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No commits in common. "6bbf32ab04a874122ca94866dff66f3362c44862" and "f7b363d46ef3be32e5018eca5a3bf02a86b5dd12" have entirely different histories.
6bbf32ab04
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f7b363d46e
@ -1,6 +1,7 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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@ -13,6 +14,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -24,6 +26,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -32,23 +35,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-06-01 10:29:41.492705: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"2023-06-01 10:29:42.477407: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
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"2023-06-01 10:29:42.477524: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
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"2023-06-01 10:29:45.603958: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
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"2023-06-01 10:29:45.604816: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
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"2023-06-01 10:29:45.604834: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Konieczne importy\n",
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"\n",
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@ -59,13 +48,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
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"11493376/11490434 [==============================] - 1s 0us/step\n",
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"x_train shape: (60000, 28, 28, 1)\n",
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"60000 train samples\n",
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"10000 test samples\n"
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@ -98,7 +89,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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@ -110,38 +101,18 @@
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"conv2d (Conv2D) (None, 26, 26, 32) 320 \n",
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" \n",
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" max_pooling2d (MaxPooling2D (None, 13, 13, 32) 0 \n",
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" ) \n",
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" \n",
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" conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-06-01 10:29:49.494604: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
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"2023-06-01 10:29:49.495467: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
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"2023-06-01 10:29:49.496113: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ELLIOT): /proc/driver/nvidia/version does not exist\n",
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"2023-06-01 10:29:49.497742: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" \n",
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" max_pooling2d_1 (MaxPooling (None, 5, 5, 64) 0 \n",
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" 2D) \n",
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" \n",
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"_________________________________________________________________\n",
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"max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 \n",
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"_________________________________________________________________\n",
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"flatten (Flatten) (None, 1600) 0 \n",
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" \n",
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"_________________________________________________________________\n",
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"dropout (Dropout) (None, 1600) 0 \n",
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" \n",
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"_________________________________________________________________\n",
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"dense (Dense) (None, 10) 16010 \n",
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" \n",
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"=================================================================\n",
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"Total params: 34,826\n",
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"Trainable params: 34,826\n",
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@ -171,36 +142,52 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-06-01 10:30:24.247916: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 169344000 exceeds 10% of free system memory.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/15\n",
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" 99/422 [======>.......................] - ETA: 24s - loss: 0.9593 - accuracy: 0.7040"
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"422/422 [==============================] - 38s 91ms/step - loss: 0.0556 - accuracy: 0.9826 - val_loss: 0.0412 - val_accuracy: 0.9893\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<tensorflow.python.keras.callbacks.History at 0x1a50b35a070>"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Uczenie modelu\n",
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"\n",
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"batch_size = 128\n",
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"epochs = 15\n",
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"\n",
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"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
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"model.fit(x_train, y_train, batch_size=128, epochs=15, validation_split=0.1)"
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"\n",
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"model.fit(x_train, y_train, epochs=1, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test loss: 0.03675819933414459\n",
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"Test accuracy: 0.988099992275238\n"
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]
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}
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],
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"source": [
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"# Ewaluacja modelu\n",
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"\n",
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@ -38,7 +38,7 @@
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"### _Batch gradient descent_\n",
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"\n",
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"* Klasyczna wersja metody gradientu prostego\n",
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"* Obliczamy gradient funkcji kosztu względem całego zbioru uczącego:\n",
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"* Obliczamy gradient funkcji kosztu względem całego zbioru treningowego:\n",
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" $$ \\theta := \\theta - \\alpha \\cdot \\nabla_\\theta J(\\theta) $$\n",
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"* Dlatego może działać bardzo powoli\n",
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"* Nie można dodawać nowych przykładów na bieżąco w trakcie trenowania modelu (*online learning*)"
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@ -288,7 +288,8 @@
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"### Adagrad\n",
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"\n",
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"* “<b>Ada</b>ptive <b>grad</b>ient”\n",
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"* Adagrad dostosowuje współczynnik uczenia (*learning rate*) do parametrów: zmniejsza go dla cech występujących częściej, a zwiększa dla występujących rzadziej\n",
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"* Adagrad dostosowuje współczynnik uczenia (*learning rate*) do parametrów: zmniejsza go dla cech występujących częściej, a zwiększa dla występujących rzadziej:\n",
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"* Świetny do trenowania na rzadkich (*sparse*) zbiorach danych\n",
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"* Wada: współczynnik uczenia może czasami gwałtownie maleć\n",
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"* Wyniki badań pokazują, że często **starannie** dobrane $\\alpha$ daje lepsze wyniki na zbiorze testowym"
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]
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@ -15,18 +15,7 @@
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Splotowe sieci neuronowe, inaczej konwolucyjne sieci neuronowe (*convolutional neural networks*, CNN, ConvNet)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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"slide_type": "slide"
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}
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},
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"source": [
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