{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "### AITech — Uczenie maszynowe — laboratoria\n", "# 11. Sieci neuronowe (Keras)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keras to napisany w języku Python interfejs do platformy TensorFlow, służącej do uczenia maszynowego.\n", "\n", "Aby z niego korzystać, trzeba zainstalować bibliotekę TensorFlow:\n", " * `pip`: https://www.tensorflow.org/install\n", " * `conda`: https://docs.anaconda.com/anaconda/user-guide/tasks/tensorflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Przykład implementacji sieci neuronowej do rozpoznawania cyfr ze zbioru MNIST, według https://keras.io/examples/vision/mnist_convnet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "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", "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", "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", "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", "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" ] } ], "source": [ "# Konieczne importy\n", "\n", "import numpy as np\n", "from tensorflow import keras\n", "from tensorflow.keras import layers" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_train shape: (60000, 28, 28, 1)\n", "60000 train samples\n", "10000 test samples\n" ] } ], "source": [ "# Przygotowanie danych\n", "\n", "num_classes = 10\n", "input_shape = (28, 28, 1)\n", "\n", "# podział danych na zbiory uczący i testowy\n", "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n", "\n", "# skalowanie obrazów do przedziału [0, 1]\n", "x_train = x_train.astype(\"float32\") / 255\n", "x_test = x_test.astype(\"float32\") / 255\n", "# upewnienie się, że obrazy mają wymiary (28, 28, 1)\n", "x_train = np.expand_dims(x_train, -1)\n", "x_test = np.expand_dims(x_test, -1)\n", "print(\"x_train shape:\", x_train.shape)\n", "print(x_train.shape[0], \"train samples\")\n", "print(x_test.shape[0], \"test samples\")\n", "\n", "# konwersja danych kategorycznych na binarne\n", "y_train = keras.utils.to_categorical(y_train, num_classes)\n", "y_test = keras.utils.to_categorical(y_test, num_classes)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " conv2d (Conv2D) (None, 26, 26, 32) 320 \n", " \n", " max_pooling2d (MaxPooling2D (None, 13, 13, 32) 0 \n", " ) \n", " \n", " conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "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", "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", "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", "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", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n", " max_pooling2d_1 (MaxPooling (None, 5, 5, 64) 0 \n", " 2D) \n", " \n", " flatten (Flatten) (None, 1600) 0 \n", " \n", " dropout (Dropout) (None, 1600) 0 \n", " \n", " dense (Dense) (None, 10) 16010 \n", " \n", "=================================================================\n", "Total params: 34,826\n", "Trainable params: 34,826\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# Stworzenie modelu\n", "\n", "model = keras.Sequential(\n", " [\n", " keras.Input(shape=input_shape),\n", " layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n", " layers.MaxPooling2D(pool_size=(2, 2)),\n", " layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n", " layers.MaxPooling2D(pool_size=(2, 2)),\n", " layers.Flatten(),\n", " layers.Dropout(0.5),\n", " layers.Dense(num_classes, activation=\"softmax\"),\n", " ]\n", ")\n", "\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/15\n", "422/422 [==============================] - 36s 82ms/step - loss: 0.3806 - accuracy: 0.8831 - val_loss: 0.0894 - val_accuracy: 0.9738\n", "Epoch 2/15\n", "422/422 [==============================] - 34s 80ms/step - loss: 0.1174 - accuracy: 0.9644 - val_loss: 0.0611 - val_accuracy: 0.9827\n", "Epoch 3/15\n", "422/422 [==============================] - 63s 149ms/step - loss: 0.0858 - accuracy: 0.9739 - val_loss: 0.0482 - val_accuracy: 0.9870\n", "Epoch 4/15\n", "422/422 [==============================] - 29s 68ms/step - loss: 0.0748 - accuracy: 0.9762 - val_loss: 0.0431 - val_accuracy: 0.9885\n", "Epoch 5/15\n", "422/422 [==============================] - 35s 84ms/step - loss: 0.0644 - accuracy: 0.9804 - val_loss: 0.0391 - val_accuracy: 0.9898\n", "Epoch 6/15\n", "422/422 [==============================] - 32s 75ms/step - loss: 0.0562 - accuracy: 0.9826 - val_loss: 0.0367 - val_accuracy: 0.9908\n", "Epoch 7/15\n", "422/422 [==============================] - 29s 68ms/step - loss: 0.0521 - accuracy: 0.9841 - val_loss: 0.0356 - val_accuracy: 0.9897\n", "Epoch 8/15\n", "422/422 [==============================] - 28s 67ms/step - loss: 0.0484 - accuracy: 0.9840 - val_loss: 0.0334 - val_accuracy: 0.9922\n", "Epoch 9/15\n", "422/422 [==============================] - 29s 69ms/step - loss: 0.0466 - accuracy: 0.9855 - val_loss: 0.0355 - val_accuracy: 0.9908\n", "Epoch 10/15\n", "422/422 [==============================] - 29s 68ms/step - loss: 0.0423 - accuracy: 0.9864 - val_loss: 0.0332 - val_accuracy: 0.9902\n", "Epoch 11/15\n", "422/422 [==============================] - 30s 71ms/step - loss: 0.0413 - accuracy: 0.9868 - val_loss: 0.0315 - val_accuracy: 0.9915\n", "Epoch 12/15\n", "422/422 [==============================] - 29s 68ms/step - loss: 0.0380 - accuracy: 0.9876 - val_loss: 0.0294 - val_accuracy: 0.9913\n", "Epoch 13/15\n", "422/422 [==============================] - 30s 70ms/step - loss: 0.0371 - accuracy: 0.9883 - val_loss: 0.0287 - val_accuracy: 0.9917\n", "Epoch 14/15\n", "422/422 [==============================] - 29s 70ms/step - loss: 0.0342 - accuracy: 0.9886 - val_loss: 0.0380 - val_accuracy: 0.9893\n", "Epoch 15/15\n", "422/422 [==============================] - 29s 68ms/step - loss: 0.0351 - accuracy: 0.9888 - val_loss: 0.0320 - val_accuracy: 0.9912\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Uczenie modelu\n", "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n", "model.fit(x_train, y_train, batch_size=128, epochs=15, validation_split=0.1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Ewaluacja modelu\n", "\n", "score = model.evaluate(x_test, y_test, verbose=0)\n", "print(\"Test loss:\", score[0])\n", "print(\"Test accuracy:\", score[1])" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "livereveal": { "start_slideshow_at": "selected", "theme": "amu" } }, "nbformat": 4, "nbformat_minor": 4 }