forked from pms/uczenie-maszynowe
278 lines
12 KiB
Plaintext
278 lines
12 KiB
Plaintext
{
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"cells": [
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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": "-"
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}
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},
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"source": [
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"### AITech — Uczenie maszynowe — laboratoria\n",
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"# 11. Sieci neuronowe (Keras)"
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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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"source": [
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"Keras to napisany w języku Python interfejs do platformy TensorFlow, służącej do uczenia maszynowego.\n",
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"\n",
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"Aby z niego korzystać, trzeba zainstalować bibliotekę TensorFlow:\n",
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" * `pip`: https://www.tensorflow.org/install\n",
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" * `conda`: https://docs.anaconda.com/anaconda/user-guide/tasks/tensorflow"
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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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"source": [
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"Przykład implementacji sieci neuronowej do rozpoznawania cyfr ze zbioru MNIST, według https://keras.io/examples/vision/mnist_convnet"
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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": 1,
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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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"source": [
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"# Konieczne importy\n",
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"\n",
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"import numpy as np\n",
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"from tensorflow import keras\n",
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"from tensorflow.keras import layers"
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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": 2,
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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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"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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]
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}
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],
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"source": [
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"# Przygotowanie danych\n",
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"\n",
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"num_classes = 10\n",
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"input_shape = (28, 28, 1)\n",
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"\n",
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"# podział danych na zbiory uczący i testowy\n",
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"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
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"\n",
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"# skalowanie obrazów do przedziału [0, 1]\n",
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"x_train = x_train.astype(\"float32\") / 255\n",
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"x_test = x_test.astype(\"float32\") / 255\n",
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"# upewnienie się, że obrazy mają wymiary (28, 28, 1)\n",
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"x_train = np.expand_dims(x_train, -1)\n",
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"x_test = np.expand_dims(x_test, -1)\n",
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"print(\"x_train shape:\", x_train.shape)\n",
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"print(x_train.shape[0], \"train samples\")\n",
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"print(x_test.shape[0], \"test samples\")\n",
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"\n",
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"# konwersja danych kategorycznych na binarne\n",
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"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
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"y_test = keras.utils.to_categorical(y_test, num_classes)"
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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": 3,
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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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"Model: \"sequential\"\n",
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"_________________________________________________________________\n",
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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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" flatten (Flatten) (None, 1600) 0 \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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" 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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"Non-trainable params: 0\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"# Stworzenie modelu\n",
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"\n",
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"model = keras.Sequential(\n",
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" [\n",
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" keras.Input(shape=input_shape),\n",
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" layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
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" layers.MaxPooling2D(pool_size=(2, 2)),\n",
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" layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
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" layers.MaxPooling2D(pool_size=(2, 2)),\n",
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" layers.Flatten(),\n",
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" layers.Dropout(0.5),\n",
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" layers.Dense(num_classes, activation=\"softmax\"),\n",
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" ]\n",
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")\n",
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"\n",
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"model.summary()"
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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": 5,
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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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"422/422 [==============================] - 36s 82ms/step - loss: 0.3806 - accuracy: 0.8831 - val_loss: 0.0894 - val_accuracy: 0.9738\n",
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"Epoch 2/15\n",
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"422/422 [==============================] - 34s 80ms/step - loss: 0.1174 - accuracy: 0.9644 - val_loss: 0.0611 - val_accuracy: 0.9827\n",
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"Epoch 3/15\n",
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"422/422 [==============================] - 63s 149ms/step - loss: 0.0858 - accuracy: 0.9739 - val_loss: 0.0482 - val_accuracy: 0.9870\n",
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"Epoch 4/15\n",
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"422/422 [==============================] - 29s 68ms/step - loss: 0.0748 - accuracy: 0.9762 - val_loss: 0.0431 - val_accuracy: 0.9885\n",
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"Epoch 5/15\n",
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"422/422 [==============================] - 35s 84ms/step - loss: 0.0644 - accuracy: 0.9804 - val_loss: 0.0391 - val_accuracy: 0.9898\n",
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"Epoch 6/15\n",
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"422/422 [==============================] - 32s 75ms/step - loss: 0.0562 - accuracy: 0.9826 - val_loss: 0.0367 - val_accuracy: 0.9908\n",
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"Epoch 7/15\n",
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"422/422 [==============================] - 29s 68ms/step - loss: 0.0521 - accuracy: 0.9841 - val_loss: 0.0356 - val_accuracy: 0.9897\n",
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"Epoch 8/15\n",
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"422/422 [==============================] - 28s 67ms/step - loss: 0.0484 - accuracy: 0.9840 - val_loss: 0.0334 - val_accuracy: 0.9922\n",
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"Epoch 9/15\n",
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"422/422 [==============================] - 29s 69ms/step - loss: 0.0466 - accuracy: 0.9855 - val_loss: 0.0355 - val_accuracy: 0.9908\n",
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"Epoch 10/15\n",
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"422/422 [==============================] - 29s 68ms/step - loss: 0.0423 - accuracy: 0.9864 - val_loss: 0.0332 - val_accuracy: 0.9902\n",
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"Epoch 11/15\n",
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"422/422 [==============================] - 30s 71ms/step - loss: 0.0413 - accuracy: 0.9868 - val_loss: 0.0315 - val_accuracy: 0.9915\n",
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"Epoch 12/15\n",
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"422/422 [==============================] - 29s 68ms/step - loss: 0.0380 - accuracy: 0.9876 - val_loss: 0.0294 - val_accuracy: 0.9913\n",
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"Epoch 13/15\n",
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"422/422 [==============================] - 30s 70ms/step - loss: 0.0371 - accuracy: 0.9883 - val_loss: 0.0287 - val_accuracy: 0.9917\n",
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"Epoch 14/15\n",
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"422/422 [==============================] - 29s 70ms/step - loss: 0.0342 - accuracy: 0.9886 - val_loss: 0.0380 - val_accuracy: 0.9893\n",
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"Epoch 15/15\n",
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"422/422 [==============================] - 29s 68ms/step - loss: 0.0351 - accuracy: 0.9888 - val_loss: 0.0320 - val_accuracy: 0.9912\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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"<keras.callbacks.History at 0x7f50553cc760>"
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]
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},
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"execution_count": 5,
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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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"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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]
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Ewaluacja modelu\n",
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"\n",
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"score = model.evaluate(x_test, y_test, verbose=0)\n",
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"print(\"Test loss:\", score[0])\n",
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"print(\"Test accuracy:\", score[1])"
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]
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}
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],
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"celltoolbar": "Slideshow",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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"theme": "amu"
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"nbformat": 4,
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"nbformat_minor": 4
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}
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