diff --git a/sw_lab9-10_4.ipynb b/sw_lab9-10_4.ipynb index ed74ee2..e7ee52c 100644 --- a/sw_lab9-10_4.ipynb +++ b/sw_lab9-10_4.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "id": "f790226b", "metadata": {}, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "id": "44319623", "metadata": {}, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "id": "4e3ebfd0", "metadata": {}, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "id": "ffeda62d", "metadata": {}, "outputs": [], @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "id": "72c68d57", "metadata": {}, "outputs": [], @@ -176,109 +176,7 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "4859d197", - "metadata": {}, - "outputs": [], - "source": [ - "# directory = r\"train_test_sw/train_sw_jezu\"\n", - "# subdirs = [r\"/Tomato\", r\"/Lemon\", r\"/Beech\", r\"/Mean\", r\"/Gardenia\"]\n", - "\n", - "# json_entries = []\n", - "\n", - "# for sub in subdirs:\n", - "# path = directory + sub\n", - " \n", - "# for filename in os.listdir(path):\n", - "# f = os.path.join(path, filename)\n", - " \n", - "# if os.path.isfile(f):\n", - "# img = cv.imread(f)\n", - "\n", - "# img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n", - "# ddepth = cv.CV_16S\n", - "# kernel_size = 3\n", - "# laplacian_operator = cv.Laplacian(img_gray, ddepth, ksize=kernel_size)\n", - "# filename_edge = f[:-4] + '_laplacian.png'\n", - "# #final_edge = fix_float_img(adjusted)\n", - "# cv.imwrite(filename_edge, laplacian_operator)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "aedb7b9f", - "metadata": {}, - "outputs": [], - "source": [ - "# directory = r\"train_test_sw/train_sw\"\n", - "# subdirs = [r\"/Tomato\", r\"/Lemon\", r\"/Beech\", r\"/Mean\", r\"/Gardenia\"]\n", - "\n", - "# json_entries = []\n", - "\n", - "# for sub in subdirs:\n", - "# path = directory + sub\n", - " \n", - "# for filename in os.listdir(path):\n", - "# f = os.path.join(path, filename)\n", - " \n", - "# if os.path.isfile(f):\n", - "# img = cv.imread(f)\n", - "\n", - "# lab_image = cv.cvtColor(img, cv.COLOR_BGR2LAB)\n", - "# filename_edge = f[:-4] + '_lab.png'\n", - "# #final_edge = fix_float_img(adjusted)\n", - "# cv.imwrite(filename_edge, lab_image)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dc650af1", - "metadata": {}, - "outputs": [], - "source": [ - "# directory = r\"train_test_sw/train_sw_emboss\"\n", - "# subdirs = [r\"/Tomato\", r\"/Lemon\", r\"/Beech\", r\"/Mean\", r\"/Gardenia\"]\n", - "\n", - "# json_entries = []\n", - "\n", - "# for sub in subdirs:\n", - "# path = directory + sub\n", - " \n", - "# for filename in os.listdir(path):\n", - "# f = os.path.join(path, filename)\n", - " \n", - "# if os.path.isfile(f):\n", - "# img = cv.imread(f)\n", - "\n", - "# height, width = img.shape[:2]\n", - "# y = np.ones((height, width), np.uint8) * 128\n", - "# output = np.zeros((height, width), np.uint8)\n", - "# # generating the kernels\n", - "# kernel1 = np.array([[0, -1, -1], # kernel for embossing bottom left side\n", - "# [1, 0, -1],\n", - "# [1, 1, 0]])\n", - "# kernel2 = np.array([[-1, -1, 0], # kernel for embossing bottom right side\n", - "# [-1, 0, 1],\n", - "# [0, 1, 1]])\n", - "# # you can generate kernels for embossing top as well\n", - "# gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n", - "# output1 = cv.add(cv.filter2D(gray, -1, kernel1), y) # emboss on bottom left side\n", - "# output2 = cv.add(cv.filter2D(gray, -1, kernel2), y) # emboss on bottom right side\n", - "# for i in range(height):\n", - "# for j in range(width):\n", - "# output[i, j] = max(output1[i, j], output2[i, j]) # combining both embosses to produce stronger emboss\n", - "\n", - "# filename_edge = f[:-4] + '_emboss.png'\n", - "# #final_edge = fix_float_img(adjusted)\n", - "# cv.imwrite(filename_edge, output)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "id": "6a3f8c81", "metadata": {}, "outputs": [], @@ -324,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "id": "c4f0f653", "metadata": {}, "outputs": [], @@ -439,6 +337,8 @@ " for e in objects:\n", " p = image_path / e['filename']\n", " img = imread(p)#zwraca ndarry postaci xSize x ySize x colorDepth\n", + " if img.shape[-1] == 4:\n", + " img = img[:, :, :3]\n", " img = cv.resize(img, newSize, interpolation=cv.INTER_AREA)# zwraca ndarray\n", " img = img / 255#normalizacja\n", " test_img.append(img)\n", @@ -455,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "id": "b0dceacc", "metadata": {}, "outputs": [], @@ -501,130 +401,6 @@ " " ] }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "b1aa7ac3", - "metadata": {}, - "source": [ - "### Emboss" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "adf27f44", - "metadata": {}, - "outputs": [], - "source": [ - "# train_ds\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5333a4e6", - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b4ff7bba", - "metadata": {}, - "outputs": [], - "source": [ - "# train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()\n", - "# test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()\n", - "# validation_ds_size = tf.data.experimental.cardinality(validation_ds).numpy()\n", - "# print(\"Training data size:\", train_ds_size)\n", - "# print(\"Test data size:\", test_ds_size)\n", - "# print(\"Validation data size:\", validation_ds_size)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5dd609ea", - "metadata": {}, - "outputs": [], - "source": [ - "# alexnet.compile(loss='sparse_categorical_crossentropy', optimizer=tf.optimizers.SGD(lr=.001), metrics=['accuracy'])\n", - "# alexnet.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "220824d7", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# checkpoint = ModelCheckpoint(\"alex_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", - "\n", - "# alex = alexnet.fit_generator(\n", - "# steps_per_epoch=len(train_ds), \n", - "# generator=train_ds, \n", - "# validation_data= validation_ds, \n", - "# validation_steps=len(validation_ds), \n", - "# epochs=25, \n", - "# callbacks=[checkpoint,early])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f87a88e9", - "metadata": {}, - "outputs": [], - "source": [ - "# model_flat_drop.fit(train_ds,\n", - "# epochs=100,\n", - "# validation_data=validation_ds,\n", - "# validation_freq=1,\n", - "# callbacks=[tensorboard_cb])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f7a72530", - "metadata": {}, - "source": [ - "### Saturacja" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "6148789c", - "metadata": {}, - "outputs": [], - "source": [ - "# from sklearn.preprocessing import LabelEncoder\n", - "\n", - "# # Data load\n", - "# data_train = load_train_data(\"train_test_sw_kontrast/train_sw\", newSize=(16,16))\n", - "# X_train = data_train['values']\n", - "# y_train = data_train['labels']\n", - "\n", - "# data_test = load_test_data(\"train_test_sw_kontrast/test_sw\", newSize=(16,16))\n", - "# X_test = data_test['values']\n", - "# y_test = data_test['labels']\n", - "\n", - "# class_le = LabelEncoder()\n", - "# y_train_enc = class_le.fit_transform(y_train)\n", - "# y_test_enc = class_le.fit_transform(y_test)\n", - "\n", - "# X_train = X_train.flatten().reshape(X_train.shape[0], int(np.prod(X_train.shape) / X_train.shape[0]))\n", - "# X_test = X_test.flatten().reshape(X_test.shape[0], int(np.prod(X_test.shape) / X_test.shape[0]))" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -636,227 +412,27 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "id": "108a46e4", "metadata": {}, "outputs": [], "source": [ - "filters = ['laplasian', 'kontrast', 'cartoon', 'saturacja', 'emboss']" + "filters = ['kontrast', 'cartoon', 'saturacja']" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "id": "12a16bca", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv2d (Conv2D) (None, 55, 55, 96) 34944 \n", - " \n", - " batch_normalization (BatchN (None, 55, 55, 96) 384 \n", - " ormalization) \n", - " \n", - " max_pooling2d (MaxPooling2D (None, 27, 27, 96) 0 \n", - " ) \n", - " \n", - " conv2d_1 (Conv2D) (None, 27, 27, 256) 614656 \n", - " \n", - " batch_normalization_1 (Batc (None, 27, 27, 256) 1024 \n", - " hNormalization) \n", - " \n", - " max_pooling2d_1 (MaxPooling (None, 13, 13, 256) 0 \n", - " 2D) \n", - " \n", - " conv2d_2 (Conv2D) (None, 13, 13, 384) 885120 \n", - " \n", - " batch_normalization_2 (Batc (None, 13, 13, 384) 1536 \n", - " hNormalization) \n", - " \n", - " conv2d_3 (Conv2D) (None, 13, 13, 384) 1327488 \n", - " \n", - " batch_normalization_3 (Batc (None, 13, 13, 384) 1536 \n", - " hNormalization) \n", - " \n", - " conv2d_4 (Conv2D) (None, 13, 13, 256) 884992 \n", - " \n", - " batch_normalization_4 (Batc (None, 13, 13, 256) 1024 \n", - " hNormalization) \n", - " \n", - " max_pooling2d_2 (MaxPooling (None, 6, 6, 256) 0 \n", - " 2D) \n", - " \n", - " flatten (Flatten) (None, 9216) 0 \n", - " \n", - " dense (Dense) (None, 4096) 37752832 \n", - " \n", - " dropout (Dropout) (None, 4096) 0 \n", - " \n", - " dense_1 (Dense) (None, 4096) 16781312 \n", - " \n", - " dropout_1 (Dropout) (None, 4096) 0 \n", - " \n", - " dense_2 (Dense) (None, 10) 40970 \n", - " \n", - "=================================================================\n", - "Total params: 58,327,818\n", - "Trainable params: 58,325,066\n", - "Non-trainable params: 2,752\n", - "_________________________________________________________________\n", - "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" + "kontrast ---------------------------------------\n" ] }, - { - "name": "stderr", - "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/6b/j4d60ym516x2s6wymzj707rh0000gn/T/ipykernel_35367/157534861.py:34: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n", - " alex = alexnet.fit_generator(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/25\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-01-11 15:57:37.093304: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "51/51 [==============================] - ETA: 0s - loss: 3.5934 - accuracy: 0.3903\n", - "Epoch 1: val_accuracy improved from -inf to 0.24740, saving model to alex_2.h5\n", - "51/51 [==============================] - 46s 888ms/step - loss: 3.5934 - accuracy: 0.3903 - val_loss: 1.9219 - val_accuracy: 0.2474\n", - "Epoch 2/25\n", - "51/51 [==============================] - ETA: 0s - loss: 1.2680 - accuracy: 0.5699\n", - "Epoch 2: val_accuracy did not improve from 0.24740\n", - "51/51 [==============================] - 45s 890ms/step - loss: 1.2680 - accuracy: 0.5699 - val_loss: 2.9384 - val_accuracy: 0.2370\n", - "Epoch 3/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.8801 - accuracy: 0.6930\n", - "Epoch 3: val_accuracy did not improve from 0.24740\n", - "51/51 [==============================] - 51s 1s/step - loss: 0.8801 - accuracy: 0.6930 - val_loss: 4.2987 - val_accuracy: 0.2318\n", - "Epoch 4/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.8070 - accuracy: 0.7181\n", - "Epoch 4: val_accuracy improved from 0.24740 to 0.27604, saving model to alex_2.h5\n", - "51/51 [==============================] - 47s 925ms/step - loss: 0.8070 - accuracy: 0.7181 - val_loss: 5.2133 - val_accuracy: 0.2760\n", - "Epoch 5/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.6284 - accuracy: 0.7714\n", - "Epoch 5: val_accuracy improved from 0.27604 to 0.28906, saving model to alex_2.h5\n", - "51/51 [==============================] - 52s 1s/step - loss: 0.6284 - accuracy: 0.7714 - val_loss: 5.1982 - val_accuracy: 0.2891\n", - "Epoch 6/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.5519 - accuracy: 0.7996\n", - "Epoch 6: val_accuracy improved from 0.28906 to 0.34635, saving model to alex_2.h5\n", - "51/51 [==============================] - 47s 925ms/step - loss: 0.5519 - accuracy: 0.7996 - val_loss: 5.3340 - val_accuracy: 0.3464\n", - "Epoch 7/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.5127 - accuracy: 0.8205\n", - "Epoch 7: val_accuracy did not improve from 0.34635\n", - "51/51 [==============================] - 48s 934ms/step - loss: 0.5127 - accuracy: 0.8205 - val_loss: 4.6689 - val_accuracy: 0.3307\n", - "Epoch 8/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.4584 - accuracy: 0.8364\n", - "Epoch 8: val_accuracy improved from 0.34635 to 0.34896, saving model to alex_2.h5\n", - "51/51 [==============================] - 48s 939ms/step - loss: 0.4584 - accuracy: 0.8364 - val_loss: 4.0851 - val_accuracy: 0.3490\n", - "Epoch 9/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.3952 - accuracy: 0.8585\n", - "Epoch 9: val_accuracy improved from 0.34896 to 0.39844, saving model to alex_2.h5\n", - "51/51 [==============================] - 49s 955ms/step - loss: 0.3952 - accuracy: 0.8585 - val_loss: 2.6378 - val_accuracy: 0.3984\n", - "Epoch 10/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.3141 - accuracy: 0.8811\n", - "Epoch 10: val_accuracy improved from 0.39844 to 0.43750, saving model to alex_2.h5\n", - "51/51 [==============================] - 48s 940ms/step - loss: 0.3141 - accuracy: 0.8811 - val_loss: 2.3606 - val_accuracy: 0.4375\n", - "Epoch 11/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.2889 - accuracy: 0.8922\n", - "Epoch 11: val_accuracy improved from 0.43750 to 0.65625, saving model to alex_2.h5\n", - "51/51 [==============================] - 48s 949ms/step - loss: 0.2889 - accuracy: 0.8922 - val_loss: 1.1387 - val_accuracy: 0.6562\n", - "Epoch 12/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.2696 - accuracy: 0.8977\n", - "Epoch 12: val_accuracy did not improve from 0.65625\n", - "51/51 [==============================] - 48s 933ms/step - loss: 0.2696 - accuracy: 0.8977 - val_loss: 1.1794 - val_accuracy: 0.6328\n", - "Epoch 13/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.2124 - accuracy: 0.9271\n", - "Epoch 13: val_accuracy improved from 0.65625 to 0.83073, saving model to alex_2.h5\n", - "51/51 [==============================] - 50s 973ms/step - loss: 0.2124 - accuracy: 0.9271 - val_loss: 0.4526 - val_accuracy: 0.8307\n", - "Epoch 14/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.1891 - accuracy: 0.9228\n", - "Epoch 14: val_accuracy did not improve from 0.83073\n", - "51/51 [==============================] - 50s 981ms/step - loss: 0.1891 - accuracy: 0.9228 - val_loss: 0.5985 - val_accuracy: 0.7943\n", - "Epoch 15/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.1603 - accuracy: 0.9381\n", - "Epoch 15: val_accuracy improved from 0.83073 to 0.83333, saving model to alex_2.h5\n", - "51/51 [==============================] - 50s 983ms/step - loss: 0.1603 - accuracy: 0.9381 - val_loss: 0.4779 - val_accuracy: 0.8333\n", - "Epoch 16/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.1852 - accuracy: 0.9314\n", - "Epoch 16: val_accuracy improved from 0.83333 to 0.86979, saving model to alex_2.h5\n", - "51/51 [==============================] - 49s 962ms/step - loss: 0.1852 - accuracy: 0.9314 - val_loss: 0.3588 - val_accuracy: 0.8698\n", - "Epoch 17/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.1484 - accuracy: 0.9504\n", - "Epoch 17: val_accuracy improved from 0.86979 to 0.87500, saving model to alex_2.h5\n", - "51/51 [==============================] - 49s 963ms/step - loss: 0.1484 - accuracy: 0.9504 - val_loss: 0.3464 - val_accuracy: 0.8750\n", - "Epoch 18/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.1367 - accuracy: 0.9534\n", - "Epoch 18: val_accuracy did not improve from 0.87500\n", - "51/51 [==============================] - 49s 962ms/step - loss: 0.1367 - accuracy: 0.9534 - val_loss: 0.4452 - val_accuracy: 0.8464\n", - "Epoch 19/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.1089 - accuracy: 0.9638\n", - "Epoch 19: val_accuracy improved from 0.87500 to 0.89062, saving model to alex_2.h5\n", - "51/51 [==============================] - 49s 953ms/step - loss: 0.1089 - accuracy: 0.9638 - val_loss: 0.3376 - val_accuracy: 0.8906\n", - "Epoch 20/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.1115 - accuracy: 0.9596\n", - "Epoch 20: val_accuracy did not improve from 0.89062\n", - "51/51 [==============================] - 49s 954ms/step - loss: 0.1115 - accuracy: 0.9596 - val_loss: 0.3655 - val_accuracy: 0.8854\n", - "Epoch 21/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.0793 - accuracy: 0.9681\n", - "Epoch 21: val_accuracy did not improve from 0.89062\n", - "51/51 [==============================] - 48s 949ms/step - loss: 0.0793 - accuracy: 0.9681 - val_loss: 0.4086 - val_accuracy: 0.8776\n", - "Epoch 22/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.0725 - accuracy: 0.9767\n", - "Epoch 22: val_accuracy improved from 0.89062 to 0.90365, saving model to alex_2.h5\n", - "51/51 [==============================] - 49s 958ms/step - loss: 0.0725 - accuracy: 0.9767 - val_loss: 0.2975 - val_accuracy: 0.9036\n", - "Epoch 23/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.0727 - accuracy: 0.9755\n", - "Epoch 23: val_accuracy did not improve from 0.90365\n", - "51/51 [==============================] - 49s 957ms/step - loss: 0.0727 - accuracy: 0.9755 - val_loss: 0.4552 - val_accuracy: 0.8698\n", - "Epoch 24/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.0659 - accuracy: 0.9737\n", - "Epoch 24: val_accuracy did not improve from 0.90365\n", - "51/51 [==============================] - 49s 952ms/step - loss: 0.0659 - accuracy: 0.9737 - val_loss: 0.3930 - val_accuracy: 0.8854\n", - "Epoch 25/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.0693 - accuracy: 0.9816\n", - "Epoch 25: val_accuracy did not improve from 0.90365\n", - "51/51 [==============================] - 50s 980ms/step - loss: 0.0693 - accuracy: 0.9816 - val_loss: 0.6543 - val_accuracy: 0.8177\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stderr", "output_type": "stream", @@ -937,20 +513,537 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/25\n", - "51/51 [==============================] - ETA: 0s - loss: 3.6077 - accuracy: 0.3566\n", - "Epoch 1: val_accuracy improved from -inf to 0.26302, saving model to alex_2.h5\n", - "51/51 [==============================] - 51s 994ms/step - loss: 3.6077 - accuracy: 0.3566 - val_loss: 1.8337 - val_accuracy: 0.2630\n", + "Epoch 1/25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/6b/j4d60ym516x2s6wymzj707rh0000gn/T/ipykernel_35974/3983922004.py:34: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n", + " alex = alexnet.fit_generator(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "51/51 [==============================] - ETA: 0s - loss: 3.8345 - accuracy: 0.3658\n", + "Epoch 1: val_accuracy improved from -inf to 0.22656, saving model to alex_2.h5\n", + "51/51 [==============================] - 46s 891ms/step - loss: 3.8345 - accuracy: 0.3658 - val_loss: 2.1574 - val_accuracy: 0.2266\n", "Epoch 2/25\n", - "51/51 [==============================] - ETA: 0s - loss: 1.2408 - accuracy: 0.5803\n", - "Epoch 2: val_accuracy improved from 0.26302 to 0.34375, saving model to alex_2.h5\n", - "51/51 [==============================] - 53s 1s/step - loss: 1.2408 - accuracy: 0.5803 - val_loss: 2.8576 - val_accuracy: 0.3438\n", + "51/51 [==============================] - ETA: 0s - loss: 1.3397 - accuracy: 0.5362\n", + "Epoch 2: val_accuracy improved from 0.22656 to 0.23177, saving model to alex_2.h5\n", + "51/51 [==============================] - 48s 945ms/step - loss: 1.3397 - accuracy: 0.5362 - val_loss: 2.7271 - val_accuracy: 0.2318\n", "Epoch 3/25\n", - "51/51 [==============================] - ETA: 0s - loss: 0.9538 - accuracy: 0.6550\n", - "Epoch 3: val_accuracy improved from 0.34375 to 0.35677, saving model to alex_2.h5\n", - "51/51 [==============================] - 56s 1s/step - loss: 0.9538 - accuracy: 0.6550 - val_loss: 4.7057 - val_accuracy: 0.3568\n", + "51/51 [==============================] - ETA: 0s - loss: 0.9793 - accuracy: 0.6428\n", + "Epoch 3: val_accuracy improved from 0.23177 to 0.34635, saving model to alex_2.h5\n", + "51/51 [==============================] - 49s 954ms/step - loss: 0.9793 - accuracy: 0.6428 - val_loss: 3.4108 - val_accuracy: 0.3464\n", "Epoch 4/25\n", - "44/51 [========================>.....] - ETA: 7s - loss: 0.7304 - accuracy: 0.7273" + "51/51 [==============================] - ETA: 0s - loss: 0.6715 - accuracy: 0.7273\n", + "Epoch 4: val_accuracy did not improve from 0.34635\n", + "51/51 [==============================] - 51s 1s/step - loss: 0.6715 - accuracy: 0.7273 - val_loss: 4.2069 - val_accuracy: 0.3411\n", + "Epoch 5/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.5853 - accuracy: 0.7917\n", + "Epoch 5: val_accuracy did not improve from 0.34635\n", + "51/51 [==============================] - 49s 962ms/step - loss: 0.5853 - accuracy: 0.7917 - val_loss: 4.3773 - val_accuracy: 0.2839\n", + "Epoch 6/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.4176 - accuracy: 0.8413\n", + "Epoch 6: val_accuracy did not improve from 0.34635\n", + "51/51 [==============================] - 49s 961ms/step - loss: 0.4176 - accuracy: 0.8413 - val_loss: 5.1601 - val_accuracy: 0.3281\n", + "Epoch 7/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.3224 - accuracy: 0.8756\n", + "Epoch 7: val_accuracy did not improve from 0.34635\n", + "51/51 [==============================] - 49s 957ms/step - loss: 0.3224 - accuracy: 0.8756 - val_loss: 5.2943 - val_accuracy: 0.3307\n", + "Epoch 8/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2591 - accuracy: 0.9026\n", + "Epoch 8: val_accuracy improved from 0.34635 to 0.41406, saving model to alex_2.h5\n", + "51/51 [==============================] - 50s 985ms/step - loss: 0.2591 - accuracy: 0.9026 - val_loss: 3.7030 - val_accuracy: 0.4141\n", + "Epoch 9/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2748 - accuracy: 0.8964\n", + "Epoch 9: val_accuracy improved from 0.41406 to 0.50000, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.2748 - accuracy: 0.8964 - val_loss: 2.1064 - val_accuracy: 0.5000\n", + "Epoch 10/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2015 - accuracy: 0.9240\n", + "Epoch 10: val_accuracy improved from 0.50000 to 0.62500, saving model to alex_2.h5\n", + "51/51 [==============================] - 52s 1s/step - loss: 0.2015 - accuracy: 0.9240 - val_loss: 1.3254 - val_accuracy: 0.6250\n", + "Epoch 11/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1754 - accuracy: 0.9350\n", + "Epoch 11: val_accuracy improved from 0.62500 to 0.74740, saving model to alex_2.h5\n", + "51/51 [==============================] - 54s 1s/step - loss: 0.1754 - accuracy: 0.9350 - val_loss: 0.7914 - val_accuracy: 0.7474\n", + "Epoch 12/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1711 - accuracy: 0.9412 \n", + "Epoch 12: val_accuracy did not improve from 0.74740\n", + "51/51 [==============================] - 738s 15s/step - loss: 0.1711 - accuracy: 0.9412 - val_loss: 1.0148 - val_accuracy: 0.7031\n", + "Epoch 13/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1424 - accuracy: 0.9498\n", + "Epoch 13: val_accuracy improved from 0.74740 to 0.82031, saving model to alex_2.h5\n", + "51/51 [==============================] - 49s 951ms/step - loss: 0.1424 - accuracy: 0.9498 - val_loss: 0.5437 - val_accuracy: 0.8203\n", + "Epoch 14/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1434 - accuracy: 0.9418\n", + "Epoch 14: val_accuracy improved from 0.82031 to 0.83594, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.1434 - accuracy: 0.9418 - val_loss: 0.4773 - val_accuracy: 0.8359\n", + "Epoch 15/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0943 - accuracy: 0.9681\n", + "Epoch 15: val_accuracy did not improve from 0.83594\n", + "51/51 [==============================] - 50s 974ms/step - loss: 0.0943 - accuracy: 0.9681 - val_loss: 0.6302 - val_accuracy: 0.8125\n", + "Epoch 16/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0859 - accuracy: 0.9669\n", + "Epoch 16: val_accuracy improved from 0.83594 to 0.93229, saving model to alex_2.h5\n", + "51/51 [==============================] - 48s 948ms/step - loss: 0.0859 - accuracy: 0.9669 - val_loss: 0.2049 - val_accuracy: 0.9323\n", + "Epoch 17/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0849 - accuracy: 0.9688\n", + "Epoch 17: val_accuracy did not improve from 0.93229\n", + "51/51 [==============================] - 55s 1s/step - loss: 0.0849 - accuracy: 0.9688 - val_loss: 0.3428 - val_accuracy: 0.8932\n", + "Epoch 18/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0876 - accuracy: 0.9712\n", + "Epoch 18: val_accuracy did not improve from 0.93229\n", + "51/51 [==============================] - 78s 2s/step - loss: 0.0876 - accuracy: 0.9712 - val_loss: 0.7060 - val_accuracy: 0.8151\n", + "Epoch 19/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0708 - accuracy: 0.9737\n", + "Epoch 19: val_accuracy improved from 0.93229 to 0.94271, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.0708 - accuracy: 0.9737 - val_loss: 0.1935 - val_accuracy: 0.9427\n", + "Epoch 20/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0829 - accuracy: 0.9657\n", + "Epoch 20: val_accuracy did not improve from 0.94271\n", + "51/51 [==============================] - 67s 1s/step - loss: 0.0829 - accuracy: 0.9657 - val_loss: 0.1955 - val_accuracy: 0.9375\n", + "Epoch 21/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0404 - accuracy: 0.9865\n", + "Epoch 21: val_accuracy improved from 0.94271 to 0.95312, saving model to alex_2.h5\n", + "51/51 [==============================] - 140s 3s/step - loss: 0.0404 - accuracy: 0.9865 - val_loss: 0.1493 - val_accuracy: 0.9531\n", + "Epoch 22/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0370 - accuracy: 0.9877\n", + "Epoch 22: val_accuracy did not improve from 0.95312\n", + "51/51 [==============================] - 64s 1s/step - loss: 0.0370 - accuracy: 0.9877 - val_loss: 0.1635 - val_accuracy: 0.9505\n", + "Epoch 23/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0353 - accuracy: 0.9865\n", + "Epoch 23: val_accuracy did not improve from 0.95312\n", + "51/51 [==============================] - 89s 2s/step - loss: 0.0353 - accuracy: 0.9865 - val_loss: 0.4217 - val_accuracy: 0.8932\n", + "Epoch 24/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0308 - accuracy: 0.9920\n", + "Epoch 24: val_accuracy did not improve from 0.95312\n", + "51/51 [==============================] - 133s 3s/step - loss: 0.0308 - accuracy: 0.9920 - val_loss: 0.2005 - val_accuracy: 0.9349\n", + "Epoch 25/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0203 - accuracy: 0.9957\n", + "Epoch 25: val_accuracy improved from 0.95312 to 0.95573, saving model to alex_2.h5\n", + "51/51 [==============================] - 54s 1s/step - loss: 0.0203 - accuracy: 0.9957 - val_loss: 0.1394 - val_accuracy: 0.9557\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 [==============================] - 2s 256ms/step - loss: 0.2136 - accuracy: 0.9375\n", + "cartoon ---------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_10 (Conv2D) (None, 55, 55, 96) 34944 \n", + " \n", + " batch_normalization_10 (Bat (None, 55, 55, 96) 384 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_6 (MaxPooling (None, 27, 27, 96) 0 \n", + " 2D) \n", + " \n", + " conv2d_11 (Conv2D) (None, 27, 27, 256) 614656 \n", + " \n", + " batch_normalization_11 (Bat (None, 27, 27, 256) 1024 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_7 (MaxPooling (None, 13, 13, 256) 0 \n", + " 2D) \n", + " \n", + " conv2d_12 (Conv2D) (None, 13, 13, 384) 885120 \n", + " \n", + " batch_normalization_12 (Bat (None, 13, 13, 384) 1536 \n", + " chNormalization) \n", + " \n", + " conv2d_13 (Conv2D) (None, 13, 13, 384) 1327488 \n", + " \n", + " batch_normalization_13 (Bat (None, 13, 13, 384) 1536 \n", + " chNormalization) \n", + " \n", + " conv2d_14 (Conv2D) (None, 13, 13, 256) 884992 \n", + " \n", + " batch_normalization_14 (Bat (None, 13, 13, 256) 1024 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_8 (MaxPooling (None, 6, 6, 256) 0 \n", + " 2D) \n", + " \n", + " flatten_2 (Flatten) (None, 9216) 0 \n", + " \n", + " dense_6 (Dense) (None, 4096) 37752832 \n", + " \n", + " dropout_4 (Dropout) (None, 4096) 0 \n", + " \n", + " dense_7 (Dense) (None, 4096) 16781312 \n", + " \n", + " dropout_5 (Dropout) (None, 4096) 0 \n", + " \n", + " dense_8 (Dense) (None, 10) 40970 \n", + " \n", + "=================================================================\n", + "Total params: 58,327,818\n", + "Trainable params: 58,325,066\n", + "Non-trainable params: 2,752\n", + "_________________________________________________________________\n", + "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/25\n", + "51/51 [==============================] - ETA: 0s - loss: 3.3183 - accuracy: 0.4295\n", + "Epoch 1: val_accuracy improved from -inf to 0.23177, saving model to alex_2.h5\n", + "51/51 [==============================] - 49s 942ms/step - loss: 3.3183 - accuracy: 0.4295 - val_loss: 2.0209 - val_accuracy: 0.2318\n", + "Epoch 2/25\n", + "51/51 [==============================] - ETA: 0s - loss: 1.0712 - accuracy: 0.6654\n", + "Epoch 2: val_accuracy did not improve from 0.23177\n", + "51/51 [==============================] - 52s 1s/step - loss: 1.0712 - accuracy: 0.6654 - val_loss: 2.9587 - val_accuracy: 0.2188\n", + "Epoch 3/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.6603 - accuracy: 0.7739\n", + "Epoch 3: val_accuracy improved from 0.23177 to 0.31250, saving model to alex_2.h5\n", + "51/51 [==============================] - 55s 1s/step - loss: 0.6603 - accuracy: 0.7739 - val_loss: 3.3996 - val_accuracy: 0.3125\n", + "Epoch 4/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.5013 - accuracy: 0.8070\n", + "Epoch 4: val_accuracy improved from 0.31250 to 0.32031, saving model to alex_2.h5\n", + "51/51 [==============================] - 54s 1s/step - loss: 0.5013 - accuracy: 0.8070 - val_loss: 4.6634 - val_accuracy: 0.3203\n", + "Epoch 5/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.3286 - accuracy: 0.8762\n", + "Epoch 5: val_accuracy did not improve from 0.32031\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.3286 - accuracy: 0.8762 - val_loss: 5.9495 - val_accuracy: 0.2109\n", + "Epoch 6/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2392 - accuracy: 0.9124\n", + "Epoch 6: val_accuracy did not improve from 0.32031\n", + "51/51 [==============================] - 59s 1s/step - loss: 0.2392 - accuracy: 0.9124 - val_loss: 6.1043 - val_accuracy: 0.2760\n", + "Epoch 7/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2096 - accuracy: 0.9216\n", + "Epoch 7: val_accuracy did not improve from 0.32031\n", + "51/51 [==============================] - 51s 995ms/step - loss: 0.2096 - accuracy: 0.9216 - val_loss: 6.5559 - val_accuracy: 0.2422\n", + "Epoch 8/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1786 - accuracy: 0.9387\n", + "Epoch 8: val_accuracy improved from 0.32031 to 0.34115, saving model to alex_2.h5\n", + "51/51 [==============================] - 47s 913ms/step - loss: 0.1786 - accuracy: 0.9387 - val_loss: 5.2047 - val_accuracy: 0.3411\n", + "Epoch 9/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1700 - accuracy: 0.9387\n", + "Epoch 9: val_accuracy improved from 0.34115 to 0.41667, saving model to alex_2.h5\n", + "51/51 [==============================] - 47s 914ms/step - loss: 0.1700 - accuracy: 0.9387 - val_loss: 3.7162 - val_accuracy: 0.4167\n", + "Epoch 10/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1615 - accuracy: 0.9430\n", + "Epoch 10: val_accuracy improved from 0.41667 to 0.59375, saving model to alex_2.h5\n", + "51/51 [==============================] - 47s 915ms/step - loss: 0.1615 - accuracy: 0.9430 - val_loss: 1.8405 - val_accuracy: 0.5938\n", + "Epoch 11/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1049 - accuracy: 0.9602\n", + "Epoch 11: val_accuracy improved from 0.59375 to 0.66406, saving model to alex_2.h5\n", + "51/51 [==============================] - 51s 1s/step - loss: 0.1049 - accuracy: 0.9602 - val_loss: 1.1911 - val_accuracy: 0.6641\n", + "Epoch 12/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0944 - accuracy: 0.9657\n", + "Epoch 12: val_accuracy improved from 0.66406 to 0.76823, saving model to alex_2.h5\n", + "51/51 [==============================] - 54s 1s/step - loss: 0.0944 - accuracy: 0.9657 - val_loss: 0.8048 - val_accuracy: 0.7682\n", + "Epoch 13/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0714 - accuracy: 0.9761\n", + "Epoch 13: val_accuracy improved from 0.76823 to 0.96615, saving model to alex_2.h5\n", + "51/51 [==============================] - 112s 2s/step - loss: 0.0714 - accuracy: 0.9761 - val_loss: 0.0924 - val_accuracy: 0.9661\n", + "Epoch 14/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0788 - accuracy: 0.9694\n", + "Epoch 14: val_accuracy did not improve from 0.96615\n", + "51/51 [==============================] - 109s 2s/step - loss: 0.0788 - accuracy: 0.9694 - val_loss: 0.1619 - val_accuracy: 0.9323\n", + "Epoch 15/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0630 - accuracy: 0.9847\n", + "Epoch 15: val_accuracy did not improve from 0.96615\n", + "51/51 [==============================] - 59s 1s/step - loss: 0.0630 - accuracy: 0.9847 - val_loss: 0.3735 - val_accuracy: 0.8750\n", + "Epoch 16/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9779\n", + "Epoch 16: val_accuracy did not improve from 0.96615\n", + "51/51 [==============================] - 49s 967ms/step - loss: 0.0662 - accuracy: 0.9779 - val_loss: 0.1856 - val_accuracy: 0.9193\n", + "Epoch 17/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0492 - accuracy: 0.9816\n", + "Epoch 17: val_accuracy did not improve from 0.96615\n", + "51/51 [==============================] - 48s 945ms/step - loss: 0.0492 - accuracy: 0.9816 - val_loss: 0.2103 - val_accuracy: 0.9271\n", + "Epoch 18/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0420 - accuracy: 0.9871\n", + "Epoch 18: val_accuracy did not improve from 0.96615\n", + "51/51 [==============================] - 48s 946ms/step - loss: 0.0420 - accuracy: 0.9871 - val_loss: 0.7410 - val_accuracy: 0.8411\n", + "Epoch 19/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0580 - accuracy: 0.9792\n", + "Epoch 19: val_accuracy improved from 0.96615 to 0.98958, saving model to alex_2.h5\n", + "51/51 [==============================] - 51s 993ms/step - loss: 0.0580 - accuracy: 0.9792 - val_loss: 0.0379 - val_accuracy: 0.9896\n", + "Epoch 20/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0471 - accuracy: 0.9853\n", + "Epoch 20: val_accuracy did not improve from 0.98958\n", + "51/51 [==============================] - 49s 961ms/step - loss: 0.0471 - accuracy: 0.9853 - val_loss: 1.3082 - val_accuracy: 0.7526\n", + "Epoch 21/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0391 - accuracy: 0.9890\n", + "Epoch 21: val_accuracy did not improve from 0.98958\n", + "51/51 [==============================] - 55s 1s/step - loss: 0.0391 - accuracy: 0.9890 - val_loss: 0.1507 - val_accuracy: 0.9323\n", + "Epoch 22/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0351 - accuracy: 0.9896\n", + "Epoch 22: val_accuracy did not improve from 0.98958\n", + "51/51 [==============================] - 55s 1s/step - loss: 0.0351 - accuracy: 0.9896 - val_loss: 0.1305 - val_accuracy: 0.9479\n", + "Epoch 23/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0231 - accuracy: 0.9933\n", + "Epoch 23: val_accuracy did not improve from 0.98958\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.0231 - accuracy: 0.9933 - val_loss: 0.0865 - val_accuracy: 0.9635\n", + "Epoch 24/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0201 - accuracy: 0.9933\n", + "Epoch 24: val_accuracy did not improve from 0.98958\n", + "51/51 [==============================] - 56s 1s/step - loss: 0.0201 - accuracy: 0.9933 - val_loss: 0.5474 - val_accuracy: 0.8281\n", + "Epoch 25/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0346 - accuracy: 0.9896\n", + "Epoch 25: val_accuracy did not improve from 0.98958\n", + "51/51 [==============================] - 56s 1s/step - loss: 0.0346 - accuracy: 0.9896 - val_loss: 0.0609 - val_accuracy: 0.9844\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 [==============================] - 3s 318ms/step - loss: 0.2321 - accuracy: 0.9297\n", + "saturacja ---------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_15 (Conv2D) (None, 55, 55, 96) 34944 \n", + " \n", + " batch_normalization_15 (Bat (None, 55, 55, 96) 384 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_9 (MaxPooling (None, 27, 27, 96) 0 \n", + " 2D) \n", + " \n", + " conv2d_16 (Conv2D) (None, 27, 27, 256) 614656 \n", + " \n", + " batch_normalization_16 (Bat (None, 27, 27, 256) 1024 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_10 (MaxPoolin (None, 13, 13, 256) 0 \n", + " g2D) \n", + " \n", + " conv2d_17 (Conv2D) (None, 13, 13, 384) 885120 \n", + " \n", + " batch_normalization_17 (Bat (None, 13, 13, 384) 1536 \n", + " chNormalization) \n", + " \n", + " conv2d_18 (Conv2D) (None, 13, 13, 384) 1327488 \n", + " \n", + " batch_normalization_18 (Bat (None, 13, 13, 384) 1536 \n", + " chNormalization) \n", + " \n", + " conv2d_19 (Conv2D) (None, 13, 13, 256) 884992 \n", + " \n", + " batch_normalization_19 (Bat (None, 13, 13, 256) 1024 \n", + " chNormalization) \n", + " \n", + " max_pooling2d_11 (MaxPoolin (None, 6, 6, 256) 0 \n", + " g2D) \n", + " \n", + " flatten_3 (Flatten) (None, 9216) 0 \n", + " \n", + " dense_9 (Dense) (None, 4096) 37752832 \n", + " \n", + " dropout_6 (Dropout) (None, 4096) 0 \n", + " \n", + " dense_10 (Dense) (None, 4096) 16781312 \n", + " \n", + " dropout_7 (Dropout) (None, 4096) 0 \n", + " \n", + " dense_11 (Dense) (None, 10) 40970 \n", + " \n", + "=================================================================\n", + "Total params: 58,327,818\n", + "Trainable params: 58,325,066\n", + "Non-trainable params: 2,752\n", + "_________________________________________________________________\n", + "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/25\n", + "51/51 [==============================] - ETA: 0s - loss: 3.6670 - accuracy: 0.3793\n", + "Epoch 1: val_accuracy improved from -inf to 0.38542, saving model to alex_2.h5\n", + "51/51 [==============================] - 49s 953ms/step - loss: 3.6670 - accuracy: 0.3793 - val_loss: 1.8499 - val_accuracy: 0.3854\n", + "Epoch 2/25\n", + "51/51 [==============================] - ETA: 0s - loss: 1.3486 - accuracy: 0.5748\n", + "Epoch 2: val_accuracy did not improve from 0.38542\n", + "51/51 [==============================] - 52s 1s/step - loss: 1.3486 - accuracy: 0.5748 - val_loss: 3.4816 - val_accuracy: 0.2578\n", + "Epoch 3/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.9585 - accuracy: 0.6458\n", + "Epoch 3: val_accuracy did not improve from 0.38542\n", + "51/51 [==============================] - 51s 1s/step - loss: 0.9585 - accuracy: 0.6458 - val_loss: 4.6736 - val_accuracy: 0.2578\n", + "Epoch 4/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.7698 - accuracy: 0.7126\n", + "Epoch 4: val_accuracy did not improve from 0.38542\n", + "51/51 [==============================] - 55s 1s/step - loss: 0.7698 - accuracy: 0.7126 - val_loss: 5.1900 - val_accuracy: 0.2500\n", + "Epoch 5/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.6196 - accuracy: 0.7770\n", + "Epoch 5: val_accuracy did not improve from 0.38542\n", + "51/51 [==============================] - 52s 1s/step - loss: 0.6196 - accuracy: 0.7770 - val_loss: 6.2598 - val_accuracy: 0.3359\n", + "Epoch 6/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.5028 - accuracy: 0.8235\n", + "Epoch 6: val_accuracy did not improve from 0.38542\n", + "51/51 [==============================] - 54s 1s/step - loss: 0.5028 - accuracy: 0.8235 - val_loss: 6.7278 - val_accuracy: 0.2708\n", + "Epoch 7/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.4281 - accuracy: 0.8425\n", + "Epoch 7: val_accuracy did not improve from 0.38542\n", + "51/51 [==============================] - 54s 1s/step - loss: 0.4281 - accuracy: 0.8425 - val_loss: 4.9290 - val_accuracy: 0.3542\n", + "Epoch 8/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.3045 - accuracy: 0.8977\n", + "Epoch 8: val_accuracy improved from 0.38542 to 0.45573, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.3045 - accuracy: 0.8977 - val_loss: 2.6881 - val_accuracy: 0.4557\n", + "Epoch 9/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2855 - accuracy: 0.8915\n", + "Epoch 9: val_accuracy improved from 0.45573 to 0.48177, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.2855 - accuracy: 0.8915 - val_loss: 2.4350 - val_accuracy: 0.4818\n", + "Epoch 10/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2387 - accuracy: 0.9148\n", + "Epoch 10: val_accuracy improved from 0.48177 to 0.59115, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.2387 - accuracy: 0.9148 - val_loss: 1.2724 - val_accuracy: 0.5911\n", + "Epoch 11/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2451 - accuracy: 0.9118\n", + "Epoch 11: val_accuracy improved from 0.59115 to 0.74479, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.2451 - accuracy: 0.9118 - val_loss: 0.7184 - val_accuracy: 0.7448\n", + "Epoch 12/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.2065 - accuracy: 0.9271\n", + "Epoch 12: val_accuracy improved from 0.74479 to 0.75521, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.2065 - accuracy: 0.9271 - val_loss: 0.6324 - val_accuracy: 0.7552\n", + "Epoch 13/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9442\n", + "Epoch 13: val_accuracy improved from 0.75521 to 0.88542, saving model to alex_2.h5\n", + "51/51 [==============================] - 55s 1s/step - loss: 0.1495 - accuracy: 0.9442 - val_loss: 0.3196 - val_accuracy: 0.8854\n", + "Epoch 14/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1121 - accuracy: 0.9620\n", + "Epoch 14: val_accuracy improved from 0.88542 to 0.93750, saving model to alex_2.h5\n", + "51/51 [==============================] - 52s 1s/step - loss: 0.1121 - accuracy: 0.9620 - val_loss: 0.1828 - val_accuracy: 0.9375\n", + "Epoch 15/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1123 - accuracy: 0.9626\n", + "Epoch 15: val_accuracy did not improve from 0.93750\n", + "51/51 [==============================] - 55s 1s/step - loss: 0.1123 - accuracy: 0.9626 - val_loss: 0.2040 - val_accuracy: 0.9271\n", + "Epoch 16/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1076 - accuracy: 0.9614\n", + "Epoch 16: val_accuracy improved from 0.93750 to 0.94271, saving model to alex_2.h5\n", + "51/51 [==============================] - 53s 1s/step - loss: 0.1076 - accuracy: 0.9614 - val_loss: 0.1781 - val_accuracy: 0.9427\n", + "Epoch 17/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.1243 - accuracy: 0.9571\n", + "Epoch 17: val_accuracy did not improve from 0.94271\n", + "51/51 [==============================] - 50s 988ms/step - loss: 0.1243 - accuracy: 0.9571 - val_loss: 0.2918 - val_accuracy: 0.8984\n", + "Epoch 18/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0914 - accuracy: 0.9706 \n", + "Epoch 18: val_accuracy did not improve from 0.94271\n", + "51/51 [==============================] - 952s 19s/step - loss: 0.0914 - accuracy: 0.9706 - val_loss: 0.2769 - val_accuracy: 0.9036\n", + "Epoch 19/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0683 - accuracy: 0.9761\n", + "Epoch 19: val_accuracy did not improve from 0.94271\n", + "51/51 [==============================] - 121s 2s/step - loss: 0.0683 - accuracy: 0.9761 - val_loss: 0.2512 - val_accuracy: 0.9036\n", + "Epoch 20/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0546 - accuracy: 0.9841\n", + "Epoch 20: val_accuracy improved from 0.94271 to 0.96354, saving model to alex_2.h5\n", + "51/51 [==============================] - 167s 3s/step - loss: 0.0546 - accuracy: 0.9841 - val_loss: 0.1222 - val_accuracy: 0.9635\n", + "Epoch 21/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0561 - accuracy: 0.9786\n", + "Epoch 21: val_accuracy did not improve from 0.96354\n", + "51/51 [==============================] - 212s 4s/step - loss: 0.0561 - accuracy: 0.9786 - val_loss: 0.1749 - val_accuracy: 0.9349\n", + "Epoch 22/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0399 - accuracy: 0.9902\n", + "Epoch 22: val_accuracy did not improve from 0.96354\n", + "51/51 [==============================] - 379s 7s/step - loss: 0.0399 - accuracy: 0.9902 - val_loss: 0.3205 - val_accuracy: 0.8958\n", + "Epoch 23/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0587 - accuracy: 0.9804\n", + "Epoch 23: val_accuracy did not improve from 0.96354\n", + "51/51 [==============================] - 332s 7s/step - loss: 0.0587 - accuracy: 0.9804 - val_loss: 0.2606 - val_accuracy: 0.9036\n", + "Epoch 24/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0629 - accuracy: 0.9804\n", + "Epoch 24: val_accuracy did not improve from 0.96354\n", + "51/51 [==============================] - 279s 6s/step - loss: 0.0629 - accuracy: 0.9804 - val_loss: 0.1527 - val_accuracy: 0.9531\n", + "Epoch 25/25\n", + "51/51 [==============================] - ETA: 0s - loss: 0.0471 - accuracy: 0.9853\n", + "Epoch 25: val_accuracy did not improve from 0.96354\n", + "51/51 [==============================] - 330s 7s/step - loss: 0.0471 - accuracy: 0.9853 - val_loss: 0.2199 - val_accuracy: 0.9297\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 [==============================] - 50s 7s/step - loss: 0.3729 - accuracy: 0.8828\n" ] } ],