5BT375OwkLF5L45RoAAt59F9devUp8DUmpxG/K2/i89hoAt5cvJ/qtt5F1por65mWt/AIeXVwckYMHk3XqFEoPD0K+XI1jq6K3vygO54c6UmvLj3hGDAZJIvnHLVx5vDfXn3kW/a1oNKGhhG3cgGMb61SGrihEwFNRKJTwv4XQfrTp73/PNyU0+zeFQd+BnYttxycIgpCHu8UHYws9VqlQ8t5D79GrZq+7QU9k2Qc9eeXv3P7iCxIWLwHAb8oU3Pv1LfV1JEnCe8TLBLz3LiiVJP/wAzdGj8GYmUkbP1PwcCLhBJn6zFzP0964wfVBz5F98RIqX19Cv/6qwIRpa1A4OeE3eTJh69ehqVMbQ2IixvR0HNu0IWzjBqs0y61oRMBTkUgSdH8Xurxt+rtPA1OdHgd3W45KEAQhX+at6fro6KIdr1Dx/kPv3w16dk9gV+SuMhtfhi6Ds4lngbsBT+K6dcTNnQeAz/hxeD43yKrXdO/fnxqfLUSytydtzx4iXxhCgN4ZP0c/9EZ9rjye7CtXuD7oOXRRUaiDgwldtxa72gU3jrUmh+bNqfn99/hOnIj3qFGErPgCpbt7uV2/PImAp6KRJOg0CcYcheF7wKnybgEUBKHqU+cEPLrYWIpax9YS9ISZgp7xe8aXWdBzLP4YBtlAoFMg/k7+JP2wmdh3ZgHgNeJlvIcNK5PrunTpQsiqlSjc3Mg8fpzrzz1HZ7Vp1sa8rJV5+jTXBz2HPjYWu7p1CP36azQ1apTJeAqi0GjwGvoiPmNGF9ofrDITAU9F5VUb1JV7C6AgCFWfys+0pCVnZWFISir68xQq3n84J+gxmoKePyL/sPr47l3OStm6zVRXBvAY/Dw+Y8da/Xr3cmzRgrB1a1EFBKC9coX/zdlPcJzMoZhDZBw9SmTECxju3MG+cWNC1qxB7Ve2Xc6rOxHwCIIgCCWmsLND6ekJgL4IeTz3Mgc9PcN6ojfqGbdnHLujdlt1fOYO6Y9EOXNzwgQwGnEf8CR+kyeXSTfx/7KrXdtUoLBuHdSJKbzztYHQn/4lcuhQjGlpOLZuTcjqVahyCjgKZUf00hIEQRBK5Wq//mSdOYNDy5aovL1BqUBSKO/+qVLm/vu9fypVyArYceMPLiZfBqWC7g3/j0aNu2BXIxhNjSAUTgV3JM+P1qClw/oO1LmSyfRNKsjW4vr44wTO+bDUzTSLy5CcTNQrr5B55KjlPqdOj1Djk08qfUG/8lLaz29RwU4QBEEoFbu6dcg6c4bMo0cLPzgfLXO+wAi7N3GLTZbH9K6OKAL9cQ6tjUNwCOqgINQ1aqAOqoE6KBCFXd4dyM/cPkNIZBZvfCeDVovzo48S+MHscg92AJRuboSsWMGOFx4j+Fg0p5q50WnO9AoT7Jy9fZYMfUaelairCjHDIwiCIJSKISmJ1D92I2dnIxsNYDCC0YBs/lNvsNz/38dlg95yv1Gv53jMvyTEX8crSY9vEjgX3okBlY+PKQCqYQqANDm3f7vyK6FzvsU5C5w6tKfGkiX5Bkfl5Xzied7c8AKXHFPxdfLjs0c/o6FXQ5uNR5ZlVpxawcJ/F2KUjUxpN4WBDQbabDwFKe3ntwh4BEEQhArFYDQQlRrFucRzXLpxnJjLJ0m9dgn7+FR8k2V8k8AnWcY3GRyK0Pw7tUENWq/7EYVj3l3Sy1tUShSjdo3iavJVHFQOvP/Q+4SHhpf7ONK0aUzZO+W+WkgzO8ykX91+5T6ewoiAp5hEwCMIglA5JWQmcC7xnOXr/O1zJMZexztZxjfJFAD5Jsn4JIN/soRXkpErAVBvxZc0Cm1r6+HnkqpNZeKeiey9tReAsS3HMrTx0HJJpAa4knSFsX+M5VrKNdQKNW+1e4vLSZf5+uzXSEi899B79Kndp1zGUlQi4CkmEfAIgiBUHRm6DC7cuZArELp45yJaoxZkGU8HL3YO2IlKUfFSVvVGPR8d+oh159YB0LtWb2Z0mIGdsmyX3bZf386Uv6eQoc/Az9GPjzt/TBOfJsiyzHsH3mPj+Y0oJAUfPvwhPWv2LNOxFIcIeIpJBDyCIAhVm96o51ryNS7cuUBdj7rU9ahr6yEVaOO5jcw+OBuDbKCZTzMWdFmAt4O31a+jN+pZ+O9CVp5aCZj6e815ZA5eDncL3BplI+/se4dNFzehlJTM7TTXJstteREBTzGJgEcQBEGoaPbd2sf4PeNJ1aYS4BTAwkcXUt+zvtXOfyfrDhP/nMiB6AMAvNDoBca2HJvnzJdRNjLl7yn8dOUnVAoVCzovoFNwJ6uNpaRK+/ktCg8KgiAIgo21D2zP2sfWEuoaSnR6NIN/G2y1IoynE04z8OeBHIg+gIPKgY86fcT41uPzXeZTSApmdZxlqYL9+u7X2Xtzr1XGYksi4BEEQRCECqCmW03WPraWdv7tyNBn8OquV1l9anWRe5Tl5YeLPzD4t8FEp0cT6hrKusfW0TOs8LwcpULJew+/R3hIODqjjrF/jLXMDlVWIuARBEEQhArCzc6NJd2WMKDeAGRk5h2Zx7R/pqEz6Ip1Hq1By8x9M5n2zzS0Ri2dgzuz/vH11PGoU+RzqBVq5jwyh841OpNtyGbMrjGWVh2VkQh4BEEQBKECUSvUTH1wKm+2fROFpGDzpc0M2z6MO1l3ivT8mPQYXtj6At9d+A4JiTEtxvBJl09w0bgUfyxKNfM6z6NjYEcy9Zm8suMVjsUdK/Z5KgIR8AiCIAhCBSNJEoMaDmJR10U4q505EnuEZ395lstJlwt83qGYQwz8eSAnE07iqnFlcfhihjcdjkIq+ce9RqlhQZcFlqW2kTtGcjrhdInPZysi4BEEQRCECuqhoIf4+rGvqeFcgxtpN3ju1+f4++bf9x0nyzJfnv6SYb8PIzErkQaeDdjQewMPBT1klXHYq+z59NFPaenbkjRdGsO3D+dc4jmrnLu8iIBHEARBECqw2u61Wff4OkuwMWrnKNaeXWtJZs7QZTDpz0nMPTwXg2ygT60+rOm1hmCXYKuOw1HtyOLwxTTzaUaKNoXhvw/n4p2LVr1GWRJ1eARBEAShEtAZdLyz/x02X9oMwIB6AxjUcBAT9kzgUtIlVJKKiW0m8kyDZ8q0RUWqNpVhvw/j9O3TeNp7sqrnKmq51Sqz65mJwoPFJAIeQRAEobKSZZk1Z9Yw7/A8ZO5+fHs7eDO/83xa+LYol3EkZyfz0u8vcS7xHD4OPqzuuZoQ15AyvaYoPCgIgiAI1YQkSUQ0imDhowtxVJm6v7fwbcE3vb8pt2AHTNvnl3VbRh33OsRnxjP096HcTLtZbtcvCTHDIwiCIAiVUGRKJCcSTtAjtAdqpdomY0jITGDI1iFcS7lGkHMQq3uuxt/Jv0yuVSVmeBYtWkRYWBj29va0a9eOgwcP5nvs8uXLefjhh/Hw8MDDw4Pw8PACjxcEQRCEqijENYTetXrbLNgB01Laih4rCHEJ4WbaTYZuG0pcRpzNxlMQmwc8GzduZNy4cUyfPp2jR4/SrFkzevToQVxc3i/Y7t27eeaZZ/jjjz/Yt28fwcHBdO/enZs3K/ZUmiAIgiBURb6OvqzosYIg5yAiUyN56feXSMhMsPWw7mPzJa127drRpk0bPvvsMwCMRiPBwcGMGTOGN998s9DnGwwGPDw8+Oyzzxg8eHChx4slLUEQBEGwvhupNxiybQgx6THUca/Dyh4r8bD3sNr5K/WSllar5ciRI4SHh1vuUygUhIeHs2/fviKdIyMjA51Oh6enZ56PZ2dnk5KSkutLEARBEATrquFSgxXdV+Dj4IOfox8OKgdbDymXvHvDl5OEhAQMBgN+fn657vfz8+PcuaJVcHzjjTcIDAzMFTTda/bs2cycObPUYxUEQRAEoWAhriGs6bUGH0cf7JR2th5OLjbP4SmNDz74gA0bNvDDDz9gb2+f5zGTJ08mOTnZ8hUVFVXOoxQEQRCE6qOGS40KF+yAjWd4vL29USqVxMbG5ro/NjYWf/+Ct7XNnTuXDz74gB07dtC0adN8j7Ozs8POruK98IIgCIIglB+bzvBoNBpatWrFzp07LfcZjUZ27txJ+/bt833enDlzmDVrFlu3bqV169blMVRBEARBECoxm87wAIwbN46IiAhat25N27ZtWbBgAenp6QwZMgSAwYMHExQUxOzZswH48MMPmTZtGuvWrSMsLIyYmBgAnJ2dcXZ2ttn3IQiCIAhCxWXzgGfgwIHEx8czbdo0YmJiaN68OVu3brUkMkdGRqJQ3J2IWrJkCVqtlieffDLXeaZPn86MGTPKc+iCIAiCIFQSNq/DU95EHR5BEARBqHwqdR0eQRAEQRCE8iACHkEQBEEQqjwR8AiCIAiCUOWJgEcQBEEQhCpPBDyCIAiCIFR5IuARBEEQBKHKEwGPIAiCIAhVngh4BEEQBEGo8mxeabm8messpqSk2HgkgiAIgiAUlflzu6T1kqtdwJOamgpAcHCwjUciCIIgCEJxpaam4ubmVuznVbvWEkajkVu3buHi4oIkSVY9d0pKCsHBwURFRYm2FeVIvO62IV532xCvu22I19027n3dXVxcSE1NJTAwMFePzaKqdjM8CoWCGjVqlOk1XF1dxX8IGxCvu22I1902xOtuG+J1tw3z616SmR0zkbQsCIIgCEKVJwIeQRAEQRCqPBHwWJGdnR3Tp0/Hzs7O1kOpVsTrbhvidbcN8brbhnjdbcOar3u1S1oWBEEQBKH6ETM8giAIgiBUeSLgEQRBEAShyhMBjyAIgiAIVZ4IeARBEARBqPJEwGMlixYtIiwsDHt7e9q1a8fBgwdtPaQqb8aMGUiSlOurQYMGth5WlfPnn3/Sp08fAgMDkSSJzZs353pclmWmTZtGQEAADg4OhIeHc/HiRdsMtgop7HV/4YUX7nv/9+zZ0zaDrSJmz55NmzZtcHFxwdfXlyeeeILz58/nOiYrK4tRo0bh5eWFs7Mz/fv3JzY21kYjrhqK8rp37tz5vvf7iBEjinUdEfBYwcaNGxk3bhzTp0/n6NGjNGvWjB49ehAXF2froVV5jRo1Ijo62vL1999/23pIVU56ejrNmjVj0aJFeT4+Z84cPv30U5YuXcqBAwdwcnKiR48eZGVllfNIq5bCXneAnj175nr/r1+/vhxHWPXs2bOHUaNGsX//frZv345Op6N79+6kp6dbjnn99df56aef+Pbbb9mzZw+3bt2iX79+Nhx15VeU1x1g2LBhud7vc+bMKd6FZKHU2rZtK48aNcryd4PBIAcGBsqzZ8+24aiqvunTp8vNmjWz9TCqFUD+4YcfLH83Go2yv7+//NFHH1nuS0pKku3s7OT169fbYIRV039fd1mW5YiICPn//u//bDKe6iIuLk4G5D179siybHpvq9Vq+dtvv7Ucc/bsWRmQ9+3bZ6thVjn/fd1lWZY7deokjx07tlTnFTM8paTVajly5Ajh4eGW+xQKBeHh4ezbt8+GI6seLl68SGBgILVq1WLQoEFERkbaekjVytWrV4mJicn1/ndzc6Ndu3bi/V8Odu/eja+vL/Xr12fkyJHcvn3b1kOqUpKTkwHw9PQE4MiRI+h0ulzv9wYNGhASEiLe71b039fdbO3atXh7e9O4cWMmT55MRkZGsc5b7ZqHWltCQgIGgwE/P79c9/v5+XHu3Dkbjap6aNeuHatXr6Z+/fpER0czc+ZMHn74YU6dOoWLi4uth1ctxMTEAOT5/jc/JpSNnj170q9fP2rWrMnly5d566236NWrF/v27UOpVNp6eJWe0Wjktddeo2PHjjRu3Bgwvd81Gg3u7u65jhXvd+vJ63UHePbZZwkNDSUwMJATJ07wxhtvcP78eb7//vsin1sEPEKl1atXL8vtpk2b0q5dO0JDQ/nmm28YOnSoDUcmCGXv6aefttxu0qQJTZs2pXbt2uzevZuuXbvacGRVw6hRozh16pTICyxn+b3uw4cPt9xu0qQJAQEBdO3alcuXL1O7du0inVssaZWSt7c3SqXyviz92NhY/P39bTSq6snd3Z169epx6dIlWw+l2jC/x8X73/Zq1aqFt7e3eP9bwejRo/n555/5448/qFGjhuV+f39/tFotSUlJuY4X73fryO91z0u7du0AivV+FwFPKWk0Glq1asXOnTst9xmNRnbu3En79u1tOLLqJy0tjcuXLxMQEGDroVQbNWvWxN/fP9f7PyUlhQMHDoj3fzm7ceMGt2/fFu//UpBlmdGjR/PDDz+wa9cuatasmevxVq1aoVarc73fz58/T2RkpHi/l0Jhr3tejh07BlCs97tY0rKCcePGERERQevWrWnbti0LFiwgPT2dIUOG2HpoVdqECRPo06cPoaGh3Lp1i+nTp6NUKnnmmWdsPbQqJS0tLddvUVevXuXYsWN4enoSEhLCa6+9xrvvvkvdunWpWbMmU6dOJTAwkCeeeMJ2g64CCnrdPT09mTlzJv3798ff35/Lly8zadIk6tSpQ48ePWw46spt1KhRrFu3jh9//BEXFxdLXo6bmxsODg64ubkxdOhQxo0bh6enJ66urowZM4b27dvz4IMP2nj0lVdhr/vly5dZt24djz32GF5eXpw4cYLXX3+dRx55hKZNmxb9QqXa4yVYLFy4UA4JCZE1Go3ctm1bef/+/bYeUpU3cOBAOSAgQNZoNHJQUJA8cOBA+dKlS7YeVpXzxx9/yMB9XxEREbIsm7amT506Vfbz85Pt7Ozkrl27yufPn7ftoKuAgl73jIwMuXv37rKPj4+sVqvl0NBQediwYXJMTIyth12p5fV6A/KqVassx2RmZsqvvPKK7OHhITs6Osp9+/aVo6OjbTfoKqCw1z0yMlJ+5JFHZE9PT9nOzk6uU6eOPHHiRDk5OblY15FyLiYIgiAIglBliRweQRAEQRCqPBHwCIIgCIJQ5YmARxAEQRCEKk8EPIIgCIIgVHki4BEEQRAEocoTAY8gCIIgCFWeCHgEQRAEQajyRMAjCIIASJLE5s2bbT0MQRDKiAh4BEGwuRdeeAFJku776tmzp62HJghCFSF6aQmCUCH07NmTVatW5brPzs7ORqMRBKGqETM8giBUCHZ2dvj7++f68vDwAEzLTUuWLKFXr144ODhQq1Ytvvvuu1zPP3nyJI8++igODg54eXkxfPhw0tLSch2zcuVKGjVqhJ2dHQEBAYwePTrX4wkJCfTt2xdHR0fq1q3Lli1byvabFgSh3IiARxCESmHq1Kn079+f48ePM2jQIJ5++mnOnj0LQHp6Oj169MDDw4NDhw7x7bffsmPHjlwBzZIlSxg1ahTDhw/n5MmTbNmyhTp16uS6xsyZM3nqqac4ceIEjz32GIMGDSIxMbFcv09BEMqI1dueCoIgFFNERISsVCplJyenXF/vvfeeLMumbsojRozI9Zx27drJI0eOlGVZlpctWyZ7eHjIaWlplsd/+eUXWaFQWDqIBwYGym+//Xa+YwDkKVOmWP6elpYmA/Jvv/1mte9TEATbETk8giBUCF26dGHJkiW57vP09LTcbt++fa7H2rdvz7FjxwA4e/YszZo1w8nJyfJ4x44dMRqNnD9/HkmSuHXrFl27di1wDE2bNrXcdnJywtXVlbi4uJJ+S4IgVCAi4BEEoUJwcnK6b4nJWhwcHIp0nFqtzvV3SZIwGo1lMSRBEMqZyOERBKFS2L9//31/b9iwIQANGzbk+PHjpKenWx7fu3cvCoWC+vXr4+LiQlhYGDt37izXMQuCUHGIGR5BECqE7OxsYmJict2nUqnw9vYG4Ntvv6V169Y89NBDrF27loMHD7JixQoABg0axPTp04mIiGDGjBnEx8czZswYnn/+efz8/ACYMWMGI0aMwNfXl169epGamsrevXsZM2ZM+X6jgiDYhAh4BEGoELZu3UpAQECu++rXr8+5c+cA0w6qDRs28MorrxAQEMD69et54IEHAHB0dGTbtm2MHTuWNm3a4OjoSP/+/Zk/f77lXBEREWRlZfHxxx8zYcIEvL29efLJJ8vvGxQEwaYkWZZlWw9CEAShIJIk8cMPP/DEE0/YeiiCIFRSIodHEARBEIQqTwQ8giAIgiBUeSKHRxCECk+svAuCUFpihkcQBEEQhCpPBDyCIAiCIFR5IuARBEEQBKHKEwGPIAiCIAhVngh4BEEQBEGo8kTAIwiCIAhClScCHkEQBEEQqjwR8AiCIAiCUOWJgEcQBEEQhCrv/wF/XocRv6lZAwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -469,13 +469,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "72/72 [==============================] - 23s 318ms/step - loss: 0.4541 - accuracy: 0.9084\n"
+      "29/29 [==============================] - 10s 306ms/step - loss: 0.3675 - accuracy: 0.8631\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[0.45413827896118164, 0.9084201455116272]"
+       "[0.367510586977005, 0.8631465435028076]"
       ]
      },
      "execution_count": 10,
@@ -497,129 +497,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " conv2d (Conv2D)             (None, 224, 224, 64)      1792      \n",
-      "                                                                 \n",
-      " conv2d_1 (Conv2D)           (None, 224, 224, 64)      36928     \n",
-      "                                                                 \n",
-      " max_pooling2d (MaxPooling2D  (None, 112, 112, 64)     0         \n",
-      " )                                                               \n",
-      "                                                                 \n",
-      " conv2d_2 (Conv2D)           (None, 112, 112, 128)     73856     \n",
-      "                                                                 \n",
-      " conv2d_3 (Conv2D)           (None, 112, 112, 128)     147584    \n",
-      "                                                                 \n",
-      " max_pooling2d_1 (MaxPooling  (None, 56, 56, 128)      0         \n",
-      " 2D)                                                             \n",
-      "                                                                 \n",
-      " conv2d_4 (Conv2D)           (None, 56, 56, 256)       295168    \n",
-      "                                                                 \n",
-      " conv2d_5 (Conv2D)           (None, 56, 56, 256)       590080    \n",
-      "                                                                 \n",
-      " conv2d_6 (Conv2D)           (None, 56, 56, 256)       590080    \n",
-      "                                                                 \n",
-      " max_pooling2d_2 (MaxPooling  (None, 28, 28, 256)      0         \n",
-      " 2D)                                                             \n",
-      "                                                                 \n",
-      " conv2d_7 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
-      "                                                                 \n",
-      " conv2d_8 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
-      "                                                                 \n",
-      " conv2d_9 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
-      "                                                                 \n",
-      " max_pooling2d_3 (MaxPooling  (None, 14, 14, 512)      0         \n",
-      " 2D)                                                             \n",
-      "                                                                 \n",
-      " conv2d_10 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
-      "                                                                 \n",
-      " conv2d_11 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
-      "                                                                 \n",
-      " conv2d_12 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
-      "                                                                 \n",
-      " flatten (Flatten)           (None, 100352)            0         \n",
-      "                                                                 \n",
-      " dense (Dense)               (None, 4096)              411045888 \n",
-      "                                                                 \n",
-      " dense_1 (Dense)             (None, 4096)              16781312  \n",
-      "                                                                 \n",
-      " dense_2 (Dense)             (None, 12)                49164     \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 442,591,052\n",
-      "Trainable params: 442,591,052\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/homebrew/lib/python3.10/site-packages/keras/optimizers/optimizer_v2/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
-      "  super().__init__(name, **kwargs)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras,os\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.optimizers import Adam\n",
-    "import numpy as np\n",
-    "\n",
-    "model = keras.models.Sequential([\n",
-    "    keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
-    "    keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
-    "    keras.layers.Flatten(),\n",
-    "    keras.layers.Dense(units = 4096, activation='relu'),\n",
-    "    keras.layers.Dense(units = 4096, activation='relu'),\n",
-    "    keras.layers.Dense(units = 12, activation='softmax')\n",
-    "])\n",
-    "\n",
-    "opt = Adam(lr=0.001)\n",
-    "model.compile(optimizer=opt, loss=keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training: 7430\n",
-      "Test: 2323\n",
-      "Validation: 1858\n"
+      "Training: 2990\n",
+      "Test: 935\n",
+      "Validation: 748\n"
      ]
     }
    ],
@@ -629,160 +516,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
+      "Model: \"model_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
+      "                                                                 \n",
+      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
+      "                                                                 \n",
+      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
+      "                                                                 \n",
+      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
+      "                                                                 \n",
+      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
+      "                                                                 \n",
+      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
+      "                                                                 \n",
+      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
+      "                                                                 \n",
+      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
+      "                                                                 \n",
+      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
+      "                                                                 \n",
+      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
+      "                                                                 \n",
+      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
+      "                                                                 \n",
+      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
+      "                                                                 \n",
+      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
+      "                                                                 \n",
+      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
+      "                                                                 \n",
+      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
+      "                                                                 \n",
+      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
+      "                                                                 \n",
+      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
+      "                                                                 \n",
+      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
+      "                                                                 \n",
+      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
+      "                                                                 \n",
+      " flatten_2 (Flatten)         (None, 25088)             0         \n",
+      "                                                                 \n",
+      " dense_4 (Dense)             (None, 5)                 125445    \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 14,840,133\n",
+      "Trainable params: 125,445\n",
+      "Non-trainable params: 14,714,688\n",
+      "_________________________________________________________________\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/_h/ljwht4gd7lb99rm1hm78h7_00000gn/T/ipykernel_24066/3966396738.py:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
-      "  vgg = model.fit_generator(steps_per_epoch=len(train_ds_v), generator=train_ds_v, validation_data= val_ds_v, validation_steps=len(val_ds_v), epochs=25, callbacks=[checkpoint,early])\n"
+      "/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_14470/2199093522.py:50: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+      "  vggr = model.fit_generator(\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/25\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-01-06 22:32:18.362109: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "232/232 [==============================] - ETA: 0s - loss: 2.4227 - accuracy: 0.1339 \n",
-      "Epoch 1: val_accuracy improved from -inf to 0.15086, saving model to vgg16_2.h5\n",
-      "232/232 [==============================] - 3659s 16s/step - loss: 2.4227 - accuracy: 0.1339 - val_loss: 2.4052 - val_accuracy: 0.1509\n",
+      "Epoch 1/25\n",
+      "93/93 [==============================] - 389s 4s/step - loss: 0.3938 - accuracy: 0.8753 - val_loss: 0.1230 - val_accuracy: 0.9647\n",
       "Epoch 2/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4051 - accuracy: 0.1356 \n",
-      "Epoch 2: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3761s 16s/step - loss: 2.4051 - accuracy: 0.1356 - val_loss: 2.4036 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 417s 4s/step - loss: 0.0512 - accuracy: 0.9909 - val_loss: 0.0867 - val_accuracy: 0.9715\n",
       "Epoch 3/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4026 - accuracy: 0.1381 \n",
-      "Epoch 3: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3712s 16s/step - loss: 2.4026 - accuracy: 0.1381 - val_loss: 2.4002 - val_accuracy: 0.1503\n",
+      "93/93 [==============================] - 424s 5s/step - loss: 0.0243 - accuracy: 0.9990 - val_loss: 0.0692 - val_accuracy: 0.9769\n",
       "Epoch 4/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4015 - accuracy: 0.1379 \n",
-      "Epoch 4: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3690s 16s/step - loss: 2.4015 - accuracy: 0.1379 - val_loss: 2.4012 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 431s 5s/step - loss: 0.0148 - accuracy: 1.0000 - val_loss: 0.0614 - val_accuracy: 0.9769\n",
       "Epoch 5/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4015 - accuracy: 0.1382 \n",
-      "Epoch 5: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3695s 16s/step - loss: 2.4015 - accuracy: 0.1382 - val_loss: 2.3971 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 439s 5s/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.0607 - val_accuracy: 0.9810\n",
       "Epoch 6/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4004 - accuracy: 0.1393 \n",
-      "Epoch 6: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3703s 16s/step - loss: 2.4004 - accuracy: 0.1393 - val_loss: 2.3999 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 445s 5s/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.0670 - val_accuracy: 0.9755\n",
       "Epoch 7/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4006 - accuracy: 0.1379 \n",
-      "Epoch 7: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3678s 16s/step - loss: 2.4006 - accuracy: 0.1379 - val_loss: 2.3984 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 448s 5s/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0559 - val_accuracy: 0.9783\n",
       "Epoch 8/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4007 - accuracy: 0.1394 \n",
-      "Epoch 8: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3677s 16s/step - loss: 2.4007 - accuracy: 0.1394 - val_loss: 2.3993 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 451s 5s/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0530 - val_accuracy: 0.9796\n",
       "Epoch 9/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4006 - accuracy: 0.1354 \n",
-      "Epoch 9: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3660s 16s/step - loss: 2.4006 - accuracy: 0.1354 - val_loss: 2.3993 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 482s 5s/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.0538 - val_accuracy: 0.9783\n",
       "Epoch 10/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4004 - accuracy: 0.1395 \n",
-      "Epoch 10: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3696s 16s/step - loss: 2.4004 - accuracy: 0.1395 - val_loss: 2.3970 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 488s 5s/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.0494 - val_accuracy: 0.9810\n",
       "Epoch 11/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4005 - accuracy: 0.1394 \n",
-      "Epoch 11: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3672s 16s/step - loss: 2.4005 - accuracy: 0.1394 - val_loss: 2.4014 - val_accuracy: 0.1498\n",
+      "93/93 [==============================] - 494s 5s/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0502 - val_accuracy: 0.9796\n",
       "Epoch 12/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4003 - accuracy: 0.1374 \n",
-      "Epoch 12: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3548s 15s/step - loss: 2.4003 - accuracy: 0.1374 - val_loss: 2.3988 - val_accuracy: 0.1503\n",
+      "93/93 [==============================] - 491s 5s/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0503 - val_accuracy: 0.9837\n",
       "Epoch 13/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4005 - accuracy: 0.1393 \n",
-      "Epoch 13: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3600s 16s/step - loss: 2.4005 - accuracy: 0.1393 - val_loss: 2.3987 - val_accuracy: 0.1503\n",
+      "93/93 [==============================] - 494s 5s/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0485 - val_accuracy: 0.9810\n",
       "Epoch 14/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4005 - accuracy: 0.1394 \n",
-      "Epoch 14: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3600s 16s/step - loss: 2.4005 - accuracy: 0.1394 - val_loss: 2.3989 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 486s 5s/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0448 - val_accuracy: 0.9851\n",
       "Epoch 15/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4004 - accuracy: 0.1393 \n",
-      "Epoch 15: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3261s 14s/step - loss: 2.4004 - accuracy: 0.1393 - val_loss: 2.3988 - val_accuracy: 0.1503\n",
+      "93/93 [==============================] - 485s 5s/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0474 - val_accuracy: 0.9810\n",
       "Epoch 16/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.3998 - accuracy: 0.1367 \n",
-      "Epoch 16: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3359s 14s/step - loss: 2.3998 - accuracy: 0.1367 - val_loss: 2.3984 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 503s 5s/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0430 - val_accuracy: 0.9823\n",
       "Epoch 17/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4001 - accuracy: 0.1395 \n",
-      "Epoch 17: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3397s 15s/step - loss: 2.4001 - accuracy: 0.1395 - val_loss: 2.4013 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 472s 5s/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0481 - val_accuracy: 0.9796\n",
       "Epoch 18/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.3998 - accuracy: 0.1394 \n",
-      "Epoch 18: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3391s 15s/step - loss: 2.3998 - accuracy: 0.1394 - val_loss: 2.3987 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 474s 5s/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0503 - val_accuracy: 0.9783\n",
       "Epoch 19/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.3991 - accuracy: 0.1395 \n",
-      "Epoch 19: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3483s 15s/step - loss: 2.3991 - accuracy: 0.1395 - val_loss: 2.4005 - val_accuracy: 0.1509\n",
+      "93/93 [==============================] - 9356s 102s/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0496 - val_accuracy: 0.9783\n",
       "Epoch 20/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.4009 - accuracy: 0.1373 \n",
-      "Epoch 20: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3464s 15s/step - loss: 2.4009 - accuracy: 0.1373 - val_loss: 2.3981 - val_accuracy: 0.1503\n",
+      "93/93 [==============================] - 10544s 115s/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0466 - val_accuracy: 0.9837\n",
       "Epoch 21/25\n",
-      "232/232 [==============================] - ETA: 0s - loss: 2.3996 - accuracy: 0.1394 \n",
-      "Epoch 21: val_accuracy did not improve from 0.15086\n",
-      "232/232 [==============================] - 3464s 15s/step - loss: 2.3996 - accuracy: 0.1394 - val_loss: 2.3978 - val_accuracy: 0.1509\n",
-      "Epoch 21: early stopping\n"
+      "93/93 [==============================] - 10648s 116s/step - loss: 9.2169e-04 - accuracy: 1.0000 - val_loss: 0.0457 - val_accuracy: 0.9837\n",
+      "Epoch 22/25\n",
+      "93/93 [==============================] - 11629s 116s/step - loss: 8.5353e-04 - accuracy: 1.0000 - val_loss: 0.0462 - val_accuracy: 0.9837\n",
+      "Epoch 23/25\n",
+      "93/93 [==============================] - 4931s 54s/step - loss: 7.7390e-04 - accuracy: 1.0000 - val_loss: 0.0466 - val_accuracy: 0.9837\n",
+      "Epoch 24/25\n",
+      "93/93 [==============================] - 419s 5s/step - loss: 7.1216e-04 - accuracy: 1.0000 - val_loss: 0.0456 - val_accuracy: 0.9823\n",
+      "Epoch 25/25\n",
+      "93/93 [==============================] - 444s 5s/step - loss: 6.6600e-04 - accuracy: 1.0000 - val_loss: 0.0463 - val_accuracy: 0.9837\n"
      ]
     }
    ],
    "source": [
-    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
+    "import keras,os\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "import numpy as np\n",
+    "from keras.applications import VGG16\n",
+    "from keras.layers import Input, Lambda, Dense, Flatten\n",
+    "from keras.models import Model\n",
+    "from keras.preprocessing import image\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.models import Sequential\n",
+    "import numpy as np\n",
+    "from glob import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ssl\n",
+    "ssl._create_default_https_context = ssl._create_unverified_context\n",
     "\n",
-    "checkpoint = ModelCheckpoint(\"vgg16_2.h5\", monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
-    "early = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=20, verbose=1, mode='auto')\n",
-    "vgg = model.fit_generator(steps_per_epoch=len(train_ds_v), generator=train_ds_v, validation_data= val_ds_v, validation_steps=len(val_ds_v), epochs=25, callbacks=[checkpoint,early])"
+    "IMAGE_SIZE = [224, 224]\n",
+    "\n",
+    "# add preprocessing layer to the front of resnet\n",
+    "vgg2 = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
+    "\n",
+    "# don't train existing weights\n",
+    "for layer in vgg2.layers:\n",
+    "  layer.trainable = False\n",
+    "  \n",
+    "  # useful for getting number of classes\n",
+    "classes = 5\n",
+    "  \n",
+    "\n",
+    "# our layers - you can add more if you want\n",
+    "x = Flatten()(vgg2.output)\n",
+    "# x = Dense(1000, activation='relu')(x)\n",
+    "prediction = Dense(5, activation='softmax')(x)\n",
+    "\n",
+    "# create a model object\n",
+    "model = Model(inputs=vgg2.input, outputs=prediction)\n",
+    "\n",
+    "# view the structure of the model\n",
+    "model.summary()\n",
+    "# tell the model what cost and optimization method to use\n",
+    "model.compile(\n",
+    "  loss='sparse_categorical_crossentropy',\n",
+    "  optimizer='adam',\n",
+    "  metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "#train_ds_vgg_sw, test_ds_vgg_sw, validation_ds_vgg_sw\n",
+    "# fit the model\n",
+    "vggr = model.fit_generator(\n",
+    "  train_ds_v,\n",
+    "  validation_data=val_ds_v,\n",
+    "  epochs=25,\n",
+    "  steps_per_epoch=len(train_ds_v),\n",
+    "  validation_steps=len(val_ds_v))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'vgg' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [11], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m plt\u001b[39m.\u001b[39mplot(vgg\u001b[39m.\u001b[39mhistory[\u001b[39m\"\u001b[39m\u001b[39maccuracy\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m      3\u001b[0m plt\u001b[39m.\u001b[39mplot(vgg\u001b[39m.\u001b[39mhistory[\u001b[39m'\u001b[39m\u001b[39mval_accuracy\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      4\u001b[0m plt\u001b[39m.\u001b[39mplot(vgg\u001b[39m.\u001b[39mhistory[\u001b[39m'\u001b[39m\u001b[39mloss\u001b[39m\u001b[39m'\u001b[39m])\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'vgg' is not defined"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "plt.plot(vgg.history[\"accuracy\"])\n",
-    "plt.plot(vgg.history['val_accuracy'])\n",
-    "plt.plot(vgg.history['loss'])\n",
-    "plt.plot(vgg.history['val_loss'])\n",
+    "plt.plot(vggr.history[\"accuracy\"])\n",
+    "plt.plot(vggr.history['val_accuracy'])\n",
+    "plt.plot(vggr.history['loss'])\n",
+    "plt.plot(vggr.history['val_loss'])\n",
     "plt.title(\"Model accuracy\")\n",
     "plt.ylabel(\"Value\")\n",
     "plt.xlabel(\"Epoch\")\n",
@@ -792,19 +730,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'model' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model\u001b[39m.\u001b[39mevaluate(test_ds_v)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29/29 [==============================] - 112s 4s/step - loss: 0.0430 - accuracy: 0.9860\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[0.043045952916145325, 0.985991358757019]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -816,42 +760,81 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ResNet50"
+    "# ResNet101V2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.layers import Input, Lambda, Dense, Flatten\n",
+    "from keras.models import Model\n",
+    "from keras.preprocessing import image\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.models import Sequential\n",
+    "import numpy as np\n",
+    "from glob import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ssl\n",
+    "ssl._create_default_https_context = ssl._create_unverified_context\n",
+    "from keras.applications import ResNet101V2\n",
+    "\n",
+    "# re-size all the images to this\n",
+    "IMAGE_SIZE = [224, 224]\n",
+    "\n",
+    "# add preprocessing layer to the front of resnet\n",
+    "resnet = ResNet101V2(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
+    "\n",
+    "# don't train existing weights\n",
+    "for layer in resnet.layers:\n",
+    "  layer.trainable = False\n",
+    "  \n",
+    "  # useful for getting number of classes\n",
+    "classes = 5\n",
+    "  \n",
+    "\n",
+    "# our layers - you can add more if you want\n",
+    "x = Flatten()(resnet.output)\n",
+    "# x = Dense(1000, activation='relu')(x)\n",
+    "prediction = Dense(5, activation='softmax')(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"model_1\"\n",
+      "Model: \"model_2\"\n",
       "__________________________________________________________________________________________________\n",
       " Layer (type)                   Output Shape         Param #     Connected to                     \n",
       "==================================================================================================\n",
-      " input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
+      " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
       "                                )]                                                                \n",
       "                                                                                                  \n",
-      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_2[0][0]']                \n",
+      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_3[0][0]']                \n",
       "                                                                                                  \n",
       " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
-      "                                )                                                                 \n",
-      "                                                                                                  \n",
-      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
-      "                                )                                                                 \n",
-      "                                                                                                  \n",
-      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
+      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_conv[0][0]']             \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
       " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
       "                                                                                                  \n",
-      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
+      " conv2_block1_preact_bn (BatchN  (None, 56, 56, 64)  256         ['pool1_pool[0][0]']             \n",
+      " ormalization)                                                                                    \n",
+      "                                                                                                  \n",
+      " conv2_block1_preact_relu (Acti  (None, 56, 56, 64)  0           ['conv2_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4096        ['conv2_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -859,7 +842,10 @@
       " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
+      " conv2_block1_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36864       ['conv2_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -867,22 +853,22 @@
       " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
+      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block1_out (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_conv[0][0]',    \n",
+      "                                                                  'conv2_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block2_preact_bn (BatchN  (None, 56, 56, 256)  1024       ['conv2_block1_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
-      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
+      " conv2_block2_preact_relu (Acti  (None, 56, 56, 256)  0          ['conv2_block2_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
-      "                                                                                                  \n",
-      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
+      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16384       ['conv2_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -890,7 +876,10 @@
       " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
+      " conv2_block2_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36864       ['conv2_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -900,15 +889,17 @@
       "                                                                                                  \n",
       " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block2_out (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
+      "                                                                  'conv2_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
-      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
+      " conv2_block3_preact_bn (BatchN  (None, 56, 56, 256)  1024       ['conv2_block2_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
+      " conv2_block3_preact_relu (Acti  (None, 56, 56, 256)  0          ['conv2_block3_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
+      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16384       ['conv2_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -916,25 +907,32 @@
       " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
+      " conv2_block3_2_pad (ZeroPaddin  (None, 58, 58, 64)  0           ['conv2_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
       "                                                                                                  \n",
-      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
+      " conv2_block3_2_conv (Conv2D)   (None, 28, 28, 64)   36864       ['conv2_block3_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv2_block3_2_bn (BatchNormal  (None, 28, 28, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
       "                                                                                                  \n",
-      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
+      " conv2_block3_2_relu (Activatio  (None, 28, 28, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
+      " max_pooling2d_3 (MaxPooling2D)  (None, 28, 28, 256)  0          ['conv2_block2_out[0][0]']       \n",
       "                                                                                                  \n",
-      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv2_block3_3_conv (Conv2D)   (None, 28, 28, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
-      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
+      " conv2_block3_out (Add)         (None, 28, 28, 256)  0           ['max_pooling2d_3[0][0]',        \n",
+      "                                                                  'conv2_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
+      " conv3_block1_preact_bn (BatchN  (None, 28, 28, 256)  1024       ['conv2_block3_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
+      " conv3_block1_preact_relu (Acti  (None, 28, 28, 256)  0          ['conv3_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32768       ['conv3_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -942,7 +940,10 @@
       " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
+      " conv3_block1_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -950,22 +951,22 @@
       " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
+      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv3_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block1_out (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_conv[0][0]',    \n",
+      "                                                                  'conv3_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block2_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block1_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
-      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
+      " conv3_block2_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block2_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
-      "                                                                                                  \n",
-      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
+      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -973,7 +974,10 @@
       " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
+      " conv3_block2_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -983,15 +987,17 @@
       "                                                                                                  \n",
       " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block2_out (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
+      "                                                                  'conv3_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
-      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
+      " conv3_block3_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block2_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
+      " conv3_block3_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block3_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
+      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -999,7 +1005,10 @@
       " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
+      " conv3_block3_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147456      ['conv3_block3_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1009,15 +1018,17 @@
       "                                                                                                  \n",
       " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block3_out (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
+      "                                                                  'conv3_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
-      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
+      " conv3_block4_preact_bn (BatchN  (None, 28, 28, 512)  2048       ['conv3_block3_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
+      " conv3_block4_preact_relu (Acti  (None, 28, 28, 512)  0          ['conv3_block4_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
+      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65536       ['conv3_block4_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1025,25 +1036,32 @@
       " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
+      " conv3_block4_2_pad (ZeroPaddin  (None, 30, 30, 128)  0          ['conv3_block4_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
       "                                                                                                  \n",
-      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
+      " conv3_block4_2_conv (Conv2D)   (None, 14, 14, 128)  147456      ['conv3_block4_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv3_block4_2_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
       "                                                                                                  \n",
-      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
+      " conv3_block4_2_relu (Activatio  (None, 14, 14, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
+      " max_pooling2d_4 (MaxPooling2D)  (None, 14, 14, 512)  0          ['conv3_block3_out[0][0]']       \n",
       "                                                                                                  \n",
-      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv3_block4_3_conv (Conv2D)   (None, 14, 14, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
-      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
+      " conv3_block4_out (Add)         (None, 14, 14, 512)  0           ['max_pooling2d_4[0][0]',        \n",
+      "                                                                  'conv3_block4_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
+      " conv4_block1_preact_bn (BatchN  (None, 14, 14, 512)  2048       ['conv3_block4_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
+      " conv4_block1_preact_relu (Acti  (None, 14, 14, 512)  0          ['conv4_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131072      ['conv4_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1051,7 +1069,10 @@
       " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
+      " conv4_block1_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1059,25 +1080,23 @@
       " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv4_block1_preact_relu[0][0]'\n",
+      "                                )                                ]                                \n",
       "                                                                                                  \n",
       " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block1_out (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_conv[0][0]',    \n",
+      "                                )                                 'conv4_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block2_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block1_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
-      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
+      " conv4_block2_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block2_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
-      "                                )                                                                 \n",
-      "                                                                                                  \n",
-      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
+      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1085,7 +1104,10 @@
       " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
+      " conv4_block2_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1096,16 +1118,17 @@
       " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block2_out (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
+      "                                )                                 'conv4_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
-      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
+      " conv4_block3_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block2_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block3_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block3_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
+      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1113,7 +1136,10 @@
       " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
+      " conv4_block3_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block3_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1124,16 +1150,17 @@
       " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block3_out (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
+      "                                )                                 'conv4_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
-      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
+      " conv4_block4_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block3_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block4_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block4_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
+      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block4_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1141,7 +1168,10 @@
       " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
+      " conv4_block4_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block4_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block4_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1152,16 +1182,17 @@
       " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block4_out (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
+      "                                )                                 'conv4_block4_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
-      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
+      " conv4_block5_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block4_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block5_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block5_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
+      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block5_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1169,7 +1200,10 @@
       " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
+      " conv4_block5_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block5_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block5_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1180,16 +1214,17 @@
       " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block5_out (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
+      "                                )                                 'conv4_block5_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
-      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
+      " conv4_block6_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block5_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
-      "                                )                                                                 \n",
+      " conv4_block6_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block6_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
+      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block6_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1197,7 +1232,10 @@
       " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
+      " conv4_block6_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block6_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block6_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1208,16 +1246,562 @@
       " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
-      " ization)                       )                                                                 \n",
+      " conv4_block6_out (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
+      "                                )                                 'conv4_block6_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
-      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
+      " conv4_block7_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block6_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
       "                                                                                                  \n",
-      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
+      " conv4_block7_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block7_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block7_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block7_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
+      "                                                                                                  \n",
+      " conv4_block7_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block7_1_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block7_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block7_1_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block7_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block7_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block7_2_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block7_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block7_2_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block7_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block7_2_relu[0][0]']    \n",
       "                                )                                                                 \n",
       "                                                                                                  \n",
-      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
+      " conv4_block7_out (Add)         (None, 14, 14, 1024  0           ['conv4_block6_out[0][0]',       \n",
+      "                                )                                 'conv4_block7_3_conv[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block8_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block7_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block8_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block8_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block8_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block8_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
+      "                                                                                                  \n",
+      " conv4_block8_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block8_1_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block8_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block8_1_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block8_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block8_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block8_2_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block8_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block8_2_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block8_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block8_2_relu[0][0]']    \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block8_out (Add)         (None, 14, 14, 1024  0           ['conv4_block7_out[0][0]',       \n",
+      "                                )                                 'conv4_block8_3_conv[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block9_preact_bn (BatchN  (None, 14, 14, 1024  4096       ['conv4_block8_out[0][0]']       \n",
+      " ormalization)                  )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block9_preact_relu (Acti  (None, 14, 14, 1024  0          ['conv4_block9_preact_bn[0][0]'] \n",
+      " vation)                        )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block9_1_conv (Conv2D)   (None, 14, 14, 256)  262144      ['conv4_block9_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
+      "                                                                                                  \n",
+      " conv4_block9_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block9_1_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block9_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block9_1_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_pad (ZeroPaddin  (None, 16, 16, 256)  0          ['conv4_block9_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_conv (Conv2D)   (None, 14, 14, 256)  589824      ['conv4_block9_2_pad[0][0]']     \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block9_2_conv[0][0]']    \n",
+      " ization)                                                                                         \n",
+      "                                                                                                  \n",
+      " conv4_block9_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block9_2_bn[0][0]']      \n",
+      " n)                                                                                               \n",
+      "                                                                                                  \n",
+      " conv4_block9_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block9_2_relu[0][0]']    \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block9_out (Add)         (None, 14, 14, 1024  0           ['conv4_block8_out[0][0]',       \n",
+      "                                )                                 'conv4_block9_3_conv[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block10_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block9_out[0][0]']       \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block10_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block10_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block10_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block10_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block10_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block10_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block10_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block10_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block10_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block10_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block10_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block10_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block10_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block10_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block10_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block10_out (Add)        (None, 14, 14, 1024  0           ['conv4_block9_out[0][0]',       \n",
+      "                                )                                 'conv4_block10_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block11_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block10_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block11_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block11_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block11_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block11_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block11_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block11_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block11_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block11_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block11_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block11_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block11_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block11_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block11_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block11_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block11_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block11_out (Add)        (None, 14, 14, 1024  0           ['conv4_block10_out[0][0]',      \n",
+      "                                )                                 'conv4_block11_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block12_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block11_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block12_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block12_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block12_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block12_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block12_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block12_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block12_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block12_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block12_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block12_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block12_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block12_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block12_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block12_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block12_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block12_out (Add)        (None, 14, 14, 1024  0           ['conv4_block11_out[0][0]',      \n",
+      "                                )                                 'conv4_block12_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block13_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block12_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block13_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block13_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block13_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block13_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block13_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block13_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block13_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block13_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block13_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block13_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block13_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block13_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block13_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block13_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block13_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block13_out (Add)        (None, 14, 14, 1024  0           ['conv4_block12_out[0][0]',      \n",
+      "                                )                                 'conv4_block13_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block14_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block13_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block14_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block14_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block14_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block14_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block14_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block14_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block14_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block14_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block14_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block14_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block14_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block14_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block14_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block14_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block14_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block14_out (Add)        (None, 14, 14, 1024  0           ['conv4_block13_out[0][0]',      \n",
+      "                                )                                 'conv4_block14_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block15_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block14_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block15_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block15_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block15_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block15_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block15_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block15_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block15_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block15_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block15_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block15_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block15_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block15_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block15_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block15_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block15_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block15_out (Add)        (None, 14, 14, 1024  0           ['conv4_block14_out[0][0]',      \n",
+      "                                )                                 'conv4_block15_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block16_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block15_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block16_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block16_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block16_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block16_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block16_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block16_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block16_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block16_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block16_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block16_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block16_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block16_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block16_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block16_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block16_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block16_out (Add)        (None, 14, 14, 1024  0           ['conv4_block15_out[0][0]',      \n",
+      "                                )                                 'conv4_block16_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block17_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block16_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block17_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block17_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block17_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block17_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block17_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block17_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block17_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block17_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block17_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block17_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block17_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block17_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block17_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block17_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block17_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block17_out (Add)        (None, 14, 14, 1024  0           ['conv4_block16_out[0][0]',      \n",
+      "                                )                                 'conv4_block17_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block18_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block17_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block18_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block18_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block18_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block18_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block18_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block18_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block18_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block18_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block18_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block18_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block18_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block18_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block18_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block18_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block18_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block18_out (Add)        (None, 14, 14, 1024  0           ['conv4_block17_out[0][0]',      \n",
+      "                                )                                 'conv4_block18_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block19_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block18_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block19_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block19_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block19_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block19_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block19_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block19_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block19_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block19_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block19_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block19_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block19_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block19_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block19_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block19_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block19_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block19_out (Add)        (None, 14, 14, 1024  0           ['conv4_block18_out[0][0]',      \n",
+      "                                )                                 'conv4_block19_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block20_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block19_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block20_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block20_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block20_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block20_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block20_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block20_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block20_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block20_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block20_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block20_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block20_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block20_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block20_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block20_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block20_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block20_out (Add)        (None, 14, 14, 1024  0           ['conv4_block19_out[0][0]',      \n",
+      "                                )                                 'conv4_block20_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block21_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block20_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block21_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block21_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block21_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block21_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block21_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block21_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block21_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block21_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block21_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block21_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block21_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block21_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block21_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block21_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block21_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block21_out (Add)        (None, 14, 14, 1024  0           ['conv4_block20_out[0][0]',      \n",
+      "                                )                                 'conv4_block21_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block22_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block21_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block22_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block22_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block22_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block22_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block22_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block22_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block22_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block22_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block22_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_conv (Conv2D)  (None, 14, 14, 256)  589824      ['conv4_block22_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block22_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block22_2_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block22_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block22_3_conv (Conv2D)  (None, 14, 14, 1024  263168      ['conv4_block22_2_relu[0][0]']   \n",
+      "                                )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block22_out (Add)        (None, 14, 14, 1024  0           ['conv4_block21_out[0][0]',      \n",
+      "                                )                                 'conv4_block22_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block23_preact_bn (Batch  (None, 14, 14, 1024  4096       ['conv4_block22_out[0][0]']      \n",
+      " Normalization)                 )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block23_preact_relu (Act  (None, 14, 14, 1024  0          ['conv4_block23_preact_bn[0][0]']\n",
+      " ivation)                       )                                                                 \n",
+      "                                                                                                  \n",
+      " conv4_block23_1_conv (Conv2D)  (None, 14, 14, 256)  262144      ['conv4_block23_preact_relu[0][0]\n",
+      "                                                                 ']                               \n",
+      "                                                                                                  \n",
+      " conv4_block23_1_bn (BatchNorma  (None, 14, 14, 256)  1024       ['conv4_block23_1_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block23_1_relu (Activati  (None, 14, 14, 256)  0          ['conv4_block23_1_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_pad (ZeroPaddi  (None, 16, 16, 256)  0          ['conv4_block23_1_relu[0][0]']   \n",
+      " ng2D)                                                                                            \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_conv (Conv2D)  (None, 7, 7, 256)    589824      ['conv4_block23_2_pad[0][0]']    \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_bn (BatchNorma  (None, 7, 7, 256)   1024        ['conv4_block23_2_conv[0][0]']   \n",
+      " lization)                                                                                        \n",
+      "                                                                                                  \n",
+      " conv4_block23_2_relu (Activati  (None, 7, 7, 256)   0           ['conv4_block23_2_bn[0][0]']     \n",
+      " on)                                                                                              \n",
+      "                                                                                                  \n",
+      " max_pooling2d_5 (MaxPooling2D)  (None, 7, 7, 1024)  0           ['conv4_block22_out[0][0]']      \n",
+      "                                                                                                  \n",
+      " conv4_block23_3_conv (Conv2D)  (None, 7, 7, 1024)   263168      ['conv4_block23_2_relu[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv4_block23_out (Add)        (None, 7, 7, 1024)   0           ['max_pooling2d_5[0][0]',        \n",
+      "                                                                  'conv4_block23_3_conv[0][0]']   \n",
+      "                                                                                                  \n",
+      " conv5_block1_preact_bn (BatchN  (None, 7, 7, 1024)  4096        ['conv4_block23_out[0][0]']      \n",
+      " ormalization)                                                                                    \n",
+      "                                                                                                  \n",
+      " conv5_block1_preact_relu (Acti  (None, 7, 7, 1024)  0           ['conv5_block1_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
+      "                                                                                                  \n",
+      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524288      ['conv5_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1225,7 +1809,10 @@
       " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
+      " conv5_block1_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block1_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block1_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1233,22 +1820,22 @@
       " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
+      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv5_block1_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block1_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_conv[0][0]',    \n",
+      "                                                                  'conv5_block1_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block2_preact_bn (BatchN  (None, 7, 7, 2048)  8192        ['conv5_block1_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
-      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
+      " conv5_block2_preact_relu (Acti  (None, 7, 7, 2048)  0           ['conv5_block2_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
-      "                                                                                                  \n",
-      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
+      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1048576     ['conv5_block2_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1256,7 +1843,10 @@
       " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
+      " conv5_block2_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block2_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block2_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1266,15 +1856,17 @@
       "                                                                                                  \n",
       " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block2_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
+      "                                                                  'conv5_block2_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
-      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
+      " conv5_block3_preact_bn (BatchN  (None, 7, 7, 2048)  8192        ['conv5_block2_out[0][0]']       \n",
+      " ormalization)                                                                                    \n",
       "                                                                                                  \n",
-      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
+      " conv5_block3_preact_relu (Acti  (None, 7, 7, 2048)  0           ['conv5_block3_preact_bn[0][0]'] \n",
+      " vation)                                                                                          \n",
       "                                                                                                  \n",
-      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
+      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1048576     ['conv5_block3_preact_relu[0][0]'\n",
+      "                                                                 ]                                \n",
       "                                                                                                  \n",
       " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1282,7 +1874,10 @@
       " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
       " n)                                                                                               \n",
       "                                                                                                  \n",
-      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
+      " conv5_block3_2_pad (ZeroPaddin  (None, 9, 9, 512)   0           ['conv5_block3_1_relu[0][0]']    \n",
+      " g2D)                                                                                             \n",
+      "                                                                                                  \n",
+      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359296     ['conv5_block3_2_pad[0][0]']     \n",
       "                                                                                                  \n",
       " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
       " ization)                                                                                         \n",
@@ -1292,92 +1887,36 @@
       "                                                                                                  \n",
       " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
-      " ization)                                                                                         \n",
+      " conv5_block3_out (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
+      "                                                                  'conv5_block3_3_conv[0][0]']    \n",
       "                                                                                                  \n",
-      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
-      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
+      " post_bn (BatchNormalization)   (None, 7, 7, 2048)   8192        ['conv5_block3_out[0][0]']       \n",
       "                                                                                                  \n",
-      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
+      " post_relu (Activation)         (None, 7, 7, 2048)   0           ['post_bn[0][0]']                \n",
       "                                                                                                  \n",
-      " flatten_1 (Flatten)            (None, 100352)       0           ['conv5_block3_out[0][0]']       \n",
+      " flatten_3 (Flatten)            (None, 100352)       0           ['post_relu[0][0]']              \n",
       "                                                                                                  \n",
-      " dense_1 (Dense)                (None, 12)           1204236     ['flatten_1[0][0]']              \n",
+      " dense_5 (Dense)                (None, 5)            501765      ['flatten_3[0][0]']              \n",
       "                                                                                                  \n",
       "==================================================================================================\n",
-      "Total params: 24,791,948\n",
-      "Trainable params: 1,204,236\n",
-      "Non-trainable params: 23,587,712\n",
+      "Total params: 43,128,325\n",
+      "Trainable params: 501,765\n",
+      "Non-trainable params: 42,626,560\n",
       "__________________________________________________________________________________________________\n"
      ]
     }
    ],
    "source": [
-    "from keras.layers import Input, Lambda, Dense, Flatten\n",
-    "from keras.models import Model\n",
-    "from keras.applications import ResNet50\n",
-    "from keras.preprocessing import image\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.models import Sequential\n",
-    "import numpy as np\n",
-    "from glob import glob\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# re-size all the images to this\n",
-    "IMAGE_SIZE = [224, 224]\n",
-    "\n",
-    "# add preprocessing layer to the front of resnet\n",
-    "resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
-    "\n",
-    "# don't train existing weights\n",
-    "for layer in resnet.layers:\n",
-    "  layer.trainable = False\n",
-    "  \n",
-    "  # useful for getting number of classes\n",
-    "classes = 12\n",
-    "  \n",
-    "\n",
-    "# our layers - you can add more if you want\n",
-    "x = Flatten()(resnet.output)\n",
-    "# x = Dense(1000, activation='relu')(x)\n",
-    "prediction = Dense(12, activation='softmax')(x)\n",
-    "\n",
     "# create a model object\n",
-    "model_resnet = Model(inputs=resnet.input, outputs=prediction)\n",
+    "model = Model(inputs=resnet.input, outputs=prediction)\n",
     "\n",
     "# view the structure of the model\n",
-    "model_resnet.summary()\n",
-    "\n",
-    "# tell the model what cost and optimization method to use\n",
-    "model_resnet.compile(\n",
-    "  loss='sparse_categorical_crossentropy',\n",
-    "  optimizer='adam',\n",
-    "  metrics=['accuracy']\n",
-    ")"
+    "model.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training: 7430\n",
-      "Test: 2323\n",
-      "Validation: 1858\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_ds_r, test_ds_r, val_ds_r = prepare_data('./plantvillage/color', img_size=IMAGE_SIZE, test_size=0.2, val_size=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1391,84 +1930,93 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/_h/ljwht4gd7lb99rm1hm78h7_00000gn/T/ipykernel_39241/1735889553.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
-      "  r = model_resnet.fit_generator(\n"
+      "/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_14470/2541214992.py:10: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+      "  r = model.fit_generator(\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "232/232 [==============================] - 297s 1s/step - loss: 0.6232 - accuracy: 0.8380 - val_loss: 1.2547 - val_accuracy: 0.7328\n",
+      "93/93 [==============================] - 197s 2s/step - loss: 1.1542 - accuracy: 0.8938 - val_loss: 0.2841 - val_accuracy: 0.9742\n",
       "Epoch 2/25\n",
-      "232/232 [==============================] - 277s 1s/step - loss: 0.4919 - accuracy: 0.8611 - val_loss: 0.8189 - val_accuracy: 0.8308\n",
+      "93/93 [==============================] - 202s 2s/step - loss: 0.1366 - accuracy: 0.9819 - val_loss: 0.5596 - val_accuracy: 0.9524\n",
       "Epoch 3/25\n",
-      "232/232 [==============================] - 299s 1s/step - loss: 0.6947 - accuracy: 0.8382 - val_loss: 0.5326 - val_accuracy: 0.8518\n",
+      "93/93 [==============================] - 208s 2s/step - loss: 0.0817 - accuracy: 0.9913 - val_loss: 0.7281 - val_accuracy: 0.9416\n",
       "Epoch 4/25\n",
-      "232/232 [==============================] - 306s 1s/step - loss: 0.6153 - accuracy: 0.8599 - val_loss: 1.1360 - val_accuracy: 0.7710\n",
+      "93/93 [==============================] - 213s 2s/step - loss: 0.0254 - accuracy: 0.9953 - val_loss: 0.2856 - val_accuracy: 0.9769\n",
       "Epoch 5/25\n",
-      "232/232 [==============================] - 311s 1s/step - loss: 0.5149 - accuracy: 0.8689 - val_loss: 1.3260 - val_accuracy: 0.7780\n",
+      "93/93 [==============================] - 216s 2s/step - loss: 0.0513 - accuracy: 0.9916 - val_loss: 0.7943 - val_accuracy: 0.9511\n",
       "Epoch 6/25\n",
-      "232/232 [==============================] - 313s 1s/step - loss: 0.6220 - accuracy: 0.8462 - val_loss: 0.8199 - val_accuracy: 0.8233\n",
+      "93/93 [==============================] - 219s 2s/step - loss: 0.0716 - accuracy: 0.9919 - val_loss: 0.4567 - val_accuracy: 0.9715\n",
       "Epoch 7/25\n",
-      "232/232 [==============================] - 318s 1s/step - loss: 0.6513 - accuracy: 0.8412 - val_loss: 1.1632 - val_accuracy: 0.7457\n",
+      "93/93 [==============================] - 221s 2s/step - loss: 0.0669 - accuracy: 0.9916 - val_loss: 0.5951 - val_accuracy: 0.9688\n",
       "Epoch 8/25\n",
-      "232/232 [==============================] - 320s 1s/step - loss: 0.5098 - accuracy: 0.8623 - val_loss: 0.8247 - val_accuracy: 0.8006\n",
+      "93/93 [==============================] - 222s 2s/step - loss: 0.0294 - accuracy: 0.9966 - val_loss: 0.3915 - val_accuracy: 0.9769\n",
       "Epoch 9/25\n",
-      "232/232 [==============================] - 323s 1s/step - loss: 0.5930 - accuracy: 0.8493 - val_loss: 0.4964 - val_accuracy: 0.8761\n",
+      "93/93 [==============================] - 223s 2s/step - loss: 0.0047 - accuracy: 0.9990 - val_loss: 0.5019 - val_accuracy: 0.9688\n",
       "Epoch 10/25\n",
-      "232/232 [==============================] - 324s 1s/step - loss: 0.5482 - accuracy: 0.8661 - val_loss: 0.8474 - val_accuracy: 0.8109\n",
+      "93/93 [==============================] - 224s 2s/step - loss: 0.0159 - accuracy: 0.9976 - val_loss: 0.5905 - val_accuracy: 0.9715\n",
       "Epoch 11/25\n",
-      "232/232 [==============================] - 322s 1s/step - loss: 0.5106 - accuracy: 0.8668 - val_loss: 1.2926 - val_accuracy: 0.7629\n",
+      "93/93 [==============================] - 225s 2s/step - loss: 0.0134 - accuracy: 0.9976 - val_loss: 0.3234 - val_accuracy: 0.9810\n",
       "Epoch 12/25\n",
-      "232/232 [==============================] - 322s 1s/step - loss: 0.5876 - accuracy: 0.8579 - val_loss: 1.0667 - val_accuracy: 0.7812\n",
+      "93/93 [==============================] - 227s 2s/step - loss: 0.1011 - accuracy: 0.9899 - val_loss: 0.5499 - val_accuracy: 0.9728\n",
       "Epoch 13/25\n",
-      "232/232 [==============================] - 323s 1s/step - loss: 0.6110 - accuracy: 0.8560 - val_loss: 0.5787 - val_accuracy: 0.8545\n",
+      "93/93 [==============================] - 225s 2s/step - loss: 0.0076 - accuracy: 0.9983 - val_loss: 0.4216 - val_accuracy: 0.9728\n",
       "Epoch 14/25\n",
-      "232/232 [==============================] - 323s 1s/step - loss: 0.5797 - accuracy: 0.8524 - val_loss: 0.6400 - val_accuracy: 0.8658\n",
+      "93/93 [==============================] - 226s 2s/step - loss: 0.0643 - accuracy: 0.9926 - val_loss: 0.8745 - val_accuracy: 0.9511\n",
       "Epoch 15/25\n",
-      "232/232 [==============================] - 326s 1s/step - loss: 0.4589 - accuracy: 0.8759 - val_loss: 0.6950 - val_accuracy: 0.8400\n",
+      "93/93 [==============================] - 226s 2s/step - loss: 0.0199 - accuracy: 0.9966 - val_loss: 0.4947 - val_accuracy: 0.9715\n",
       "Epoch 16/25\n",
-      "232/232 [==============================] - 324s 1s/step - loss: 0.5822 - accuracy: 0.8700 - val_loss: 1.4940 - val_accuracy: 0.7678\n",
+      "93/93 [==============================] - 226s 2s/step - loss: 5.7203e-04 - accuracy: 0.9997 - val_loss: 0.4923 - val_accuracy: 0.9810\n",
       "Epoch 17/25\n",
-      "232/232 [==============================] - 322s 1s/step - loss: 0.4404 - accuracy: 0.8827 - val_loss: 1.5049 - val_accuracy: 0.7559\n",
+      "93/93 [==============================] - 227s 2s/step - loss: 0.0131 - accuracy: 0.9970 - val_loss: 0.6881 - val_accuracy: 0.9647\n",
       "Epoch 18/25\n",
-      "232/232 [==============================] - 321s 1s/step - loss: 0.6142 - accuracy: 0.8598 - val_loss: 0.8974 - val_accuracy: 0.8060\n",
+      "93/93 [==============================] - 225s 2s/step - loss: 0.0345 - accuracy: 0.9960 - val_loss: 0.4938 - val_accuracy: 0.9823\n",
       "Epoch 19/25\n",
-      "232/232 [==============================] - 322s 1s/step - loss: 0.5486 - accuracy: 0.8677 - val_loss: 1.5655 - val_accuracy: 0.7753\n",
+      "93/93 [==============================] - 228s 2s/step - loss: 0.0126 - accuracy: 0.9987 - val_loss: 0.5642 - val_accuracy: 0.9688\n",
       "Epoch 20/25\n",
-      "232/232 [==============================] - 326s 1s/step - loss: 0.3964 - accuracy: 0.8947 - val_loss: 0.7896 - val_accuracy: 0.8292\n",
+      "93/93 [==============================] - 226s 2s/step - loss: 0.0056 - accuracy: 0.9997 - val_loss: 0.4294 - val_accuracy: 0.9783\n",
       "Epoch 21/25\n",
-      "232/232 [==============================] - 324s 1s/step - loss: 0.4499 - accuracy: 0.8848 - val_loss: 1.7746 - val_accuracy: 0.7150\n",
+      "93/93 [==============================] - 225s 2s/step - loss: 2.7678e-05 - accuracy: 1.0000 - val_loss: 0.4342 - val_accuracy: 0.9783\n",
       "Epoch 22/25\n",
-      "232/232 [==============================] - 323s 1s/step - loss: 0.4320 - accuracy: 0.8817 - val_loss: 1.2487 - val_accuracy: 0.7974\n",
+      "93/93 [==============================] - 226s 2s/step - loss: 5.2474e-07 - accuracy: 1.0000 - val_loss: 0.4337 - val_accuracy: 0.9783\n",
       "Epoch 23/25\n",
-      "232/232 [==============================] - 322s 1s/step - loss: 0.4307 - accuracy: 0.8844 - val_loss: 0.6485 - val_accuracy: 0.8470\n",
+      "93/93 [==============================] - 227s 2s/step - loss: 4.2286e-07 - accuracy: 1.0000 - val_loss: 0.4334 - val_accuracy: 0.9783\n",
       "Epoch 24/25\n",
-      "232/232 [==============================] - 322s 1s/step - loss: 0.4287 - accuracy: 0.8900 - val_loss: 1.5260 - val_accuracy: 0.7586\n",
+      "93/93 [==============================] - 227s 2s/step - loss: 3.5546e-07 - accuracy: 1.0000 - val_loss: 0.4332 - val_accuracy: 0.9783\n",
       "Epoch 25/25\n",
-      "232/232 [==============================] - 323s 1s/step - loss: 0.6704 - accuracy: 0.8482 - val_loss: 0.7494 - val_accuracy: 0.8287\n"
+      "93/93 [==============================] - 227s 2s/step - loss: 3.0831e-07 - accuracy: 1.0000 - val_loss: 0.4024 - val_accuracy: 0.9796\n"
      ]
     }
    ],
    "source": [
-    "r = model_resnet.fit_generator(\n",
-    "  train_ds_r,\n",
-    "  validation_data=val_ds_r,\n",
+    "# tell the model what cost and optimization method to use\n",
+    "model.compile(\n",
+    "  loss='sparse_categorical_crossentropy',\n",
+    "  optimizer='adam',\n",
+    "  metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "#train_ds_vgg_sw, test_ds_vgg_sw, validation_ds_vgg_sw\n",
+    "# fit the model\n",
+    "r = model.fit_generator(\n",
+    "  train_ds_v,\n",
+    "  validation_data=val_ds_v,\n",
     "  epochs=25,\n",
-    "  steps_per_epoch=len(train_ds_r),\n",
-    "  validation_steps=len(val_ds_r)\n",
+    "  steps_per_epoch=len(train_ds_v),\n",
+    "  validation_steps=len(val_ds_v)\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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yCwBg8eLFGD16NIYMGYLGxkZ8/fXXGDRoEADgxRdfRHJyMkaOHAmj0YjPP/8cSUlJiI6O9nlNBEEQktNWgMK9UCpzgeZGwGxVfl2E11CAoiIXX3wxYmNjceTIEcyaNavFbS+++CJuvvlmnHvuuYiLi8ODDz6Iqqoqv/b3zDPPwOFw4MYbb0R1dTXGjBmDH374ATExMQAAi8WCRYsWITs7GyEhITj//PPxySefAAAiIiLw7LPP4tixYzCZTBg7diy+/fZbUTNDEAShOvZmNosHaBmghCcAlgjAVg2cyQbiB6ixOsJLDILgYW+silRVVSEqKgqVlZWIjIxscVtDQwOysrLQq1cvBAcHq7RCwh/of0gQhCTUlADP9wVgABaXAUaT67b/uwAo2Adc/xEw8ArVltjV6Oj43Rl0+ksQBEEEBry8ExLTMjgBSIeiQyhAIQiCIAKDulJ26V7e4cRSJ4/eoACFIAiCCAzaEshyxJk8J5VbD+EXFKAQBEEQgUGHAQplUPQGBSgEQRBEYNB6Do87fB5PdQHQWKPcmgifCZgARQfNSEQ70P+OIAhJaGsODyc0FghxBi5U5tEFug9QgoKCAAB1dXUqr4TwFW7fbzKZOrknQRBEB3RU4gHcdCjUyaMHdG/UZjKZEB0djeLiYgBAaGgoDAaDyqsiPMXhcKCkpAShoaEwm3X/diQIQk08CVBO7yAdik4IiCNCUlISAIhBCqEvjEYjevToQYElQRD+0WmA4tShlFGJRw8ERIBiMBiQnJyMhIQENDU1qb0cwkssFgtZ5hME4T+elngog6ILvA5QNm7ciOeeew67d+9GQUEBVq1ahRkzZnT4mMbGRjzxxBP48MMPUVhYiOTkZCxevBg333yzr+tuE5PJRDoGgiCIroookm1nwjo3ayMNii7wOkCpra1FRkYGbr75Zlx99dUePea6665DUVER3nnnHfTt2xcFBQV+TeYlCIIgiBY0NQA2Z/twexkU3mpcVwbUn2GW+IRm8TpAmTZtGqZNm+bx/b///nv88ssvOHnyJGJjWVSbnp7u7W4JgiAIon3qndkTgwkIjmr7PtZwICKZeaGUnQRSRyu3PsJrZC/8r1mzBmPGjMGzzz6L7t27o3///rjvvvtQX1/f7mMaGxtRVVXV4ocgCIIg2sVdf9KR4J50KLpBdpHsyZMnsXnzZgQHB2PVqlUoLS3FnXfeibKyMrz33nttPmbp0qV4/PHH5V4aQRAEESh0JpDlxPYGsjeRDkUHyJ5BcTgcMBgMWLFiBcaNG4fLL78cL774It5///12syiLFi1CZWWl+JObmyv3MgmCIAg942mAQhkU3SB7BiU5ORndu3dHVJSrJjho0CAIgoDTp0+jX79+Zz3GarXCarXKvTSCIAgiUOisg4cjDg2kDIrWkT2DMnHiROTn56OmxjWc6ejRozAajUhNTZV79wRBEERXwOsMygmA5oBpGq8DlJqaGmRmZiIzMxMAkJWVhczMTOTk5ABg5Zk5c+aI9581axa6deuGefPm4eDBg9i4cSPuv/9+3HzzzQgJCZHmryAIgiC6Np4GKDHpgMEI2KqB2hLZl0X4jtcByq5duzBy5EiMHDkSALBw4UKMHDkSixcvBgAUFBSIwQoAhIeHY+3ataioqMCYMWMwe/ZsTJ8+Ha+88opEfwJBEATR5fE0QDFbgag0tk06FE3jtQZl0qRJEDpIiy1fvvys6wYOHIi1a9d6uyuCIAiC8AxPAxSA6VAqTrEyT89z5V0X4TM0AIUgCILwnbpyYMO/gDPZKq/DmwCFOnn0AAUoBEEQhO/s/S+w4Wlg87/VXYenXTwAzeTRCRSgEARBEL5TfpJdVuR0fD85EQQfMygUoGgZClAIgiAI36k8zS6ri9RbQ1Md0NzAtsPiOr9/N+fQwPKTAA2u1SwUoBAEQRC+U5nHLqsL1FsDz56Yg4Gg0M7vH9UDMAaxoKYqT961ET5DAQpBEAThOzyDUl8ONDeqswZPBwVyTGbmhwKQDkXDUIBCEARB+EZDJTM849SoVOYRAxQPBLIc6uTRPBSgEARBEL7BsycctXQoYgePBwJZjjiT56T06yEkgQIUgiAIwjcqW+k3agrVWYc3HTwcMUChDIpWoQCFIAiC8I3K3Ja/V+soQCEvFM1DAQpBEAThG607YPQUoHANyplswN4s+ZJkJ2sj8NJw4PhPaq9ENihAIQiCIHyDa1CsUexSTwFKRDJgDgEczWwuj9449DVb98E1aq9ENihAIQiCIHyDa1C6j2KXamlQan3o4jEa3XQoOizz1BazS7U6pxSAAhSCIAjCN7gGJXUMu9RTBgUAYp2OsnoUytaWsks1DfJkhgIUgiAIwnscDqAqn21312mAwnUoehTK1jgzKGqOGJAZs9oLIAiCINqgoYrNimnxk8WyFZc9qfbqWInB0QQYjEDKCHZdXSlgbwJMQcqtw9tBge7oudW4tsR5WQw47IDRpO56ZIACFIIgCLWoP+MKPHgQUnaCXdaVtv2YnF+BiXd7NhRPTrj+JCIZCEsAjGYmOK0pAqJSlVtHQyUg2Nl2iBcaFMDNTVZnZm32JjZaAAAEBwtWIpLUXZMMUIBCEAQhJ/ZmoCDTFXi4//CDTHuEJTCdRGxvNoF362vsMWdOaSBAcepPolKZ4DQ8Cag6zUoOSgYoPHtiCQeCgr17LPdCqcwFmhq8f7xa8L+ZU11IAQpBEAThJV/MAw510AoanuQKQGJb/VgjWt732E9A7jbWXpo6Wt51dwb3QInszi4jEp0BisKiTdHm3svsCcCCPGsU0FgJnMkCEgZJuza54PoTjlraH5mhAIUgCEJOcnewy+5jgMTBbgFIHzZR1xru+XPF9HQGKDmyLNUruAcKz5ZEJLNLpVuNfdWfAGzycbfeQP5epkPRS4DC9Scctdq7ZYYCFIIgCLlw2F0Hk+tX+J+Gj+7BLrVgLNY6QAlPZJdKn837E6AATIeSv1dfXiitA5QAzaBQmzFBEIRc1JU5BZwGIFQCzYgYoGg4g6K3ACVWh508FKAQBEEQfsEPHGHxgEmChHV0T3Z5RkMZFHcNCqC8s6kUGRSAiZb1Ag9QgsLYZYC6yVKAQhAEIRdczMgP3v7CMyiVucz/Qy2aG11W61Fp7DLcWb5SXCTrg829O9106CZb4wxQEoewywB1k6UAhSAIQi64eDFcogAlKpUZozU3qHvWzDt4zMGuwIDra5R2NhW7ePws8dQUAY3V0qxJbngGJWkYuwxQN1kKUAiCIOSCl3jCJfKoMAW5Sipq6lC4SVtUKuuEAVwBSm0J835RCn9LPCHRLn2QXoSyPHvFA5SaIibIDjAoQCEIgpALnuWQqsQDaEOH0lp/ArCDvMEEQHAdQJXA3wAF0N9MHj4oMHEIAAMTYrc2bwsAKEAhCIKQCx6gSFXiAbTRalzFO3jSXNcZjW6txgpqIqQMUPSQQREEV4knIpkJsIGA7OShAIUgCEIuqmUIUGKcGRQ1A5TWLcYcpXUoDjubZwT4GaBwoawOApSGSsBuY9th8a7sHAUoBEEQhMdwkayUc1K04IUialC6t7ye/51KOZvWVwBwdjOFxPj+PGIGRQedPDx7Yo1ks4PUcvBVAApQCIIg5EAQXG3G4QnSPa+WNCitMyhKu8ny8k5wFBMQ+wrv5NGDBoUHKHxYpFoOvgpAAQpBEIQcNFYDTXVsW6ouHsDNC+W0ep0b4qDA1iUehd1kpdCfAGw2EsDKRXWdTJhWGx70hjmDXrUcfBWAAhSCIAg54AJZayRgCZXueSNTAKMZcDSpY9DVUAk0VrHts0o8KmVQ/A1QLKGujiSt61BaZ1DUcvBVAApQCIIg5EDs4JGwvAMARpOrtKKGDoXrT0JiAEtYy9uU1kNIFaAAQDedzOThAQp/X6nl4KsAFKAQBEHIgdQmbe6oqUNpT38CqKdBkSJA0YsORcygONuLxRIPZVAIgiAIT5DDpI2jZicP90BprT8BXAfL2hJl9DH+zuFxRy+dPKIGhQcovMRTCDgc6qxJJihAIQiCkAOxxCNDBkVNL5SOMihhcWxWkOBwnenLib9zeNwRSzxaz6A4XWR5gMKzVo5moF7jAl8voQCFIAhCDqpl0qAArhKPmhqU1gJZgOljeHeJEpoISTUobm6yak6K7ozaVq3rpiDXLKEA6+TxOkDZuHEjpk+fjpSUFBgMBqxevdrjx27ZsgVmsxkjRozwdrcEQRD6Qg6TNk60FjIoaW3frqSbrJQBSnRPlv1pqtV2R0zrDArg9pp38QCltrYWGRkZWLZsmVePq6iowJw5c3DJJZd4u0uCIAj9IYfNPUf0QslTdnIw4KZBaSODArgdLHWWQTFbXIGfVnUoTQ2uFu+2ApQAc5M1e/uAadOmYdq0aV7v6Pbbb8esWbNgMpm8yroQBEHoEjkGBXLCEwGTFbA3MtM0rkmRG4fDrcTThgYFcDtYKpFB4RqUOGmer1sf4EwWK/OknyfNc0oJ1/WYLMw9lxOgrcaKaFDee+89nDx5EkuWLPHo/o2NjaiqqmrxQxAEoRuabS7BohwlHqMRiHaWWJQs89QWM4M4g9HVsdMapQ6W9iagsZJtS9HFA2i/k6fWrYPHYHBdr/SQRoWQPUA5duwYHnroIXz44Ycwmz1L2CxduhRRUVHiT1paO7VOgiAILcKzB8Yg/4bYdYQarcY8exKRDJja+T5X6mDJsycGIxAcLc1zil4oJ6V5PqkR9SetMkYBWuKRNUCx2+2YNWsWHn/8cfTv39/jxy1atAiVlZXiT25uroyrJAiCkBhxSGBiyzNdKVHDrK3S+V3cnv4EUO5gWec8WIfEsoySFGjdTbb1HB5OgA4M9FqD4g3V1dXYtWsX9u7diwULFgAAHA4HBEGA2WzGjz/+iIsvvvisx1mtVlitVjmXRhAEIR9iB48M+hOOGhmUqk70J4ByB0spBbIcHqCUZzGjOaNJuueWgtYuspwAdZOVNUCJjIzE/v37W1z32muvYf369fjiiy/Qq1cvOXdPEAShDnLa3HPUMGvryKSNI87jKZb3IC9HgBKVxgSo9kb2tyolPvYUcQ5P6wDFzU1WEOTL2imM1wFKTU0Njh93pb+ysrKQmZmJ2NhY9OjRA4sWLUJeXh4++OADGI1GDB06tMXjExISEBwcfNb1BEEQAUNNKzMtOVDDrM2TACUsHoABEOxMMyFXFklKm3uO0QTE9AJKj7CZPFoNUFpnUHjWym4D6s9I+5qoiNeFu127dmHkyJEYOXIkAGDhwoUYOXIkFi9eDAAoKChATo4K7oYEQRBaQU6TNg4PUKrygeZG+fbjjicBisnsCszk1KFIaXPvjrujrNZoPYeHY7YyLQ4QUDoUrwOUSZMmQRCEs36WL18OAFi+fDk2bNjQ7uMfe+wxZGZm+rhcgiAIHSCnSRsnLA4ICgUguAIHueEalI5EsoAyOhQ5SjwA0K03u9RigNKWiyxHSYM8haBZPARBEFIjp0kbx2BwE8oqoENpbnT9Xe3Z3HNE0aYeAxQNe6HUtpNBAZQ1yFMIClAIgiCkhh8k5OziAZTt5KnKZ5fm4M41DqJoU8aDpVwBiuiForEMisPu+pvb0jYFoJssBSgEQRBS4nC4ZVBk1KAAynqhuOtPOusSUeJgKXcG5cwp5gisFerKAcHBttv6mwPQTZYCFIIgCCmpPwM4nAP85OziARTOoHioPwGUOVjKJZKNSAKCwlgXkhrTotuDd/CExAKmoLNvD0A3WQpQCIIgpIQfIEK7tX0gkRIlvVC4i2xn+hNAGcGmHG3GAMsOaVEo216LMScA3WQpQCEIgpASJUzaOEpmUMQpxl5kUOTSoNjqgKY6ti11BgVw6VC0JJQVTdraycopIUxWGApQCIIgpEQpgSzg0qDUFAFN9fLuyxMPFE64W4DicEi/Fj4p2hgEWCOkf36uQ9GSUFbMoMS1fXuEWwZFEJRZk8xQgEIQBCElSrQYc0JiAIvzAF0h81BVbzQo4QkADEyLw0sxUuIukJXD1l2LQwPbGxTI4UGhvRFoqFBkSXJDAQpBEISUKGHSxlHSC0XMoHigQTEFuc705RBtytXBwxG9UE7K8/y+0JkGJSgYCI5m2wFS5qEAhfCcmhJ50rUEEUgoYXPvjhJC2YZKoLGKbXuiQQHcWo3lCFB4B49MM2e4BqXqNNO7aIH2BgW6EyHja64CFKAQnrF3BfB8X2DnW2qvhCC0jRKDAt1RQijLBbIhMYAlzLPHyHmwlDuDEhrrykacyZJnH97SWQYFCDg3WQpQiM4RBGDzi2z7xHp110IQWkfJLh5AGbM2Xt6J9EAgy4mQse1V7gDFYNCeDqWGBygdBL4B5iZLAQrROdmbXB9SrXxYCUKriF08SgUoCmRQqrzo4OHwtlc9alAAbU01FoTOu3iAgHOTpQCF6Jxd77q2z2QD9ibVlkIQmqaxBrDVsG2lSjxKaFC8aTHmyGkcpkSAInqhaCBAsdUAzc428o7eVwE20ZgCFKJjaoqBQ1+zbYOJtQ2eyVZ1SQShWXj2JChMHn+OtuAZlLoyFiDJgTcmbRw5jcMUyaBoaGggz54EhXasAQpXYEijglCAQnTM3g8BRxPQfQyQOIRdV3pM3TURhFZR0qSNExzlEnTKVebxpsWYI6dgU+4uHkBbGpQaDwSyQMC5yVKAQrSPwwHsfo9tj7kZiOvHtssoQCGINlFqinFr5NahcA2KJyZtnHAZnU2VLPHUlrA2azXxpIMHCDg3WQpQiPY5sZ594QVHAUOuAro5AxTKoBBE24gmbQrpTzhy6lAcDrcSjw8aFEeTK+MhBYKgTIASHOn6G9TWodR62LrOA+PmepdvjY6hAIVoHy6OzZgFWELdMigaSHkS3rPuCeC5vkAp/f9kQ2mTNg5vNZYjg1JbwoIMg9FVQvAEs8UVQEgp2rTVAHYb25YzQAFcWZRylR1la0vZZUcdPAD7nrZGse0AKPNQgEK0TWUecPQ7tj1mHrvkbXeUQdEnv33ODja8bEdIj5I29+6IXijZ0j83159EJAMms3ePlaPVmGdPzCHsgCwnWtGhdDaHxx05/WcUhgIUom32/hcQHEDP84D4Aew6HqDUlQL1Z9RbG+E9DVVApfPs+vdVNLJALpQcFOiOnBoUX/QnHDlajZUo73C6aaTV2FMNChBQbrIUoBBnY28Gdr/Ptnn2BACs4UBECtumMoG+KDns2q7KA07vUG8tgYwaXTyAvBoUXzxQOHJ0lSjRwcMRzdpU/r7zZA4PJ4DcZClAIc7m2A9AdT47Qxk0veVtcfwDS2UeXVF8sOXvB1aqs45AR2mbew5v/22oBOorpH1uXzxQOBEy+HIomUGJdfNCUbMrxpcMSgC4yVKAQpwNF8eO/DNgtra8jTp59EmRM0CJH8QuD64GHHbVlhOQ2JtcB0+lSzzWcCDUKaCUusxTmcsuvfFA4chxNq9ogNILgIEFfny/auCVBoUyKESgUp4FHF/HtkfPPft28kLRJzyDcs7tzNSrpgg4tUXVJQUctSUABMBoVubg2Rq5dChVzgyKLxoUOc7mlQxQgkJcpS21dCjNNqChgm17kkEJIDdZClCIlux5H4AA9LkYiO199u1iBoU0KLqi+BC7TM5wle2ozCMtvLwTlgAYVfhqlUuH4pcGhQcoOhXJAup38tQ5W4wNJiAkpvP7B5CbLAUohItmG7O2B5hzbFtwDUr5SSoR6IWaYueXnAGIGwAMvZpdf2gNDX6UkhoPzbTkQo4MSnOj60zcnwClRkJnU+4JooRIFmipQ1ED9ynGngS+cgSFKtG1A5QfHwFeGg4cXKP2SrTB4a/ZhyEiGeg/te37RKUBJitgb5R3vDshHby8E9ub+UakX8D0CnVlQNYv6q4tkFDLpI3DA5QzEmZQqvLZpTnYt4wFLzfYbdJZE4hdPEplUFTu5PF0Dg+Hv+ZNtUBjtTxrUoiuHaDUlbN0aNHvaq9EG4ji2BsBU1Db9zGa1E95Et7ByzsJToGsyQwMvpJtH1ilzpoCEbVM2jjR6exSyhMHd/2JweD9481WV1lCqjN6XuLpzFVVKsTvO5XcZL3p4AGYYNrinKSt8yxK1w5Q+HTeogPqrkMLlBwFsjcxO+tRczq+LznK6gsegCcMdl3HyzyH/8fS+IT/qGXSxnEv8UhVTvFHf8KR2k1WcQ0KL2ur1GrM5/B4GqAAAeMm27UDFP6F3dojoiuyezm77DcFiO6knZA6efQFz6AkugUoPSawFtCGSjYUkvAftUzaOPxza6uWrpziT4sxR0o3WYcDqFe4xBOVBsAANNW5shlKIpq0eaFtEoNCfXfydO0AhWdQyrMAW626a1GTpnogcwXbbk8c6w55oegHh8PlIuueQTGa2IRqgLp5pEItkzZOUIgrGJCqk8cfkzaOlF0lDRVsBAcAhCgkkjVbXC3WUup7PKXGTSTrKWJQqG8vlK4doIQnONNmQksr8K7Gwa/YBz+qB9D3ks7vT1ON9UNlDpv+arKc3TbOyzxHvmVBKuEfYgZFpQAFcBsaKFWAIkWJR8IMChfIWiNZ4KAUco4S6AxRg+JNBiUwOnm6doACuM4qu7JQlotjR9/Ezqw7g9dkqwt0rxIPeHh5J27A2cLn1LEsfW2rAY79qPzaAglBcNOgqNRmDEjfauyPSRtHSg2KqD9RKHvCkXNadGf4pEGhACUwEIWyXVSHUngAyN3O3C9H3ujZY0KiXR8WyqJoG66v4h087hgMVOaRivozrJUWUE8kC0h/pi9mUDSiQVFaIMsRAz81MihO3xdPBgVyeJmRNCg6RxTKdtEMyu732OXAK7wT95GjrD4o6iBAAVxlnqM/AI01yqwpEOEHgpCYs+dXKYmUGZSGSqCxim37pUGR8GxerQAlRuLSmacIgvdtxgBlUAIGMYPyu7rTKtWgsQbY9ynb9kQc6w5NNdYHYgfPkLZvTx4BxPQCmuuBo98rtqyAQ+0WY46UGhQukA2JASxhvj9PhNvZvL/fsaplUHhmSmFzyvozgKOZbVOA0jkbN27E9OnTkZKSAoPBgNWrV3d4/5UrV+LSSy9FfHw8IiMjMWHCBPzwww++rld64gcCMLA3Preq7ioc+IK1JMb2Ye6i3kCdPNrH3gSUHmXb7WVQDAZg6DVsm8o8vqO2SRtHSi8UUX/ih0AWcJUbmhtcQ+98Re0MSuVpZUd88PKONcq7zBwPUGzVus6Meh2g1NbWIiMjA8uWLfPo/hs3bsSll16Kb7/9Frt378ZFF12E6dOnY+/evV4vVhYsoa7uhq5W5uHi2DHzvB9uRl4o2qfsBOBoAizhHWsIeJnn+FqW1ie8R22bew737Giu99+zQ/RA8TNACQpmE7QB/6caizb3CotkI5IBYxD7PHH7fyXgAllv9CcAYI0AgpxZLx3rUMzePmDatGmYNm2ax/d/6aWXWvz+9NNP46uvvsL//vc/jBw50tvdy0PiYOYSWHSQTfHtCuTtAQr2sbk6GbO8fzzPoJSdYF4bakxvJTqGB9wJgzq2KU8YzLp8So8Ah78BRvjwfujqqD0okGO2AJEpLPtRkePfeqTwQOFEJLHsSXUBkDDQ9+dRK4NiNDEjvPKTTCjbmZmlVPiiP+FEJLL1Vhe67Pp1huJHFYfDgerqasTGKhwBd0TiUHbZlVqNefZkyAwgzIcPe0xP1vnTVAdUK3hGQXiOOINncMf3MxhcWRQq8/iG2iZt7kjVEiuFBwonQqKuErUCFEB6jxlP8HZQoDtSjxhQAcUDlOeffx41NTW47rrr2r1PY2MjqqqqWvzISlfr5KmvAA58ybZHz/PtOUxBTFwJkA5Fq3gaoADAEGeAcvJnVxqd8BwtmLRxpOrkkUqDArgCN3+dTVUNUFRoNfYngyJle7dKKBqgfPTRR3j88cfx2WefISGh/dTj0qVLERUVJf6kpcmcTuMdDiVHlBVAqcVvn7HMR/wgoMc5vj8POcpqmyK3Ek9nxPcHEoexjoFDa+RdVyCiBZM2jlReKFJpUAA3N1kdZ1DUaDWu9aN0KOWIAZVQLED55JNPcOutt+Kzzz7D5MmTO7zvokWLUFlZKf7k5ubKu7iYdCAolKnMy1Uaqa0UguAmjr3ZtxHqHJpqrF1sta4Uf3stxq0ZSqZtPiN28QRIBsXhcIlBJdGgSFBusDe7uoDULPEomkFxdvF4M4eHEwATjRUJUD7++GPMmzcPH3/8Ma644opO72+1WhEZGdniR1aMJme7MYCiA/LuS21ytgElh1hAljHTv+eiTh7tUnIEgMBSw55+ufEyT/amrtdy7w9N9UCjs/tJrUnG7kihlagtYc64BqMruPAHKcoN4oRmg6srSEli0tmlkl4o/HPozRwejugm24UClJqaGmRmZiIzMxMAkJWVhczMTOTksH/aokWLMGfOHPH+H330EebMmYMXXngB48ePR2FhIQoLC1FZqbF2Rj6KPtAt77lz7NBrgOAo/56L3GS1S0cW9+0R2wtIGcWmxR78Sp51BSL8oGsOYUPs1IZnUCpzWSbEF6qcAtnwpLNnOPmCFOUGXt4JiQZMXjeg+g8P/KrygeZGZfbpVxcP1/3ot83Y6wBl165dGDlypNgivHDhQowcORKLFy8GABQUFIjBCgC8+eabaG5uxvz585GcnCz+3H333RL9CRKR4EyDFwdwgFJbBvy+mm176xzbFjyDUplL03C1hiiQ9bC8w6FuHu9xbzH2p2QqFZHdAYOJZUB8PXuWsoMHaFlu8NVATk39CcAykUGhAATX6yM3PEDxSYOifzdZr8PQSZMmQejgDbZ8+fIWv2/YsMHbXaiDu+V9oLLvI8DeyOzNu4/y//lCu7FUa0MF80NJGur/cxLS4EsGBWDDA398BMjZynwwpNAfBDpaMWnjmMzs/1aRw34iU7x/Dik9UAA3N9l6Nt/Hl+yt2gGKwcCyKCWHmL5Lbm8RWx2bNA74qEFxvuaNley5LKHSrU0hyF2LwwOUM1m6tgZuF4cD2OUs74zxsbW4NQYD6VC0ijgk0IMWY3eiUoG0cwAIwMHVUq8qMNGKzb07/upQpM6gWEKZXTvg+xm92gEKIP206I7g2ROT1bfSoTWSlR0B3epQKEDhhMW5hEglh9Vdixxkb2RuuZYIYOi10j2v2jqUvD3Ake/U2bdWqSt3fSH54tpJZR7v0MqgQHf8HW4nthhLaPHgb1eJGKCoaPLJ9T1KtBqLHTzxvpUODQbp2rtVggIUd0ShbACWeXhrccZMwBou3fOqOdXY4QA+ug74+HqgOACDSl/h+pPoHmwmh7cMnsG6N/J2+e9G2hUQSzxaClD8NBUTTdokLPH56yYrzuFRMYOiZKuxr3N43NG5mywFKO4EquV9dRGbsQL47hzbHmpONS496kqDntqs/P61SrGP5R1ORCLQcyLb/n2VNGsKZLTkgcLxtxQhdYkH8N9NVlMlHgVajf3p4OHo3E2WAhR3RMv7AOvk2ftf5hCaNl56Iau7m6y/4929JXe72/YOZfetZXwVyLpDZR7P0WSJxw+ztuZG198kZYDib9urFgIUJefx+OOBwtG5mywFKO64l3iUPtjKhcMO7H6fbUvRWtyamF4ADEyZr7S512m3oMQ9WOnq+Npi7M6gK1mrauFv5HPTGeIcHi0FKM4DaeVp78d3cAdZc7C0wUBEAGVQ6krlb6bwx0WWo3M3WQpQ3IkfyGrv9eX+T93UCsfXAZU5QEgMMPhK6Z8/KNh1tqa0DsU9a3ImW7dCMEkRBGkyKGHdgN6T2PbvlEVpF4fdzatCQyWeiCTAGMQyp1VeTht3159I6evitwZFAwFKcJTLxVbuMg/XoPhV4tG3mywFKO4EhQCxzt72QLG8F8Wxs9jfJwdxKuhQ6sqZBgVwdRpQFoUdjBoqWfaD/198hco8nVNbypx3DUb/znSlxmgCop2fC291KHLoTwAJNCgaEMkCyrUa+2PSxtG5mywFKK0JJMv7yjzg2A9sWyrvk7bopsJU49O7nPvuC/S7lG1TgOIq78T1A8xW/55r4B/YWXjJocD4PMgBPzMNi2dBgZbwVYciV4Diz8GyuRGwVbNtNduMAeV0KDVcJOtPicfPoFBlKEBpTSBZ3uduY2d3KaP8P5vuiDgVphpz/UnqOCb+BUgoC0hT3uGERAN9nZPHqczTNlo0aeP4eiCVLYPifI2aaoHGau8ey8s7BpPL8E0t/G3h9hSxi0eCDEpDBdDU4PeSlIYClNYEkuU9/2KSMzgB3DIoCgYoPFuSNo79AEBBpi4/hJLib4txa9zLPIEiHJcSnkHRZIDiYwZFDg8UgPkvWZy+PN6KNt1N2owqH7b4VGM5Myj2Ztff7I8GJTiaOdECutShUIDSGl7iKTnC3iR6hkf4/ExKLngAdOYU0GyTd18A+7/k7WHbaeNYJ1FYAhuOVpAp//61jNQByoBprJuj/ATr6CFaosUOHg4/kGpFgwL4PsBOCwJZjr8uvZ5QXw5AAGDw72/WuZssBSitiU4HgsLYUL3yE2qvxj/4ByhG5gAlIhmwhAOCnc0ykpvig2yIljXS2XllcGVRurIOxWFngTUgTYkHYE60/S5j2ySWPRstmrRxfNag8EGBFKC0ibtIVq6sIrdsCI1lwx/9QcdushSgtMZodM0v0XuZh6cg+ReVXBgMrsmeSuhQuP6k+2iXMJF0KEB5FtDcwAaE8bNnKeBlnt+pzHMWWptk7A4/06/KA+xNnj2moYpNvwWkL/EAbq3G3gYovINHZYEs4Po+bawC6s/Isw8p9CccHbvJUoDSFokBIJR1OFwDv+Qu8QDK6lB4EMKDEgDocQ67zNnWdQ+i/P0aP0DajpJ+U1hWsSIHyNst3fMGAvxM159WULkIT2DlOcHhKtt0BtefBEdLO7NLXJOPB0stZVCCQlx/h1xC2VoJOng4OnaTpQClLRICQChbU8g0GQaTPGdCrRG9UBRoNRYDlLGu65IzAJOFOTyWn5R/DVqEtxgn+uEg2xaWUKZFAajM0xr+pa/FEo/B4H3Hiag/kXCKsTuBUOIB5G81lsIDhaNjN1kKUNoiEKYa8w9OVHf/a5ie0E2hqcY1JU6diwHoPsZ1vdkKpIxk2121zFPsfL9KpT9xRyzzrGLZOYJl6rQskgW816GIAYpMJzWiHsJLwabmAhSZW41rJHCR5ejYTZYClLbgGZSKU97362sFpTp4OEq5yXL9SfxA5tPhTlcXyoozeGQIUPpOZqLk6nzmr0MwDUKzs61di23GgOtA6umZvpwdPIBbicdL4zAxQNGIW2+M3BkUPodHggBFx26yFKC0RVg3V9TJv/T1Bv/gyN3Bw+EZlPpyl6BNDkT/k7Fn35Z2Tsv7dCWaGoAyZ9eZP0MC28NsZc6yAJV5OPwL3xol3xgJf/G2JbZKxg4ewE0PofcMisx296IGRcoARX9ushSgtIfeyzz8Cyk6XZn9WcJcWhc5syi5O9mlu0CWwzMoxYeA+gr51qBFSo+yNu/gaPk6SniZ5+Bq/XsESYHYwaPR7AnguwYlUq4Axfla2aq9mwaspS4ewK3VWCYvlFoJxdc8KKwvZyMDdAQFKO3Bja702slToVCLsTty61CabUC+06AtddzZt4cnMNM2CEDeLnnWoFXE8s5gaSfQutN7EpuKXVsCnNoszz70hNjBo+EAxdsDKe/8kyuDYo1gnkmA5zoUQdBwBiVHnq5BKUs8ITGsgQDwfZK0SlCA0h6JQ9mlXoekKV3iAeTXoRTtZzX/kBhXMNQasd24i5V5eCCdKJGDbFuYgoBBf2TbVOZx6+DRcIDCD6TVBZ2fPTscbBo2IJ9IFvBeh9JU59L6aCVAiUplE6ybG6Q/6AuCtCJZg8HtNacAJTAQSzwH9OerYW921ZKVEskC8k815t05qWPbn8fRVYWyUg4J7Ahe5jm0xnPzr0BFyyZtnNBuzMMGACpyO75vbYnTmsDoKgvIgbetxjx7YrKyUrIWMAW5ymBSC2Ubq5mTOSBNgAL4bpCnMhSgtEfcAOYh0lChP3FR1WmmRzBZlT2741ON5Q5Q0too73C4NiVvd9fSSbiXeOSk53nsS7P+DHByg7z70jpaNmnjeOOFUuXUn4QnsQOwXIgHSw/P5t3LO3KVL30hRiahLBfIWsKZB5EU6NRNlgKU9ggKdtm3663MI1rcpyk7+ZNnUMpPsrkwUiNmUDoIUOIHsnZYW41+9UPe0lDl0g7InUExmYHBM9h2Vy/zaNmkzR1PD6Rytxhzwr3sKtGa/oTjbQu3p0jpIsvRqZssBSgdIVre66yTR+zgUbC8AzD3SXMwSxNLfVZRmcfO8AxGNoOnPYwmVgICuk6Zp+Qwu4xIYfocueFlnsNf664rQFK0btLG8dSsTRwSKLPztLe+HFrr4OGIQtlsaZ9X1J9ImJnTqZssBSgdoVfLezU6eACWrYnlQwMlLvNwg7bEIZ3PCBEHB3aRAKVIRgfZtkg7BwiOYkZlpUeV2acW0YNIFvDcll2pDIq3vhxazaDIZdYmpQcKR6dushSgdIQolNVZqUCNDh5OnEytxh35n7Smqwll5XSQbQujkWm0gK4boDQ3Mn0aoIMAxcMMSpXMHigcfzQoWsJbEzxPEefwSBig+GqQpzIUoHQEL/GUHtFXx4LSNvfudJOp1ZgHGx3pTzipY1gpqCIHqNKZwNkXxBZjGRxk2yO+P7ss6aIBCj+4mqzKlNX8QbMaFC+7eLQWoPDXtfK0tIJ8OTIoEV62dmsEClA6IqoHU1LbbS4bcT3AI3pVMigytBo3NQAF+9h2Rx08HGuE62Ad6FkUQVCuxdidOGeA0lUzKPxMNDxRW50lbcEzKLUlgK2u/fsppkFxHiwbqzpeD0erAUp4EgtQBbvL1kEKZNGgODModaW6OtmmAKUjjEbXl37RAXXX4ilNDa4oOVAyKAX7AEcTO6OISffsMaIOJcAnG9eWOL/ADa6yixJ09QCFZ1C03GLMCYlh84KA9ssRzTbX3xSVJu96rJFAkLN91hNNhFZFskYj65QEpG0KEF1kJeziCYkFjM6p9jpyk6UApTP0ZnnP202DwtQ54+AalJpC1v4qBeKAwPGen612FaEsf1/G9pbOM8ETeIBSdlyelnKtoweTNnc606FU5wMQWBee3N8bLZxNPQlQNJpBATwXIHuDlHN4OEajLt1kKUDpDL1Z3le4CWTVSD0HR7lSk1KVeU67Och6Cg9QCvYBTfXSrEOLFKlQ3gHYF7PJwqy+KztxKA1E3Es8eqAzHYo4JLC7Mt8b3vhyaDpA8XIYoyfIoUEBdOkmSwFKZ+htqvEZlVqM3ZFShyIIbg6yHnTwcKJ7sBqxownI3+v/OrSKqD+R2UG2NSazax5SVxTK1ugsQOnsQKqU/oTjqS+HFgcFuiN1q3FzI9BQybalDlC8NcjTABSgdAb/4q/Mka5kISdqdvBw+IFLCh1KRQ47GBjNQMoIzx9nMHSNdmPeYiznkMD2EIdDduEAResmbZzOShHiFGOZ9SccnkHp7Gy+sQpwODtktKZBAdxajSUKULj+xGgGgqOleU6OtwZ5GoAClM4IjXV9mPjBQMuo2cHDETMoEgQoPHuSnAEEhXj32ECfbOxwKDeDpy1EL5Qjyu9bbfRic8/pTIPCu1DkbjHmeKpB4dmToDDvP/9KECOxFwrXn4TGST+mxFuDPA1AAYon6MnyXgslHrGTR4ISz2kP5u+0h7tQVm8TqT2hMgdoqmVakNjeyu9f7OSR2PNGD+hhUKA7nZZ43DQoSuDpRGOxg0eD5R0AiE5nl9UFrIPSX3gGRUqTNg4PCqmLJ8BI0JEORQslHncNisPh33OJHTw+BChJw5lPQX25vnxsPIVnT+L6yzt9tj34/7mki2VQHA7Xma7eunjqz7Rdqq5UOIPiqZusqD/RYHkHYOuyOEdvSCEWFz1QZAhQRGFyAGdQNm7ciOnTpyMlJQUGgwGrV6/u9DEbNmzAqFGjYLVa0bdvXyxfvtyHpaoIz6BovZOnscb1gVYzgxLdEzAGAc31/hkY2WqBQqf/jC8BitkCdB/FtgNRhyLO4FGhvAO4ApT6cqC2TJ01qEFdmVMXYZDnQCIHwZEux9u2yhFKuchyPBVsalkgCzCtm5StxmIHjwyZuYgu0GZcW1uLjIwMLFu2zKP7Z2Vl4YorrsBFF12EzMxM3HPPPbj11lvxww8/eL1Y1XAv8Wi5VMC/eIKjgJBo9dZhMgOxvdi2PzqUvD3MpTGyu+9fnGKZZ5vv69AqSs/gaY0lzCWq7EpCWS7sDO2mTubKV9qbHdNQBTQ6O0eULvE0VHZsA6D1AAVwK59l+/9cYoAioUkbh2dQakukteaXEbO3D5g2bRqmTZvm8f3feOMN9OrVCy+88AIAYNCgQdi8eTP+/e9/Y8qUKd7uXh3i+gMGE/swVeUr14rnLVoo73C69WMHrdLjQJ+LfXsOX/xPWhPIjrJqCmQ5cf1Zarv0CNBzgnrrUBKxg0cn5R1OdA+gIPPsAIVnOYOjO58ULhXBUcwUrrmB6VD4CU1r9BCgSNlqLA4KlCGDEhrHjmOCnZUoI1Ok34fEyK5B2bp1KyZPntziuilTpmDr1q3tPqaxsRFVVVUtflTFbHWls7WsQ1FzinFrpJhq7Iv/SWt4aajkMKu/Bwr2JlfWQo0WY05XFMrqzaSN055Zm1jeUajFGGjpJtuRDoWLRrUcoEjZaiynBsVodAU+ng5qVBnZA5TCwkIkJrb8ICcmJqKqqgr19W2n9pYuXYqoqCjxJy1NwQ9Oe4iW9xoOUPiZkVYyKIDvB64WBm0+6E84YXEuX5bcnb4/j9YoO85M6Czhyh5YWiNONe5CQlm92dxz2ivxiAGKwplhT0SbWp3D446kGRQ+h0em7jBPu6c0gia7eBYtWoTKykrxJzdXA1baehDKaqnE46+bbNkJJr40WVk3jj8E4lwe9wnGak7T7YpDA/XWYsxpT8yptECW44loUw8lnvYCP1/g3WFyaFAAlzhZJ3b3sgcoSUlJKCpq+QYsKipCZGQkQkLaNt6xWq2IjIxs8aM6YoCi4QyKlko8PINSmevZSPXWcP1JykjWjeMPgegoqwX9CeAya6vICeyZR+7ozaSN055ZG9egKCWQ5XjiJquHAIV/39aXA43Vvj+Pw+HmgyJ3BkUfnTyyBygTJkzAunXrWly3du1aTJigM0EdPxCUHmX1fy2ipRJPWDdXW2O5Dx4k/viftIZnUPJ260a93ilFKs3gaU1YnNOSW5BuOKTW0ZvNPYcHKI2VLfVYamhQAM/cZPUQoFgjgBBnCcqfMk/9GSZgBZigVQ505ibrdYBSU1ODzMxMZGZmAmBtxJmZmcjJYQfHRYsWYc6cOeL9b7/9dpw8eRIPPPAADh8+jNdeew2fffYZ7r33Xmn+AqWI7gFYIljdX4uCwPozrlbBaA1odgD/dChcLyJFgBI3gHUNNNUBRfv9fz4t4F7iURODoeuVefQ2KJBjCXWJL92zKKprUNoJUBx2VyCl5QAF6HxatCfwDp7gaP+zxu2hMzdZrwOUXbt2YeTIkRg5ciQAYOHChRg5ciQWL14MACgoKBCDFQDo1asXvvnmG6xduxYZGRl44YUX8Pbbb+unxZhjMLi6JYo1qEPhkXtYPPOn0AK+6lAaKl2vsS8W960xGl3PEwjtxrZa4Ew22+alRzURhbJdJEDRaxcPcLYOxeFQfg4Pp7OJxvUVAJy+U1oWyQKu7JQ/GZRaGTt4ODpzk/XaB2XSpEkQOjAra8sldtKkSdi7NwBG3icMZqWHogPAsGvVXk1LtFTe4fg61ThvNwCB/S1SpdHTxgPH17L/3/i/SvOcalFyGIDAvsjkEtN5Q1fKoDRWs/lHgE4DlB5A3i7X90VdKWC3ATC4Dl5K0Zlgk5d3rFHaN8STotVYTg8Ujs7cZDXZxaNZtNzJI3bwqGhx3xpfpxpL0V7cmh5OHUogTDZW20G2NeJU4y4QoPAOHku4cqZmUtK6FMHnx0QkKx8EcD1E/Zm2B+1pfQ6PO1K0GostxjKedIhussWshKZxKEDxBtHyXoMBipY6eDjuU429GREghUFba1JGMRfFqtOumrte0UoHD6fFcEjtf+n5hdjBo8PsCXB2J484JFAFd+yQGGYjALStidCDQJbDpxr7k0ERTdpkzKCExQMGIyA4XBkbDUMBijfwM9bKXKaT0BJa8kDhxPZiHwZbteeiLIcDOL2Lbftjcd8aaziQNJRt612HovaQwNbEpAMmC7Mtl8ILQsvo1aSN01qDopYHCsB0fREdiDb1FKDEuHmh+DqvTZzDI6MGxWhyPb8OzNooQPGGkBiXV4DWyjz8wKClDIrZ6vpC9FSHUnqEdSMFhQKJQ6VdT9o57FLvAYrWMihGk+96I70hCmR1ZtLGiW51IFXLA4XT0VRjPQUovEXbVuNyv/UWUYMi84RsHbnJUoDiLVq0vBcEbYpkAe91KNz/pPtoNhVZSkTDNh1PNq4rd53FJwxUdy3u8P9zaYBb3ostxnrNoDgPpE21LADgGhS1xiV0ZBymJw1KULBL3+HrVGMlMiiArtxkKUDxFi0KZWtLmMcHDOqkajvCXYfiCVL6n7SGa1oKfmOtunqE65+iezCDKK3QVYSyejVp45itbgfSU24aFJW+NzoyDuOZCC10qnlCe6MEPEUJDQqgKzdZClC8RYtCWZ49iUxhX0BaolsfdultBkUK/5PWRKUCESnMrTFfp23vWivvcLrKVGO9i2SBlp4dapm0cToyDtNTiQdwEyD7GKAo0cUD6MpNlgIUb+EHhqKDvouhpIabdmmpxZgT54WbbF25K5CRUiDLMRjc2o11WubRioNsa7rKVGNxUKCeAxTnmX7ZCVdgoFqJpwPjML0FKP60Gttq3fx1ZM6g6MhNlgIUb4nrDxjNTMiplXZVLXbwcHiJp+IU0NzY8X1PO8s73fqyWT5yIE421qlQVpzBowEHWXe4SLa+HKgtU3ctcqL3Lh7AdSKTsxWAAJiD1QsCOjIO01uA4o9ZG9efmIOZx46c6MhNlgIUbzFbXOlsrZR5tNjBw4lIYh84wQGUZ3V8Xzn8T1rDtS2nd7CWZj0hCNozaeNYwoAo54EvUIWyzTbXQVOvIlnA9T3By6mR3Vl2UQ06mmjMNSh6CVD8yaDUcIFsgvz/Cx25yVKA4gtimeeAuuvgnNGgiyzHYHCdXXemQzntDFDkKO9wkoYD5hDmXqm36btV+SxzZzC5SmdaQiznBahQlp/lGs2uSd16hH9P2GrYpVr6E8AV6NWVsQCQY29yDT/VS4DCMyiVud6f/IgdPAoIgsWgsEjzJ2kUoPhCopsORQtoucQDeKZDsTcDp3ezbTkzKKYg1sIM6K/dmGdP4vppTwwNuDKLgTo0sMZNIGvU8Vdn6+8JtfQnAGshNjot9t01ETx7YjCySeR6ILI7O3mw27xv4eWDApXw1wlLAGBgzQJ1pfLvzw90/ClTEW4gpoUSj8MBVDi9DLRY4gFcOpSOMhbFB5lIzBoJxMvs7yH6oehsLg/33tFaeYfDhbKBmkHR8xRjd6JS2YGfo5ZJG+B0k23DOIyX0kJimBGgHjCZXe3a3pZ5lMygmMyu/WjcrI0CFF/gJZ7Soy3TkmpQXQA4mljaOSJF3bW0R5wHLqNie/EY+c9O9SqU1WqLMSfQpxrXBEiAYgpqGZSo7Z0kdpW0EaDopbzD8bXVuEYhkzaOTtxkKUDxhahUNgLc0az+lzH/IER2l955VSq6eeAmyzt45PA/aQ3PoJQe9d2WWg202mLM4WZtFTlAU726a5EDvZu0ueOuV1NTgwJ0nEHRW4Diq1C21k0kqwQ6cZOlAMUXDAbXQULtMo8Wpxi3hpu11Z9pvwWVZ1DkcJBtTWis62xfL1kUh93lMaLVDEpYHBAcDUDQnwDZE0STNh138HDcdShqalCAwApQfJ1qrJTNPUcnbrIUoPiKaHmv8kwerc7gcccSBkQ608htZVFqip1mcwZW4lECvelQyrPYtGBzCJserEUMBiDemUUJRMM2scSj00GB7rhnUNTUoACug2WLEg9vMdbBHB53/M2gyD0okKMTN1kKUHyFd/KonUHRegcPpyMdCs9iJAxSTrGvt8nG/H0WP0DbokFvnIP1hljiCYAMCj+QBkcDVpmNwTojPJAyKD6atYlzeBQKUHTiJksBiq8kaCSDoocSD9CxDkUJ/5PWcKFs3m7muaB1eIDCM3daRRTKBmAGReziCYAAhb+PtFAuFJ1N3duMdRqg8O/hqjzPv1fszcyBGVBOg6ITN1kKUHyFZ1Cq8pi2Qi30UOIB3M6s29AmKOEg25pufVkLY3M9UPibcvv1Fa0LZDniVOMAy6AIQmCVeJIzgJv+B1z7jtorcXM2dTtY6jVACU9kdvWCw/NRKNyLxGBUrqRFGpQAJzjKJS7j7Z9KY28CqpwfAi26yLrTnptss801WVgJgSzHaHR1DOmhzKNVi/vWxLl53jjs6q5FSurPsHZ+QP9txpxeF7AJ6GojusmWurIOeg1QDAbvW425/iS0m3LlW1H3o203WQpQ/EG0vFepzFN5mkXqJqv2vzT5gas8i6U0OYX7mfgzJMYVxCgFn2ysdaFsUwObPAtob0hga2LSAZOF/U95di8Q4PqIkFg2j4uQjtBuzMcJcGWp9DaHxx0eoHgqlFVafwK4SkmOJld5SYNQgOIPiSoHKGJ5p4f2rbcjU1kHiqOp5ZmFqD8Zp/zAMl5SytnOUvhapfQos6UOjta+QNNocgWagVTmcbe5J6TFaHS9rrzkIGZQdNbFA3gvlK11lniUDFDMFlfwp2GzNo0f1TSO2pb3FToRyALsS4j7obgfuJT0P2lNyih25lad73m9WA3cHWTVmjrrDYEolOVnuYFg0qZF3FuNm+rZ2AtAnxkUb1uNa1XIoABuQlkKUAITscRzUJ0zcC1PMW6LtnQouU4HWTUCFEsom24MaLvMo/UZPK0JRMv7QDJp0yLhbr4cPHtiNLPZXHrD6wwK90BRWHzd1ogBjUEBij/E9WOTOG3V6tTb9eKBwmntkVGZx0S+BhPLZqhBmg50KDyDkqiBllBPCMSpxoFkc69F3LtK3AWyesgYtsbbDIo4h0eBQYHuUAYlwDEFub6M1Sjz8KBIDyUe4Oypxlx/kjhEPbMoPTjKan1IYGsCcapxoAwK1CoRbWRQ9FjeAVwnjLXFns2kUnoOD0ds76YAJXBR0/JebyWe1m6yaviftIbvu/AA0Fij3jrao6ESqMxl23op8fBSXn15+7OX9EY1BSiy4u5squcOHoB1JPLSlCeZdbU0KDoYGEgBir+oZXnfVO96Y/EBVVqHZ1Bqi9mBVwxQVNCfcKK6Mz8bwQ7k71FvHe1RfJhdRqSwLz49YAkDopxBc6AIZflnTetdVHrF3dlUzx08gNMLxYsyD+/iUWoOD6etIY0agwIUf1HL8p53nVjC9fNBDo50nSkVHgAK9rFtNQMU9/3naLDMoxcH2daIeqMAKfNQBkVeItzajPVe4gE8N2sTBOUnGXN04CZLAYq/8BJP6TGguVG5/bqXd/QkJONZlP2fM0+UsAT1Rb5aFsoWHWCXegtQxKnGARCg2GqZEB6gAEUueAaltsSl99FzgCIKZbM7vl9DJWC3sW21ApSaQs36QFGA4i+RKcz2XrAre7ZYkc0u1T64ewvXoRxYyS7TVDBoaw0PUE7v0J7t82lnG3b30equw1sCKYPCD5hBoYA1Qt21BCqhcaybD4KrrKnnAMXTVmNe3rFEAEEh8q6pNTzYttvUnSfXARSg+IvB4FbmUVCHorcOHg7PoDRWsku1yzsAM9wLCmVnM1rSTNjqWCkMUHbSsxSIQwM19Hr6iljeSVA/mA5U3N1keVlTzwGKp63GXCCrtP4EAMxWl65NozoUClCkQLS8P6DcPvXWwcPhZ9acVA0EKCazK0OhpTJPQSbLzIUnAVGpaq/GO3j7fUUuC7T0jNhiTAJZWeE6lMYqdqkXbV1beJxBUUl/wnEXJ2sQClCkgOtQlOzk0ZtJG8d9IKAxCEgZodpSWtDjHHappcnGvLyTOkZ/Z+5hcWx2EASg/ITaq/EPMmlThtYBoJ4zKPzEsaESqK9o/35qDAp0x729W4NQgCIFapR4eAZFbyWe6J4sMAGA5OHK113bQxwcuE3ddbijhTZsXzEY3ISyOi/zVNOgQEVo3cKt5wDFGs50NUDHXihqDAp0R+NushSgSAHvsKjOd5kMyUljtWtEtt5KPCYzENubbatp0Naa1DEADOxsn1tPq4kguGVQdKY/4bQebaBX+FkuBSjyEkgBCuBZq7GoQVHYRZajcTdZClCkIDjSVWrJ3yv//nhEHhzNOoj0Ru8L2eWAy9VdhzshMS4r+VwNZFEqT7O0q8EEJI9QezW+EShCWTJpUwb319cczITresYToazaGhSNu8n6FKAsW7YM6enpCA4Oxvjx47FjR8d1+5deegkDBgxASEgI0tLScO+996KhocGnBWuW9PPY5ckN8u9Lr+UdzqVPAnfvA3qdr/ZKWsJ1KFoo8/DsSdJQNnVZj4hTjXWeQakmkawiuL++eh0U6I4nQtkatUWy2naT9TpA+fTTT7Fw4UIsWbIEe/bsQUZGBqZMmYLi4uI27//RRx/hoYcewpIlS3Do0CG88847+PTTT/Hwww/7vXhN0fsidnnyZ/n3xTMoehPIcoKCgZh0tVdxNj0msMucrequAwBO72KXWuhy8pV4twDFYVd3Lf5Q49ZmTMiHewZFzx08HD1kUAItQHnxxRdx2223Yd68eRg8eDDeeOMNhIaG4t13323z/r/++ismTpyIWbNmIT09HZdddhluuOGGTrMuuoOXLQr3y69hqNBpi7HW4RmUgn3MPVRN+KRnvepPABZAmyyAvdGzoWlaxN7sOohQiUdeIlplUPSOJxkU/t5STYPCSzxFmnST9SpAsdls2L17NyZPnux6AqMRkydPxtatbZ91nnvuudi9e7cYkJw8eRLffvstLr+8ff1BY2MjqqqqWvxonvAEZvgFAFm/yLsvscSTLu9+uhrRaUBkKuBoBvJ2q7eO5kbXnKLUMeqtw1+MJldbuV7LPLUlAASmBQqEg6aWCYsHDM5DUiC81vz7uSKn7YN/U4PL8yUsTrFltYCX1ZobgIYKddbQAV4FKKWlpbDb7UhMbKlmT0xMRGFh2ymiWbNm4YknnsB5552HoKAg9OnTB5MmTeqwxLN06VJERUWJP2lpad4sUz16T2KXcpd59F7i0TJa0KEU7mf20yGxro4nvSLqUHQqlOXiwbB4FnAR8mE0sdlcgKtFV89EpQIwAE11rnZid3j2xBjk9AxSgaBgV6OFBocGyt7Fs2HDBjz99NN47bXXsGfPHqxcuRLffPMNnnzyyXYfs2jRIlRWVoo/ubm5ci9TGvo4dSgnNsiXLhMEKvHIiRigqKhDcW8v1rtQUAxQdDqTh7cYk0mbMvDXORAyKGYrm9UGtF3mcdefqPk517CbrNmbO8fFxcFkMqGoqGWkVVRUhKSktuuzjz76KG688UbceuutAIBhw4ahtrYWf/nLX/CPf/wDRuPZMZLVaoXVavVmadqgx7ms5l51Gig7fratuxTUn3GlBSlAkR4ulM3dwfQHJq8+ItLAA5Q0HetPOHqfaiyatJH+RBGi0lh5M1AEydE9gKo8NtW4dblWDFBUzhaFJwIlhzXpJutVBsVisWD06NFYt26deJ3D4cC6deswYcKENh9TV1d3VhBiMrFUqaBBUY5fWEJdZ+AnZCrz8PJOWIJ+20+1TMIgwBoF2GqUna3kTq7ODdrc0ftUY+rgUZYLHwAmLACGzFB7JdLQkVBWbYEsR8Nusl6XeBYuXIi33noL77//Pg4dOoQ77rgDtbW1mDdvHgBgzpw5WLRokXj/6dOn4/XXX8cnn3yCrKwsrF27Fo8++iimT58uBioBhdztxhU690DROkaTy1peDR1KdSFQmQPAAKSMUn7/UsOnV9eXt12H1zriHB7KoChCcgYw5Z+uKbt6p6NWY7Xn8HA07Cbrdf565syZKCkpweLFi1FYWIgRI0bg+++/F4WzOTk5LTImjzzyCAwGAx555BHk5eUhPj4e06dPxz//+U/p/got0eciYN3jQNYmwN4EmIKkfX69TjHWEz3OAY6vZTqUc25Xdt/c/yRhEHMo1juWUCCqBwu6So+qn872FprDQ/hDhxkUlefwcDTsJutTgX3BggVYsGBBm7dt2LCh5Q7MZixZsgRLlizxZVf6IymDRf/1Z1irKi/5SIVepxjrCdGwbRsTJSspYHOfYBwoxPVzBSg9z1V7Nd4hlngoQCF8oKMMSq1WMijaNWujWTxSYzQCvZymbXLoULgGhUo88tF9NBM71xQycZuSBIKDbGv0LJSlEg/hD/xEsvL02W7KmtGgUIDSteDtxnLM5aESj/wEBQMpI9m2kjoUezOQv4dtB4JAlqNXoawguM3hoQwK4QORKcznxNF0dhtvjUa6eDTsJksBihxwoezpnUCDhC64gkAmbUqhhh9K8e/M1Mka6fIPCQT0OtW4oYLZ9AMUoBC+YTQ5DdtwdplHbDNWOYPCNShNdS4LC41AAYocxPRkDqCCHcjeLN3z1pYAzfUADMwvgJAPdx2KUnD9SffRrFQYKPBgqyIXsNWpuxZv4F0WwVEsq0YQvsCz3e5CWYcdqNOISNYSyk6KAM25yQbQt6DGkKPdmEfgkSmA2SLd8xJnkzaeXZYeAWrLlNmnqD8JoPIOwFLYITEABGZgqBfIpI2QgraEsvVnAMHBttUu8QBuOhRtuclSgCIXou29hAEKdfAoR2gsED+QbeduV2afuc4JxmkBJJAFWBeUHi3vRYEslXcIP2ir1Zhn50JipLei8AVewtSYmywFKHKRfj6bzFl2jCm4pYBM2pRFSR1KXTlQfoJtdx8t//6URhTK6miqMbUYE1LApxq7Z1C0oj/hjLoJuOwpZpSnIShAkYuQaNeBRqosyhnKoCiKkjoUXt7p1pdlbwINPQplyaSNkIK2MijugwK1wPA/Aef+zWUJoBEoQJETqXUoNMVYWXgGJX8v0FQv775OB9D8nbYQSzw6zKCQBwrhDzzjXZUPNNvYtuiBopEARaNQgCInvSexy5MbAIfD/+c7QyUeRYnuyQZpOZqYK7CcBKKDrDvxbgFKa8MqrUIZFEIKwuIBcwgAAajMZddpZQ6PxqEARU5SxwJBYUBdmf+TcR12l5aFSjzKYDAoo0NxOFwBUCA5yLoT3RMwWZmvCPfy0Tr8IEIBCuEPBsPZrcZa06BoFApQ5MRsAdLPY9v+lnmqC9iZvNHM2owJZVBCh1J6hBkkBYUCCYPl24+aGE1MXwPop5OHD0+jEg/hL61bjWs14iKrcShAkRup2o35GzsqlX3ZE8rAMyi5O+QrTfDyTsoowOTT/E59oCfL+6Z6oKGSbas9K4XQP62FslqZw6NxKECRGy6UzdkKNDX4/jxkca8OiUMBSwTLcBQflGcfga4/4YhDA3XQycPLOyYrEByt6lKIAKDdDAppUDqCAhS5iR/AhJbNDf7pGKiDRx2MJpdxmlxlHt5iHGgGba3RUyePu0mbwaDuWgj9455BEQS3QYEUoHQEBShyYzBI025MHTzqIepQZBDKNlQCxYfYdvcAz6DoqcRDHTyElLhnUGy1zplqoAClEyhAUQIpdChiiSfd7+UQXsJ1KKe2Sj+OPG8PAIFlxgLdUr2bM0CpLwdqS9VdS2eQiywhJTyDUlcKnMlm20GhgDVctSXpAQpQlID7oRT+5tEXc255HfIrWhmDUYlHPbqPZt1T1fkuHwOpCNQBgW1hCQWinO9frWdRyKSNkJKQaDYVG3BZClAHT6dQgKIE4QlAwhC2fXJDh3ctr7Vh2subMPnFX7Aru5xdaW8CqvLYNpV4lMcSCiSPYNtS61AC3UG2NdywTctC2axNwN4VbJta+gmp4CeXec6TEvJA6RQKUJSCl3k6CVBW781DTWMz6mx2zH1vJzJzK5hBm+AAzMGUclYLOQzbBMEtQAlwgSxHy0JZexPw02PA+9NZtiy2DzDiz2qviggUeJmHZ01Jf9IpFKAoRW+3AKUdHYMgCPhsFyshxIZZUNPYjDnvbEfWcWd7a3QP6ihQCzkM28pPMj2GyQokDZPuebWMGKBoLINSdgJ451Jg878BCMDIG4G/bgx8XRChHHyqMRfFU4mnUyhAUYqe5wImC9MwlJ1o8y6/51fhcGE1LGYjvv7beRjdMwZVDc3473cb2R1If6IePINSfBCoK5fmOXn2JDmDuQ53BcQARSMaFEEA9vwXeON8NhQyOBq47gPgyv+QgJGQFtHDynmCSiZtnUIBilJYQoG08Wy7nXbjz53ZkylDkpASHYL35o1FRmoUYpsKAACVVqqHq0ZYnKsLJXeHNM/Z1fQngCtAqcgFbHXqrqWuHPj8JmDNAqCpFkg/H7jjV2DwlequiwhMWusHqcTTKRSgKEkH7cYNTXaszswHAPxpdCoAIDI4CB/cPB5DQisAAO8fEpBdWqvIUok2kFqH0lUcZN0JiwNCYgAIQNlx9daRtQl44zzg4FesQ2vyY8Ccr4Co7uqtiQhsWruAayhA2XCkGKv35qGyvkntpbSAAhQl4TqU7E2AvbnFTT8dKkJlfROSo4Ixsa+rNhkVGoTz4tiZ5uGGGMx6axtyy1U+8+yqSKlDsdUChc4J14HuIOuOwaBumafZ5hLCVuUxIewta4Hz7qUZV4S8tC7RayhAeXPjSdzzaSY+2ymxjYKfUICiJMkZ7OyxscrVC+/k812nAQDXjk6FydhSCBtUxUzaHNE9kF/ZgFlvb0NBZSufFEJ+eAYlf49/c5UAID8TEOxsDEJkFztrVytAKTsBvHvZ2ULY7qOUXQfRNbGEtmwt1ogG5UytDduzmK5uyhBt+f5QgKIkRhPQ60K27aZDKaisx6ZjbDbDtc7yjkhTvWga9ficK9CzWyhyy+sx663tKK7y8yBJeEdsb/YFY7cxQaU/uJd3ulpnltIBCglhCa3gnkXRSAblp0NFsDsEDEqORI9uoWovpwUUoCgNd5V106Gs3JMHhwCM6xWLnt3CWt6/wplys4QjMTEZH912DrpHhyCrtBaz396OsppGZdZNsEBCKh1KVxTIcsSpxgoEKCSEJbQEF8oajEBIrLprcfLD72zu1FSNZU8AClCUhwtlT+8EGqogCILYvXPdmLSz7y9a3PcEDAZ0jw7Bx7edg6TIYBwrrsHst7ejos6m0OK1xbGiajzz3WE8+/1hNNsdyuy057ns0h8dSguDti4YoPChgWXHAYddvv2QEJbQGlwoGxoHGNU//NY0NmPjMTZ+ZepQ7QUoZrUX0OWISQdiegFnsoBTW7DLOh7ZZXUIs5hw+bA23iAVZ08x7tEtFB/dNh7X/d82HC6sxo3v7MCHt45HVEiQMn+DilQ1NOF/+/Lx+a7TzGXXSVpsKG4Yp4BPDM+g5G4DHA7fvmQqc1nZzmh2Weh3JaJ7MnM6eyMbghnbS9rnb7YBG54GNr8EQGBC2GveJq0JoT78e1wj+pNfjpTA1uxAerdQ9E/UXrlT/RCuK+LWbsxV01cMT0aopY148YxbBsWN3vHh+Oi28YgNs2B/XiXmvrcDNY3NZz8+AHA4BGw5Xoq7P9mLsU/9hH+sOoDM3AqYjAYMSo4EAPx77VHU2RT4+xOHAUFhQEMlUHLYt+fg2ZPEoUw419UwmoBufdm21DqU1kLYUXNICEtoh14XMg1U/ylqrwQA8L2zvDNlaBIMGtTCUYCiBs52Y8eJ9fhmPzNh+1Nb5R2gwynG/RMj8OEtLHOyN6cCN7+3U5mDtELkltfhxbVHcf6zP2P229vxVWY+Gpsd6J8Yjn9cPgjbFl2C1fPPRVpsCIqrG/HOpiz5F2UyA2nOsoyvOpSuNMG4PXiZR8oAZf8XZwth//gqCWEJ7RDbC3ggC7hksdorQWOzHT8fLgagve4dDgUoatDrAsBghLHsGCJtxegVF4YxPWPavu+Zs0s87gxOicR/bxmHCKsZO7LLcdsHu9DQJGNdX2bqbXas3HMaN7y5Dec/+zNeWXcMeRX1iAg2Y/b4Hvhq/kT8cM8FuO2C3oiPsMJqNuG+y5jo8v82nkSpEqJhf/1QurL+hCMKZSWYyWNvBn74B/DlLSSEJbSPBrQnAPDr8TLUNDYjMdKKEanRai+nTUiDogYh0UDKKCBvF8437Uf66HPbT69VMA+Us1wI3RieGo3lN4/DnHe2Y8vxMtz+4W78342jYTXrw3hKEATsyanA57ty8fVvBWKpymAAzusbh2tHp2LKkCQEB7X990wfnoK3N2Vhf14lXl13DI9fOVTeBfvTydPcCBTsY9tdyUG2NVJNNa4tBb6YB2Q551WdtxC4+BEyXSOITvj+gLO8MyQJRqP2yjsABSiorGtCVKjy4tKK5PMQnbcL5xsPYNyo1Lbv1FjNpt0CnQ4KHN0zBu/OHYub3tuBDUdKsOCjvXht9igEmbQRrbdFUVUDVu7Jw+e7c3GyxGXh3yM2FNeOTsU1o1PRPTqk0+cxGg1YNG0gZr29HSu252DexF5Ijwvr9HE+030MYDAxsWtFLhDdTnmuLQp+Yz4qod2Yr0pXxX2qsSD45gWTvxf49Eb2fwgKA656nbImBOEBdoeAtYeYv5ZWyztAFy7xCIKAl346inOWrsNvpysU3//3dQMBAJOCDiIpop1Jtry8ExIDBEd2+pzje3fD23PGwmI2Yu3BItzzSaZy7bdecLiwCjcv34kJS9fhX98fxsmSWoQEmXD1qO745C/nYMN9k3DXJf08Ck445/aNw6QB8Wh2CHjuBwnKBh1hDQeSh7Pt3O3ePda9vKNBUZpicJFs/Rmgrsz7x2d+BLwzhQUnsX2A29ZRcEIQHrIzuxzltTZEhwZhXC9t+LG0RZcNUAwGA3LK6lDfZMeSNb/D4RAU27fdIWDZ8RjUClZEOiqAogNt39GD8k5rzusXh/+7cTSCTAZ8s78A932+D3YF/7bOKK+14aZ3d2D94WI4BGBMzxj865ph2PnIZLx43Qic07ubz+nGB6cOhMEAfLO/AHtzzki88laIOhQvyzxdcUBgW1hCgShnVtAboWyzDfjmPmD1HaxNuf9U4Lb1QMIgedZJEAEIL+9MHpSo6Sy7dlemAA9NG4gwiwl7cyqwam+eYvvdfLwUuVV27DEMZlec3ND2HTvo4OmIiwYkYNmsUTAbDVidmY8la9oJgBRGEAQ8+OVvKKpqRO/4MKz7+4X44o5zMXNsD4Rb/a82DkqOxDXOctnS7w5DEGQMzEQdipdCWergcRHvLPN4KpStLgI++COw8y32+4UPAdd/zDRdhFfYmh3499qj+NbZRUh0HQRBwI+/u/QnWsanAGXZsmVIT09HcHAwxo8fjx07dnR4/4qKCsyfPx/JycmwWq3o378/vv32W58WLCUJkcG46xLW7rj0u8OoblBm1DR3jq1NvYBd4TaXpwWddPB0xGVDkvDy9SNhMAAfbsvBF7tP+7JUSVmxPQdrDxbBYjLiletHok+89O2fCy/tD6vZiB1Z5VjvbKGThTRngFL0O1Bf4dljqguByhwABiaS7sLYHQKKLCzwXv3TBtyyfCd+OljUfrYvdyfw5oUsY2WNBG74BLhokWY6IvTGpztz8PK6Y7hzxR4s/faQohlkQl3251Uiv7IBoRYTzu8Xp/ZyOsTrT/enn36KhQsXYsmSJdizZw8yMjIwZcoUFBe3fTCw2Wy49NJLkZ2djS+++AJHjhzBW2+9he7dtWE3PW9iL/SOC0NpTSNeXX9c9v1V1jXhx4NMnNRn/B/Ylad+bXs6rg8lHneuGJ6Mey5hZ6mPrN6PQwVVPj2PFBwrqsaTXx8EADwwdQCGdo+SZT8p0SGYN5E5kz7znYwW+BGJTpGrm219Z/D7JQz2SFMUaNQ2NuP7A4W47/N9GPfPn/DSPlbKi67NwrrDxbj1g104/1/r8cq6YyhyH4S56z3gvWlAdQEQNwC47WdgwDSV/gr943AIeHdLtvj7/208ids/3B1QHkpE+/DyzqQB8e12RmoFrwOUF198EbfddhvmzZuHwYMH44033kBoaCjefffdNu//7rvvory8HKtXr8bEiRORnp6OCy+8EBkZGX4vXgosZiMenc5KLe9uzsLx4hpZ97dmXx5szQ4MSo5Ev6FjgYhkoLmBWae3RrS5T/d5f3+7uC8u6B+PhiYH7lyxB1UKZYncaWiy428f70VjswMX9o/HzRMltjZvxR2T+iA6NAjHimvw5R4ZM0fe6lC6oP6ksLIBH247hXnv7cDIJ9fi9g9344vdp1FWa0NBEOt+GhtRir9c0BsxoUHIr2zAi2uP4txn1mPBB1tR8N/bgK/vARxNwKDpTAwb11fdP0rnrD9cjKzSWkQGm/HM1cNgMRnx48EizPy/bS0DQyIg+UEn5R3AywDFZrNh9+7dmDx5susJjEZMnjwZW7e2/SW9Zs0aTJgwAfPnz0diYiKGDh2Kp59+GnZ7+2ZijY2NqKqqavEjJxcNSMDkQQlodgh4/H+/y6pd+GwXO2D+aXQq6+JoY7oxANZ6ecY3DYo7RqMBL80cgZSoYGSV1uLBL36TV5vRBs98dxiHC6sRF27B83/KkL3nPiokCAsuYgexF9ceRb1NJuM6b3UoXUB/IggCDuRV4qWfjmL6q5txztJ1eGT1AfzsnPmRFhuCmyf2wke3jsdbC2cDAMLq8vHw5J7YuugSvDRzBMamxyDeUYpbji9A8onP4IABO3ovQPkV7wDWCJX/Qv3zzmbmuHzD+B64flyPFiMzZizbgoP56mVaCXk5XlyNEyW1sJiMuHigNuYBdYRXAUppaSnsdjsSExNbXJ+YmIjCwsI2H3Py5El88cUXsNvt+Pbbb/Hoo4/ihRdewFNPPdXufpYuXYqoqCjxJy3NC58JH3nkisGwmIzYdKwUPx2SR7twuLAK+/MqEWQyYMZIZ4nLaXt/lg6l/gxgq2bbfgQoABAbZsGy2aMQZDLguwOFLdK7crP+cBGW/8r299y1GYiPsCqy3xsn9ERqTAiKqhrx7haZLPB7OCcb5+1mBmxOGpvt+HhHDg7kVbrua28G8vaw7QALUBqb7dhwpBiPrj6Ac59Zjz+8uhkv/XQM+/MqYTAAo3pE44GpA/DjvRdg4/0XYfH0wTi3bxyCIuJZCz0EoOw4goNMmDGyOz6fKmBzzOMYaTyOSiEMc20P4LqD5+Kcpetxzyd7sTO7XPEgO1D4Pb8SW0+WwWw0YO656QCAMemxWH3nRPSJD0NBZQP+9MavWH+4SN2FErLAyzvn9u2GiGDtD5eVXWHmcDiQkJCAN998E6NHj8bMmTPxj3/8A2+88Ua7j1m0aBEqKyvFn9zcXLmXifS4MNx6Pis9PPn1QVns4j93Zk8mD0pEbJjT+4RnUAp+A2rd/CB4eSc8EQjy3A+kPUb2iMEjV7BS1tJvD2FXdrnfz9kZxdUNuP/z3wAA8yam4yIFI3ar2YT7pzA79dc3nECZHBb43fqwsenNDaI7bLPdgbs/zsSilfsx/T+b8cAX+1BS3chayZvrAWuUy6RMx1TWN+HL3adxx4e7MeqJtZj73k78d9spFFQ2ICTIhMsGJ+LZa4dj5z8mY+WdE3HnpL7onxjR0jHZYGCaEoC1GgsCsP3/gA/+CHN9KZA4FEF3bMTlM/6Mod0jYbM7sDozH396YyumvLQR7/+arUrJUs/w7Mnlw5KRHOX6XunRLRQr75yIiX27odZmx63v78J7cgX2hGr88DsLPKfqoLwDeBmgxMXFwWQyoaioZXRdVFSEpKS2/+Dk5GT0798fJpNLjDNo0CAUFhbCZrO1+Rir1YrIyMgWP0ow/6K+SIoMRk55Hd7edFLS57Y1O8RW5j+NcXOOjUhkokkIQNYG1/USlHdaM2dCT0zPSEGzQ8CCj/bKOrfG4RDw98/2oazWhkHJkXho2kDZ9tUe04enYGj3SNQ0NssjgDYYWtjeC4KAf6w6gO9/L4TZaIAgsJLeRc9vwOYN37H7pY7WfefJ8eIaXPriL/j75/vw3YFC1NrsSIy0Ytb4Hnh37hjsXXwp3pwzBteNSUNceCcZMz40sGAfsOp24LsHAEczMPRa4JYfEZrUF9eP64Gv/3Y+1iyYiJlj0hAcZMTRohosWfM7xv9zHR784jfsP13Z8X4IFFc14H/78gFAPBlzJyokCMvnjcPMMWlwCMDj/zuIxV8d0KTZI+E9p8/UYX9eJYwGYPLgxM4foAG8+qa0WCwYPXo01q1bJ17ncDiwbt06TJgwoc3HTJw4EcePH4fD4XqTHz16FMnJybBY2nFQVYkwqxmLLmcH0mU/n0B+Rb1kz73+cDHKa21IiLDign7xLW8UyzwbXNeJHii+dfC0hcFgwDNXD0Of+DAUVjXg7k/2ymbi9u6WLGw6VorgICNeuX6EKnOBmAU+M/Basf0UTpXVdvIIH+BC2VNb8cx3h/HprlwYDcB/Zo3Cl3eci+GpUahpbEbRoc0AgKzgwdKvQUGOF9fg+je3obi6EWmxIbjrkn5Ys2Aitj50CZ6+ahguHpjoXWcAzyb9+grw2ydshMCUp4Fr3gYsLccVDE+Nxr+uHY7tD0/G438cgv6J4ahvsuPTXbmY/p/N+ON/NuPTnTmwNWvrgPrqumO44c1t8mTxvOCDrafQZBcwNj0Gw9sZDhdkMuKZa4Zh0TRmevjB1lO49YNdilkwEPLxozN7MiY9tvMTB43g9ancwoUL8dZbb+H999/HoUOHcMcdd6C2thbz5s0DAMyZMweLFi0S73/HHXegvLwcd999N44ePYpvvvkGTz/9NObPny/dXyEhf8xIwbj0WNQ32fH0t4cke94vdrMy1dWjUmFu7dzXxxmgnNjA0tyAq8XYBw+UjgizmvHGn0cjJMiELcfL8PJPEo67d3IgrxL/+v4wAODRPwxGv0T1hI0T+8bhgv7xaLLLZIHvDFAaTv6KNzeyLM0zVw/H1KFJGN0zBqvvnIhnrx2OMaYTAIDH94Zi3ns7cLJE3m4xOeDBSWlNIwYlR2LN/POw8NL+GJ4a7bvwmU81Bth8ojmrgQnzOxwDEBUShJvOTccP91yAz2+fgBkjUmAxGfHb6Uo8+OV+3PPpXt/WIgM7ssrxwtqj2HqyDC+v83Mwoh/U2+z4cDs76bnlvI5nQBkMBvz1wj54ffYoBAcZseFICf70xlbkSXjCRijP987uHb2UdwAfApSZM2fi+eefx+LFizFixAhkZmbi+++/F4WzOTk5KChwuROmpaXhhx9+wM6dOzF8+HDcdddduPvuu/HQQw9J91dIiMFgwJI/DobRAHz9WwG2nfRhTkgriqsb8POREgCtyjucnucCJgsz8Sp3lpZkKPFw+iVG4JlrhgEAXll/HD8fkU4UXGdrxl2f7EWTXcBlgxMxa5z06/eWh5wW+F//VoB9uRXSPnnycDQbgxHcXIk+hnz84/JBuG6sS9RtNBpw3eAw9AT7TBww9MPPR0ow5aWN+Oc3B3WjoWgdnHx063jEhEmQAU0bx1rtU8cBf/kF6HWBxw81GAwYmx6Ll64fiW0PX4KHpg2E2WjAt/sL8f0B9R1Sbc0OPLxqv/j7R9tzkFUqQxbPA1buPY2Kuib0iA3FpR6m96cOTcZnf52A+AgrDhdW48r/bJH+80MoQmlNI3Y6dYeXDdFHeQfwUSS7YMECnDp1Co2Njdi+fTvGjx8v3rZhwwYsX768xf0nTJiAbdu2oaGhASdOnMDDDz/cQpOiNYakRGHWeHZgfWzN737XYFftyYPdIWB0z5i23VMtYUCa8zU8sZ5dylDicefKEd1x4znsue/9NBOnz9RJ8rxP/O8gTpbUIikyGP+6ZnhLUaRKDE6JxFXOrqmnvz0kaQfIN7+XYkczOyO9f2A5brugjbNT7n/SrR8+u+dyXDwwAU12AW9tysLFz2/AZztzNe3keby4Wp7gBGBdPPf+Dty61rup0K2IDbPg9gv74PYL+wAAFn/1Oyrr1Q3+3tx4AseLaxAXbsE5vWPR7BDwvNyDLNvA4RBEcey8iekweZHtGp4aja/mT8TApAiU1jRi5ptb8R3Z4+uOnw4WQRCAYd2jkBoTqvZyPEbfaj0Z+fulAxAdGoTDhdX4aEeOz88jCAI+3+3mfdIevJvn5AZW5pGpxOPOI38YhIzUKFTUNWH+ij1obPavc+nb/QX4ZGcuDAbgxZkZ0h3EJODvlw2AxWzE9qxyyTJGG4+W4J5P92Kng5UpLotop+vBbYJx7/hwvDt3LN6bN9bpYGzDA1/+hiuXbcHuU/J3VnkLC062yxOccIzSnawsuLgveseFobi6USwzqkF2aS1ecQqzH7liMB774xDlBlm24pejJThZUosIqxl/GuN9EJgSHYIv7jgXkwYww8c7VuzB6xtOUKu3jhDLO0P1U94BKEBpl5gwC/5+GTvwvPDjUZTXtt1x1BmZuRU4XlyD4CAjrhie3P4duQ4layNQlc9aVw1GILKDoMZPrGYTls0ehaiQIOw7XYl/fuO75ia/oh4Pfclaiu+4sA/O7aOtGQ/do0Mwz+n78Mx3h/0WB+8+dQZ//e9uNNkFGHsyHYqhPcO2NhxkLxqQgO/vuQD/uHwQIqxm7M+rxDWvb8U9n+xFYaU23Dzdg5PBcgUnEhMcZMLTV7Py5Ufbc7BdghKttwiCgEe/OgBbswPn9Y3DlSNSMDBJwUGWrXh7Mysb3zDe96Gc4VYz3p4zRvRO+df3h/Hgl79pTpBMnE1VQxO2HC8FAEzRUXkHoAClQ2aN64FByZGorG/C8z/6lprlzrGXD03u2BgneQQQHA00VgEHV7PrIlIAs7wHhNSYULw0cwQAptj/KtP7qc52h4B7Ps1EVUMzMtKice+l2vT5uHNSX0SFBOFoUQ2+9GN44pHCaty8fCfqm+y4oH88/jprJgsmK06x4NIdhx04vZtttzJos5iNuO2C3lh/3yTMHJMGgwFYnZmPi1/YgGU/H5fFi8dTWgcnK3QQnHDO6d0NN4xjmYJFq/Yr/jqu2ZePTcdKYTEb8dSMoWKZU7FBlm4cKqjCluNlMBkNuMkZXPiK2WTEY38cgsemM43eZ7tO46Z3d6CyTh86qq7Kz4eL0WQX0Cc+DH0T9OXETAFKB5iMBjzmnNNzljOoB9Tb7Pja6TvQaWrVaAJ6X8i2dy9nlzKWd9y5aGAC/nYxs4ZftHI/jhVVe/X4134+jh1Z5QizmPDK9SMQ1LpLSSNEhfpvgZ9TVocb39mOyvomjOoRjTf+PAqWsGggcajzDq2yKKVHmSNwUJjT7+Zs4iOs+Ne1w7Fm/nkY3TMGdTY7nvvhCC799y/4/kCh4ql0PQcnnIemDUJ8hBUnS2rx2s/yDwHlVNTZxKGYd13cF+lxrlbplOgQzJ2YDoBlIORq8XeHa0+mDU1C92j/DR8BYO7EXnj7pjEIs5iw9WQZrnp9C7JVEv8SnfODTss7AAUonTK+dzf8MSMFgsAEs94cLH74vRDVjc1Iiw3B+F6xnT+A61BKna2/Mglk2+Keyf0xsW831NnsuGPFHtQ2ejbZdPepM3jJ2T755Iyh6NktrJNHqMuNE3qie3QICqsavLbAL65qwJ/f2Y7i6kYMSIzAu3PHItTiTJmLgwNbBSi5O9hl91GAqeP0+rDUKHxx+wS8NHMEEiOtyC2vx+0f7saf39mO305XeLVWXzlWpP/gBGCtyI//cQgA4LUNJ3Ck0Lug21f+9f1hlNbY0DchHH+5oM9Zt995oTRZPE8orm7Amkx2gnTLedIO6Lx4YCI+v/1cJEcF42RJLa56bQuZ5WmQhiY7Njg7SPUwHLA1FKB4wKLLByIkyIRdp87gq8z8zh/g5LNdzPvk2lFpnvlEcMM2jgwtxu1hMhrw8vUjkRhpxfHiGjy0cn+nwVhVQ5No9nbliBSxU0bLBAeZcN8UVoJ6Y8MJj7VFlXVNmPPuDuSU16FHbCj+e8s4RIe6HbjdHGVb4OUEY4OBzWla//dJWHBRX1jMRmw5XoY//mcLbnp3h6xC2mNF1bjhLf0HJ5xpQ5Nw6eBENDsEPLTyN9kzFjuzy/HxDvaZf/qqYbCYz/56lSKL5ykfbj0Fm92B0T1jMLJHjOTPPzglEl/Nn4hh3aNwpq4Ji1YpP4iU6JhNx0pRZ7MjJSoYw7pHqb0cr6EAxQOSo0KwwFkCefrbQ6jxILuQW16HX0+UwWAArhnt4YE7thcQk+76XaESDycu3Ipls0bBbDTgf/vy8d9tp9q9ryAIeHT1AZw+U4/UmBA86VZr1zpXZnTH4ORIVDc249X1nZtn1dmacfP7O3G4sBrxEVZ8eMt4JEQGt7wTz6AUHQAa3KbB+jjBOMxqxn1TBuCney/E1aO6w2Q04JejJbjm9a2Y9dY2bD1RJunBwD04GZKi/+AEYMHek1cORbjVjL05Ffiwg/ezv9iaHXh4JfM8uX5sGsZ1kDF1z+K996s8824amuz4cDvrBJQ6e+JOQmQw3r95HMIsJhzIq8LagzRkUEvw4YCXDUnSzfezOxSgeMgt5/VCz26hKK5uxH88mOvy5R6Wvp3YJ867vnP3LIqCJR7OmPRYcW7Ok18fRGY7xkyr9ubhq8x8MfMSqYPJmByj0SCONPhw2ynklLXvAWNrduCOD/dg96kziAw247+3jEOPbm38PyOTWXApOFxZk4ZKoMTZ6trdswxKa3p0C8WL143A+r9fiOvHpsFsNODXE2W44a1tuO7/tuKXoyV+ByqBGJxwkqKC8eBU1o337PeHJR1f4c5bm07iWHENuoVZOp07FRxkwt8vY1m81zecwBkfOwQ7YtXePJTX2pAaE4LLZJ67EhtmEQW4//7pmKY9fboSTXYH1jmnUutRfwJQgOIxwUEmPOqcBvzO5pMdOkI6HII4ubhN59iO6OMeoKjjwnrLeb0wbWgSmuwC5q/Yc9YX6KmyWjy6+gAA4J5L+mF0T+nTx3Jzfr94nN8vjlngt9OhZXcIWPhZJn45WoKQIBPemzcWA5M6GFzZWoeStweAwP6PEf4dJHp2C8Mz1wzHLw9chBvP6QmLyYid2Wdw07s7MOO1X51GTN4fGFhwsq1FcNKidBUAzB7fE6N7xqDWZsejqw9IXobILq3FK04d1qN/GOzR6zdjRHcMSo5EdUMz/iOxiFcQXMZsc89NP3u0hgzcdn5vhFvNOFRQJYoyCXXZkVWOiromdAuzYGy6BxpIDUIBihdcMigBFzrnunClfltsO1mGvIp6RASbvRcm9bqAtRtHdgciU/xbsI8YDAY8e+1w9IoLQ15FPe75NFM8K2qyO3DXJ5motdkxLj0Wdzrr6XrkIedAtP/tyz9LhCoIAhZ/dQBf/1aAIJMBb9w4GqN7dvIhb61DEfUn4yRbc/doVk7b9OBFuHliLwQHGbEvtwK3frALV7yyGd/tL/D4DNYVnNgCNjgBWMbsmauHIchkwLrDxfhGQidU7nnS6OZ54umaeKblv1tPIbdcGidngBmzHS+uQbjVjJljfXfn9YaYMAtudnYovURZFE3AyzuTByV65R6sJShA8QKDwYDF0wcjyGTA+sPFWH+47Xord479Y0aKd5NdAWb9ffsm4NafJHXY9JaI4CC85hwW9svREixznuX9e+1R7MutQGSwGf++foRu3/gAG2lw1QimD1r6bUvzrBd+PIoV23OYK+51I3Bh//j2nsYFz6Cc3gU021o4yEpNYmQwFk8fjM0PXozbL+yDMIsJBwuqcMeKPZj68kZ8lZnXoSi0qwQnnH6JEbhzEgumH1vzOyrqpCmrtOd54gkX9IvDxL7dYLM78IKPPkttwbMnM8emdey9JDG3nNcbEcFmHCmqxrcamIXUlXE4BPx4UL/txRwKULykT3w4bp7IRGdP/O/gWfbwVQ1N+M754fTFVhoAKwmolD1xZ1ByJJ6awVw5X/zpKP699ihe/4VN5X3mmuGS+SqoycLL+sNiMmLryTJsOMra8d7edFJMuz81YyimZ3j4v4jrD4TEAs31QME+WQMUcZfhVjw0bSA2P3gx7rq4LyKCzThaVIO7P8nE5Bd/wee7ctHUapZUVwtOOHde1Ad9E8JRWmOTZFJ5ZV2TmEn920UtPU88wWAw4KGpgwAwgz5vfZba4khhNTYdK4XRANH1VSmiQoNEQe7LPx1TxOeFaJvM0xUoqmpEuNWMc/t2U3s5PkMBig8suLgv4iOsyC6rw7ubs1vc9vW+AjQ0OdAvIRwZqfpr62rNtaNTcf3YNAgC8PK6YxAE1qVw+bAObPt1RGpMqGie9cy3h/Hpzhw85bT8v3/KAMwe74VQ2WBwlXkyVwD1ZwCTFUgaJvGqzyYmzIKFlw3A5gcvxt8v7Y/o0CBkldbi/i9+w8UvbMBH23PQ2GzvssEJwEY7POO0wf9s12n86rT/9pVn3D1PLmxjSKQHDEuNwh+dAbAUs4PecdraTx2ahLRY5YfC3XxeL0QGm3GsuAZf/+a5JQMhLT84yzsXDUyA1azdwbydQQGKD0QEB2GRs3786vpjLWanfL6b+SBcNyZNl21dbfHYH4dgSAoTh/aOC8Pi6W07ouqVOyf1QaQzNf3gl6xV9Lbze+HOSWcbbXUKD1D2fcwukzNkH1fgTlRIEP52ST9sfvBiLJo2EHHhFuSW1+PhVfsx6bkNzqnEXS844YxJj8Wfz2Hic39s8Hdll+Nj5xDRf84Y6tdB4P4pAxBkMmDTsVJsdGbxfKGkuhGrRWM23wImf4kMDsJt57N9v7yOsihqIAiCyz1Wh+Zs7lCA4iMzRnTHqB7RqLPZ8cx37Iz7eHE19uZUwGRkZluBQnCQCe/OHYu7L+mH928e53JPDRCiQy2izw3Apk4/fPkg3wJMrkNpdgatadIJZL0h3GrGXy/sg00PXIzFfxiMxEgrCiobUFbbdYMTzgNTByIx0opTZXV4eV3nPjitsTU78PAqFsjOHJOG8b39S6GnxYbixnPSAbBBlr4KTD/cdgq2ZgdGpEWr2lk3d2I6okODcLKkFmv2eT/bi/CPI0XVyC6rg8VsxKQBHmjnNAwFKD5iNBrw+B+HigPedmaXi63FFw1IQHyEVeUVSktiZDDuvbS/KmljJZgzIR1ThyThz+f0wNKrh/me/UrOAMxuJm4eOsjKRYjFhJvP64Vf7r8IT80YinkT07t0cAKws/wnr2Szk97ceBIH86s6eURL3tp0EkeLmOcJ99PxlwUX90WE1YyDBVX4yoeDekOTXTSiu/V8+YzZPCHCPYvy0zE022nisZL8cIA1b1zQLw5hPk6v1goUoPjBsNQoXO9s41vy1e9YuZd9sVznrfcJoTrBQSa8ceNoPDVjmH++EWYr0H2063cZBbLeEBxkwp/P6Ykl04d06eCEc9mQJEwbmgS7lzb4p8pcnieP/GGQZK9lbJgFtztLis//cNTr0tNXmXkoq7Whe3SIJtL6N52bjtgwC7LL6rBqL2VRlOR7Z3lHj7N3WkMBip/cd9kARASzM5+S6kbEhVtw0cAEtZdFqAnXoUQkMz8bQpM8/schiAg247fTlXjPg8GRgiDgkdXM82Ri326YMULa/+3NE3shKTIYeRX1Xtnyq2HM1hnhVjP+egHLory6/vhZnWSEPOSU1eFQQRVMRgMmD5LXQVgJ1H8n65xu4VYsvLS/+PuMEd0RpIEvCEJFBs8ATBZg6DWss4fQJAmRwXj4ctbm+8KPRzs1S/vfbwVunid+lAHbIcRiwr2X9gMA/Ofn46isb/LocZuOleJoUQ3CLCbMHKeMMZsn3DihJ+LCLcgpr8PKPfJObiYYXBw7vldsQIyroCOpBNx4Tk8M6x4Fq9mI68epY09PaIjk4cBDucClT6q9EqITZo5hg/3qm+z4Rwc2+JV1TXjify7Pk15eep54yjWjUtEvIRwVdU14fcMJjx7DsyfXjU3T1EysUIsZt1/IylavrDsOWzNlUeQmkMo7AAUokmA2GfHpX8/BpgcuQt+EcLWXQ2iBoGDASB8vrWM0GrD06mGwmI3YeLQEX2W27d3xrx8Oo7Sm0S/PE08wm4x4cCoT3r63JavT4YbHiqrxy9ESGAzAvHPVFce2xezxPREfYUVeRT2+2E1ZFDkprmrA7lNnAACXDdF/eQegAEUyQi1mJEQGd35HgiA0RZ/4cNzlbDN/4uuDKG81HHP3qXJ8tF0azxNPuGRQAsalx6Kx2YF/rz3a4X3fdWpnpgxOanvKtsqEWEy4w5lF+c/6Y2c5bxPS8eNB1r0zIi0ayVH6d/kGKEAhCILAXy7ogwGJESivteEpt0GgTXYHHl7JJndfNybVb88TTzAYDHjI2b785Z7TOFJY3eb9ymoa8eUe1iFzi8qtxR0xa3wPJEZakV/ZgM92URZFLn4IsPIOQAEKQRAELGYjnrlmGAwGYOXePNHR9a1NJ3GkqBqxYRYsmjZIsfWM6hGDaUOT4BDat8BfsT0HtmYHMlKjMEZFY7bOCA4yYb5z6vmy9cd9du8l2qeyrglbT5QBAKYESHkHoACFIAgCADCyRwxumpAOAPjH6v04XFiFl39yep5cMUjxroj7pwyAycgmp287WdbitoYmOz7Ymg2Azb/R+liNmWPTkBwVjMKqBnziHBFASMe6w0VodggYkBiB3vGBo4OkAIUgCMLJfVMGICUqGLnl9bjmtV/R2OzAuX264SoVRlf0jg/HDc624aXfHW7RYbRmXz5Ka2xIjgrWxeBOq9mVRXltwwnKokjM9wd4eSdwsicABSgEQRAi4VYznrqK2eDX2uxOz5OhqmUo7r6kP0ItJuzLrcC3+9lBSBAEvOtsLb7p3HTd+C5dNyYN3aNDUFzdiBXbKYsiFXW2Zmw8xkqSU4YGjv4EoACFIAiiBRcPTMSMESkAgLsv6adqyjw+woq/OB1Zn/3hMGzNDmw5XobDhdUItZhww1j9+C5ZzEZxKOfrG06g3kZZFCnYeLQEDU0OpMaEYHBypNrLkRQKUAiCIFrx3J8y8NX8ibjTOR9HTW47vzfiwtn05Y935OCdzScBsKnbUaHaMWbzhGtHpyItNgSlNY1e2fl7iyAIWLMvH0u/O4RvfitAUVWDbPtSG17emTokSfNaJG/R96hDgiAIGQgyGZGRFq32MgAAYVYz7p7cD4+uPoAXfjyCqoZmZsw2Ubutxe0RZDLibxf3wwNf/IY3fjmBWeN7SD5xt6S6EQ99+RvWHS5ucX1abAjG9ozF6PQYjE2PRd/4cBiN+j6g25od4t85NcDKOwAFKARBEJrn+rFpeG9zFk6W1gIAJg9KRLpMdvtyc/XI7lj283GcKqvDB1tP4Q4Js1Q//F6IRSv3o7zWBovJiCuGJ+NoUTUOFVQht7weueV54tT5qJAgjO4Zg9E9WcAyPDUKwUHymvBJzdaTZahuaEZcuBWjemi31dxXKEAhCILQOEEmI+6fMgB3rNgDALj1PP1lTzhmkxF3XdwPf/98H97ceAI3TuiJcD+zKNUNbFbS5047/UHJkXhp5ggMSIoQb9+bU4Fdp85gV3Y59uZUoLK+CesPF2O9MwNhMRkxtHskxqbHYnTPGIxJj0Wsxgfu8fLOZUMSdZ8NagsKUAiCIHTA1KFJ+OsFvWEwGDCuV6zay/GLK0ekYNnPx3GytBbv/5ottiD7wo6sciz8LBOnz9TDYAD+ekEf3HtpvxYjCSKCg3BB/3hc0D8eAHMIPlRQhZ3ZZ7D7VDl2Zp9BSXUj9uRUYE9Ohfi43vFhGNszFmPSY9CzWxhMRgOCTAaYjAaYjUaYTQaYjW3/HmQyOq83yKINsTsErHXa208NIPdYdwxCe+M7NURVVRWioqJQWVmJyMjAUikTBEF0RVbvzcM9n2YiKiQImx68yOtJzI3Ndry49ije3HgSggCkxoTgxetG+BS8CYKAnPI67Mo+g12nyrEr+wyOFdd4/TztYTSwzBEPXiwmIyxmI4KclxaTEUFmI6wmI4LMbd/ufj+LyYjqhma8uyULEcFm7H7kUljM2ux58ef4TRkUgiAIQnGmZ6Tg1fXHcKKkFsu3ZOOuS/p5/NjDhVW455NMHHbOKbpuTCoe/cNgRHgZ5HAMBgN6dgtDz25huGZ0KgDgTK0Ne3LOiFmWshobmh0Cmu0ONDsE2B1Cm7+3hUNgglZbm7f6x+RBiZoNTvyFMigEQRCEKvxvXz7+9vFeRASbsfnBixEV0nGA4XAIeGdzFp774QhsdgdiwyxYevUwzQzIEwRXoOIewLi2BTQ5HGi2CyxgsTtga3agyXlps7PtRvfrWtzOH2dHU7MAk8mAOy7sg7RY7U2y5lAGhSAIgtAdVwxLxqvrj+FoUQ3e3ZyFey/t3+59T5+pw32f78O2k+UAgEsGJuCZa4YjPsKq1HI7xWAwMB2KvpqBNItPeaFly5YhPT0dwcHBGD9+PHbs2OHR4z755BMYDAbMmDHDl90SBEEQAYTRaMA9k1lQ8u7mLFTUnV0EEQQBX+4+jWkvbcK2k+UItZjwzNXD8PZNYzQVnBDS43WA8umnn2LhwoVYsmQJ9uzZg4yMDEyZMgXFxcUdPi47Oxv33Xcfzj//fJ8XSxAEQQQWU4ckYWBSBKobm/H2pqwWt5XX2nDnij34++f7UN3YjFE9ovHtXefj+nE9As41lTgbrwOUF198EbfddhvmzZuHwYMH44033kBoaCjefffddh9jt9sxe/ZsPP744+jdu7dfCyYIgiACB/csyntbsnCmlmVRfj5SjCkvbcR3BwphNhpw/5QB+OyvE3RrUEd4j1caFJvNht27d2PRokXidUajEZMnT8bWrVvbfdwTTzyBhIQE3HLLLdi0aZPvqyUIgiACjilDEjEkJRK/51fh5XXH0Oxw4MNtbOJx34RwvDRzBIZ2j1J5lYTSeBWglJaWwm63IzExscX1iYmJOHz4cJuP2bx5M9555x1kZmZ6vJ/GxkY0NjaKv1dVVXmzTIIgCEJHGAwsi3LbB7uw/Nds8fp5E9Px4NSBurOgJ6RB1ubp6upq3HjjjXjrrbcQFxfn8eOWLl2KqKgo8SctLU3GVRIEQRBqM3lQAoansixJUmQwPrxlPJZMH0LBSRfGqwxKXFwcTCYTioqKWlxfVFSEpKSz+9BPnDiB7OxsTJ8+XbzO4XCwHZvNOHLkCPr0OXtQ1KJFi7Bw4ULx96qqKgpSCIIgAhiDwYC35ozBjweL8MfhKYgK9c10jQgcvApQLBYLRo8ejXXr1omtwg6HA+vWrcOCBQvOuv/AgQOxf//+Ftc98sgjqK6uxssvv9xu0GG1WmG1UvsYQRBEVyIxMhg3ntNT7WUQGsFro7aFCxfipptuwpgxYzBu3Di89NJLqK2txbx58wAAc+bMQffu3bF06VIEBwdj6NChLR4fHR0NAGddTxAEQRAEwfE6QJk5cyZKSkqwePFiFBYWYsSIEfj+++9F4WxOTg6MxsCcC0AQBEEQhDLQLB6CIAiCIGTBn+M3pToIgiAIgtAcFKAQBEEQBKE5KEAhCIIgCEJzUIBCEARBEITmoACFIAiCIAjNQQEKQRAEQRCagwIUgiAIgiA0BwUoBEEQBEFoDgpQCIIgCILQHBSgEARBEAShObyexaMG3I2/qqpK5ZUQBEEQBOEp/Ljty1QdXQQo1dXVAIC0tDSVV0IQBEEQhLdUV1cjKirKq8foYligw+FAfn4+IiIiYDAYJHveqqoqpKWlITc3l4YQKgi97upAr7s60OuuDvS6q0Pr110QBFRXVyMlJQVGo3eqEl1kUIxGI1JTU2V7/sjISHoDqwC97upAr7s60OuuDvS6q4P76+5t5oRDIlmCIAiCIDQHBSgEQRAEQWiOLh2gWK1WLFmyBFarVe2ldCnodVcHet3VgV53daDXXR2kfN11IZIlCIIgCKJr0aUzKARBEARBaBMKUAiCIAiC0BwUoBAEQRAEoTkoQCEIgiAIQnN06QBl2bJlSE9PR3BwMMaPH48dO3aovaSA5rHHHoPBYGjxM3DgQLWXFXBs3LgR06dPR0pKCgwGA1avXt3idkEQsHjxYiQnJyMkJASTJ0/GsWPH1FlsANHZ6z537tyz3v9Tp05VZ7EBwtKlSzF27FhEREQgISEBM2bMwJEjR1rcp6GhAfPnz0e3bt0QHh6Oa665BkVFRSqtODDw5HWfNGnSWe/322+/3av9dNkA5dNPP8XChQuxZMkS7NmzBxkZGZgyZQqKi4vVXlpAM2TIEBQUFIg/mzdvVntJAUdtbS0yMjKwbNmyNm9/9tln8corr+CNN97A9u3bERYWhilTpqChoUHhlQYWnb3uADB16tQW7/+PP/5YwRUGHr/88gvmz5+Pbdu2Ye3atWhqasJll12G2tpa8T733nsv/ve//+Hzzz/HL7/8gvz8fFx99dUqrlr/ePK6A8Btt93W4v3+7LPPercjoYsybtw4Yf78+eLvdrtdSElJEZYuXariqgKbJUuWCBkZGWovo0sBQFi1apX4u8PhEJKSkoTnnntOvK6iokKwWq3Cxx9/rMIKA5PWr7sgCMJNN90kXHnllaqsp6tQXFwsABB++eUXQRDYezsoKEj4/PPPxfscOnRIACBs3bpVrWUGHK1fd0EQhAsvvFC4++67/XreLplBsdls2L17NyZPnixeZzQaMXnyZGzdulXFlQU+x44dQ0pKCnr37o3Zs2cjJydH7SV1KbKyslBYWNjivR8VFYXx48fTe18BNmzYgISEBAwYMAB33HEHysrK1F5SQFFZWQkAiI2NBQDs3r0bTU1NLd7vAwcORI8ePej9LiGtX3fOihUrEBcXh6FDh2LRokWoq6vz6nl1MSxQakpLS2G325GYmNji+sTERBw+fFilVQU+48ePx/LlyzFgwAAUFBTg8ccfx/nnn48DBw4gIiJC7eV1CQoLCwGgzfc+v42Qh6lTp+Lqq69Gr169cOLECTz88MOYNm0atm7dCpPJpPbydI/D4cA999yDiRMnYujQoQDY+91isSA6OrrFfen9Lh1tve4AMGvWLPTs2RMpKSn47bff8OCDD+LIkSNYuXKlx8/dJQMUQh2mTZsmbg8fPhzjx49Hz5498dlnn+GWW25RcWUEIT/XX3+9uD1s2DAMHz4cffr0wYYNG3DJJZeouLLAYP78+Thw4ADp2hSmvdf9L3/5i7g9bNgwJCcn45JLLsGJEyfQp08fj567S5Z44uLiYDKZzlJyFxUVISkpSaVVdT2io6PRv39/HD9+XO2ldBn4+5ve++rTu3dvxMXF0ftfAhYsWICvv/4aP//8M1JTU8Xrk5KSYLPZUFFR0eL+9H6XhvZe97YYP348AHj1fu+SAYrFYsHo0aOxbt068TqHw4F169ZhwoQJKq6sa1FTU4MTJ04gOTlZ7aV0GXr16oWkpKQW7/2qqips376d3vsKc/r0aZSVldH73w8EQcCCBQuwatUqrF+/Hr169Wpx++jRoxEUFNTi/X7kyBHk5OTQ+90POnvd2yIzMxMAvHq/d9kSz8KFC3HTTTdhzJgxGDduHF566SXU1tZi3rx5ai8tYLnvvvswffp09OzZE/n5+ViyZAlMJhNuuOEGtZcWUNTU1LQ4S8nKykJmZiZiY2PRo0cP3HPPPXjqqafQr18/9OrVC48++ihSUlIwY8YM9RYdAHT0usfGxuLxxx/HNddcg6SkJJw4cQIPPPAA+vbtiylTpqi4an0zf/58fPTRR/jqq68QEREh6kqioqIQEhKCqKgo3HLLLVi4cCFiY2MRGRmJv/3tb5gwYQLOOecclVevXzp73U+cOIGPPvoIl19+Obp164bffvsN9957Ly644AIMHz7c8x351QOkc1599VWhR48egsViEcaNGyds27ZN7SUFNDNnzhSSk5MFi8UidO/eXZg5c6Zw/PhxtZcVcPz8888CgLN+brrpJkEQWKvxo48+KiQmJgpWq1W45JJLhCNHjqi76ACgo9e9rq5OuOyyy4T4+HghKChI6Nmzp3DbbbcJhYWFai9b17T1egMQ3nvvPfE+9fX1wp133inExMQIoaGhwlVXXSUUFBSot+gAoLPXPScnR7jggguE2NhYwWq1Cn379hXuv/9+obKy0qv9GJw7IwiCIAiC0AxdUoNCEARBEIS2oQCFIAiCIAjNQQEKQRAEQRCagwIUgiAIgiA0BwUoBEEQBEFoDgpQCIIgCILQHBSgEARBEAShOShAIQiCIAhCc1CAQhAEQRCE5qAAhSAIgiAIzUEBCkEQBEEQmoMCFIIgCIIgNMf/Ay62naWR9SEKAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1503,7 +2051,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1533,38 +2081,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_resnet.save('resnet_new_model_2.h5')"
+    "model.save('resnet_1.h5')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "72/72 [==============================] - 61s 843ms/step - loss: 0.7182 - accuracy: 0.8411\n"
+      "29/29 [==============================] - 55s 2s/step - loss: 0.3070 - accuracy: 0.9828\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[0.7181549072265625, 0.8411458134651184]"
+       "[0.30702900886535645, 0.982758641242981]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_resnet.evaluate(test_ds_r)"
+    "model.evaluate(test_ds_v)"
    ]
   }
  ],
@@ -1584,12 +2132,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.1"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
    }
   }
  },