uczenie-maszynowe/lab/CNN_Keras.ipynb
2023-06-01 10:31:27 +02:00

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"### AITech — Uczenie maszynowe — laboratoria\n",
"# 11. Sieci neuronowe (Keras)"
]
},
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"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"
]
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"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": {},
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"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)"
]
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"cell_type": "code",
"execution_count": 3,
"metadata": {},
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"name": "stdout",
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"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"
]
},
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"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"
]
},
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"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": null,
"metadata": {},
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"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",
" 99/422 [======>.......................] - ETA: 24s - loss: 0.9593 - accuracy: 0.7040"
]
}
],
"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])"
]
}
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