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"metadata": {
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"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.15.1)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.0)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.4.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.27.1)\n",
"Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.65.0)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (1.26.15)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.12)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n"
]
}
],
"source": [
"import tensorflow.keras\n",
"from tensorflow import keras\n",
"from tensorflow.keras.layers import Conv2D, Input, MaxPool2D, RandomRotation, RandomTranslation, RandomZoom, Dropout, Flatten, Dense\n",
"import numpy as np\n",
"from tensorflow.keras.datasets import fashion_mnist\n",
"! pip install huggingface_hub\n",
"from huggingface_hub import from_pretrained_keras\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"source": [
"(train_x, train_y), (test_x, test_y) = fashion_mnist.load_data()\n",
"\n",
"train_y = tensorflow.keras.utils.to_categorical(train_y, 10)\n",
"test_y = tensorflow.keras.utils.to_categorical(test_y, 10)\n",
"\n"
],
"metadata": {
"id": "bgWx-DtnKwla"
},
"execution_count": 180,
"outputs": []
},
{
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"source": [
"decoder = from_pretrained_keras(\"vladimirchabanov/mnist_decoder\")\n",
"\n",
"for layer in decoder.layers:\n",
" layer.trainable = False\n"
],
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"text/plain": [
"Downloading fingerprint.pb: 0%| | 0.00/57.0 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c4e1024f1d704d7cb64247dc00dc9fef"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading saved_model.pb: 0%| | 0.00/65.8k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "5f1a1d8e8c3140c8ae5ca329b2011a3b"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading keras_metadata.pb: 0%| | 0.00/7.55k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "42276e62a7fc4fb3861cf1f8638209d5"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"decoder.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y3-5v2uEXgrX",
"outputId": "6f29855b-6008-4fd0-8b5e-11c8f918feb6"
},
"execution_count": 182,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"model_20\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" input_19 (InputLayer) [(None, 49)] 0 \n",
" \n",
" dense_47 (Dense) (None, 64) 3200 \n",
" \n",
" dense_48 (Dense) (None, 784) 50960 \n",
" \n",
" reshape_7 (Reshape) (None, 28, 28, 1) 0 \n",
" \n",
"=================================================================\n",
"Total params: 54,160\n",
"Trainable params: 0\n",
"Non-trainable params: 54,160\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from keras.utils.vis_utils import plot_model"
],
"metadata": {
"id": "141Ggsgb51Ja"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"plot_model(decoder, to_file='model_plot.png', show_shapes=True, show_layer_names=True, show_layer_activations=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 422
},
"id": "xt0YwmYn5p7D",
"outputId": "e31dd84e-5dcf-4828-c46a-79b7ca9afee7"
},
"execution_count": 185,
"outputs": [
{
"output_type": "execute_result",
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 185
}
]
},
{
"cell_type": "code",
"source": [
"#Создадим encoder\n",
"x = Input(shape=(10,))\n",
"encoder = Dense(49, activation='relu')(x)\n",
"encoder_model = Model(x, encoder)\n",
"\n"
],
"metadata": {
"id": "lOs0BA7fbbnl"
},
"execution_count": 187,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plot_model(encoder_model, to_file='model_plot.png', show_shapes=True, show_layer_names=True, show_layer_activations=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 201
},
"id": "BdmWuFSZ6y9T",
"outputId": "569dd388-c91a-4330-abd0-3db28e268b9d"
},
"execution_count": 190,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 190
}
]
},
{
"cell_type": "code",
"source": [
"#Создаем autoencoder\n",
"autoencoder = Model(x, decoder(encoder_model(x)))"
],
"metadata": {
"id": "cvzDksj0nuCy"
},
"execution_count": 192,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plot_model(encoder_model, to_file='model_plot.png', show_shapes=True, show_layer_names=True, show_layer_activations=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 201
},
"id": "IexLzMRw7PNv",
"outputId": "82c99098-213a-46af-9d27-0e433f56eb4e"
},
"execution_count": 193,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 193
}
]
},
{
"cell_type": "code",
"source": [
"autoencoder.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vKctkiS67PG4",
"outputId": "7f1b218c-4c26-4ab2-cc7e-1e5ade604054"
},
"execution_count": 194,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"model_52\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" input_41 (InputLayer) [(None, 10)] 0 \n",
" \n",
" model_51 (Functional) (None, 49) 539 \n",
" \n",
" model_20 (Functional) (None, 28, 28, 1) 54160 \n",
" \n",
"=================================================================\n",
"Total params: 54,699\n",
"Trainable params: 539\n",
"Non-trainable params: 54,160\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"autoencoder.compile(optimizer='adam', loss='mean_squared_error')"
],
"metadata": {
"id": "rVAaCGjhXHnc"
},
"execution_count": 197,
"outputs": []
},
{
"cell_type": "code",
"source": [
"autoencoder.fit(train_y, train_x, epochs=10, batch_size=1000, validation_data=(test_y, test_x))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0ihAEoPXetBj",
"outputId": "3108b058-1f94-496c-c7e7-a0176a959d18"
},
"execution_count": 213,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n",
"60/60 [==============================] - 1s 11ms/step - loss: 13327.3203 - val_loss: 13330.5859\n",
"Epoch 2/10\n",
"60/60 [==============================] - 1s 11ms/step - loss: 13327.1436 - val_loss: 13330.4043\n",
"Epoch 3/10\n",
"60/60 [==============================] - 1s 11ms/step - loss: 13326.9678 - val_loss: 13330.2305\n",
"Epoch 4/10\n",
"60/60 [==============================] - 1s 11ms/step - loss: 13326.7979 - val_loss: 13330.0664\n",
"Epoch 5/10\n",
"60/60 [==============================] - 1s 15ms/step - loss: 13326.6357 - val_loss: 13329.9053\n",
"Epoch 6/10\n",
"60/60 [==============================] - 1s 18ms/step - loss: 13326.4766 - val_loss: 13329.7549\n",
"Epoch 7/10\n",
"60/60 [==============================] - 1s 16ms/step - loss: 13326.3271 - val_loss: 13329.5996\n",
"Epoch 8/10\n",
"60/60 [==============================] - 1s 11ms/step - loss: 13326.1777 - val_loss: 13329.4531\n",
"Epoch 9/10\n",
"60/60 [==============================] - 1s 11ms/step - loss: 13326.0312 - val_loss: 13329.3096\n",
"Epoch 10/10\n",
"60/60 [==============================] - 1s 11ms/step - loss: 13325.8926 - val_loss: 13329.1729\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f8c4dbde170>"
]
},
"metadata": {},
"execution_count": 213
}
]
},
{
"cell_type": "code",
"source": [
"i = 120\n",
"plt.imshow(x_test[i], cmap='gray')"
],
"metadata": {
"id": "Z7mfAhfsesc9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 447
},
"outputId": "f7cf3332-20b8-432b-c5a9-63d6537b7c8e"
},
"execution_count": 216,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f8c4dad5f90>"
]
},
"metadata": {},
"execution_count": 216
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"plt.imshow(autoencoder.predict(test_y[i-1:i]).squeeze(), cmap='gray')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "OT0jdmhV8TiA",
"outputId": "4a7c2ff3-6ee0-450c-ee4f-81f36bf60fc6"
},
"execution_count": 217,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 0s 22ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f8c4dc4ca60>"
]
},
"metadata": {},
"execution_count": 217
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"autoencoder.save(\"model.pb\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qI0avhTU9Xkr",
"outputId": "abe943d2-9f68-4cb0-b701-6bb46e446a86"
},
"execution_count": 218,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
]
}
]
}
]
} |