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"cells": [
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"execution_count": 4,
"metadata": {
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},
"id": "BB-jirPso-bz",
"outputId": "49f9909a-a3f2-4ca3-a230-1a70e10fd581"
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"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting huggingface_hub\n",
" Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement 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",
"Installing collected packages: huggingface_hub\n",
"Successfully installed huggingface_hub-0.15.1\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 mnist\n",
"! pip install huggingface_hub\n",
"from huggingface_hub import from_pretrained_keras\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"source": [
"# Загружаем данные\n",
"(x, y), (xt, yt) = mnist.load_data()\n",
"\n",
"# Преобразуем метки из цифр в one hot ветора 6 -> [0,0,0,0,0,0,1,0,0,0]\n",
"y = tensorflow.keras.utils.to_categorical(y, 10)\n",
"yt = tensorflow.keras.utils.to_categorical(yt, 10)"
],
"metadata": {
"id": "9kDKYVuZpAUL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cf98bd13-2ba1-498e-db53-b70a5be7c6af"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
"11490434/11490434 [==============================] - 0s 0us/step\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Основаня модель для классификации цифр\n",
"main_model = tensorflow.keras.Sequential([\n",
" Input(shape=(10,)),\n",
" Dense(128, activation='relu'),\n",
" Dense(64, activation='relu'),\n",
" Dense(49, activation='relu') # <- здесь функция активации softmax, чтобы выходы были как вероятности от 0 до 1\n",
"])"
],
"metadata": {
"id": "K75VVilqpBRi"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"main_model.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_rCyvHz4pL2k",
"outputId": "63e6add6-bfc5-450c-fe0a-5118602cf828"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense (Dense) (None, 128) 1408 \n",
" \n",
" dense_1 (Dense) (None, 64) 8256 \n",
" \n",
" dense_2 (Dense) (None, 49) 3185 \n",
" \n",
"=================================================================\n",
"Total params: 12,849\n",
"Trainable params: 12,849\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Визуализация encoder-а\n",
"plot_model(main_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": 422
},
"id": "UtRJZaZh9S73",
"outputId": "8c8ca2f1-31e3-4ac4-fb3b-48dfc9f34dc4"
},
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"# Посмотрим первые 9 изображений из нашего train датасета\n",
"# чтобы понимать что нам нужно\n",
"for i in range(1, 10):\n",
" plt.subplot(330 + i)\n",
" plt.imshow(x[i], cmap=plt.get_cmap('gray'))\n",
"\n",
"# Show the figure\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 433
},
"id": "gLbrDT78AIyx",
"outputId": "cda7f9ab-3c78-4f13-8dab-475f61f1fd59"
},
"execution_count": 32,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 9 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Хвост новой нейронки\n",
"my_new_nodel_tail = from_pretrained_keras(\"vladimirchabanov/mnist_decoder\")\n",
"\n",
"# Проходим по всем слоям Основной сети и отключаем возможность обучаться для каждого слоя\n",
"# чтобы не портить уже обученные слои в процессе обучения хвоста\n",
"for layer in my_new_nodel_tail.layers:\n",
" layer.trainable = False\n",
"\n",
"# Собираем новую нейронку из обученной головы Основной сети и не обученного хвоста\n",
"my_new_model = tensorflow.keras.Model(\n",
" inputs = main_model.layers[0].input,\n",
" outputs = my_new_nodel_tail(main_model.layers[-1].output)\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 325,
"referenced_widgets": [
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},
"id": "fY__mWf8pCMK",
"outputId": "b69139e5-af5c-4050-dec5-6abb66e2f689"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"config.json not found in HuggingFace Hub.\n",
"WARNING:huggingface_hub.hub_mixin:config.json not found in HuggingFace Hub.\n"
]
},
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}
},
"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": [
"my_new_nodel_tail.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cPFgAwf1pDlz",
"outputId": "1532ab1e-8b1f-4fc4-cccf-fb7477122f0b"
},
"execution_count": 9,
"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": [
"my_new_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
],
"metadata": {
"id": "Lxi3lTUEpEiz"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"my_new_model.fit(y, x, batch_size=500, epochs=50, validation_split=0.2, validation_data=(yt, xt))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4m_1F3SapFes",
"outputId": "e078ade4-97dc-4493-f45a-7b5161f7b4a6"
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2517.2793 - accuracy: 0.7220 - val_loss: 2540.1492 - val_accuracy: 0.7225\n",
"Epoch 2/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2517.2495 - accuracy: 0.7218 - val_loss: 2539.4521 - val_accuracy: 0.7230\n",
"Epoch 3/50\n",
"120/120 [==============================] - 4s 29ms/step - loss: 2517.3235 - accuracy: 0.7219 - val_loss: 2539.4229 - val_accuracy: 0.7243\n",
"Epoch 4/50\n",
"120/120 [==============================] - 5s 40ms/step - loss: 2517.2219 - accuracy: 0.7219 - val_loss: 2539.7112 - val_accuracy: 0.7228\n",
"Epoch 5/50\n",
"120/120 [==============================] - 3s 23ms/step - loss: 2517.1079 - accuracy: 0.7223 - val_loss: 2539.3132 - val_accuracy: 0.7214\n",
"Epoch 6/50\n",
"120/120 [==============================] - 3s 22ms/step - loss: 2516.9851 - accuracy: 0.7223 - val_loss: 2539.7681 - val_accuracy: 0.7238\n",
"Epoch 7/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2516.9062 - accuracy: 0.7224 - val_loss: 2538.9009 - val_accuracy: 0.7243\n",
"Epoch 8/50\n",
"120/120 [==============================] - 4s 30ms/step - loss: 2516.8491 - accuracy: 0.7225 - val_loss: 2538.9583 - val_accuracy: 0.7235\n",
"Epoch 9/50\n",
"120/120 [==============================] - 6s 52ms/step - loss: 2516.4375 - accuracy: 0.7228 - val_loss: 2539.3418 - val_accuracy: 0.7220\n",
"Epoch 10/50\n",
"120/120 [==============================] - 4s 31ms/step - loss: 2516.0671 - accuracy: 0.7231 - val_loss: 2538.0801 - val_accuracy: 0.7234\n",
"Epoch 11/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2515.7295 - accuracy: 0.7233 - val_loss: 2538.5339 - val_accuracy: 0.7229\n",
"Epoch 12/50\n",
"120/120 [==============================] - 3s 29ms/step - loss: 2515.6223 - accuracy: 0.7235 - val_loss: 2537.9490 - val_accuracy: 0.7244\n",
"Epoch 13/50\n",
"120/120 [==============================] - 5s 39ms/step - loss: 2515.0876 - accuracy: 0.7234 - val_loss: 2537.0908 - val_accuracy: 0.7246\n",
"Epoch 14/50\n",
"120/120 [==============================] - 3s 27ms/step - loss: 2514.2043 - accuracy: 0.7239 - val_loss: 2536.2979 - val_accuracy: 0.7237\n",
"Epoch 15/50\n",
"120/120 [==============================] - 4s 30ms/step - loss: 2513.8752 - accuracy: 0.7239 - val_loss: 2535.7981 - val_accuracy: 0.7246\n",
"Epoch 16/50\n",
"120/120 [==============================] - 4s 31ms/step - loss: 2513.5620 - accuracy: 0.7241 - val_loss: 2535.5916 - val_accuracy: 0.7259\n",
"Epoch 17/50\n",
"120/120 [==============================] - 7s 56ms/step - loss: 2513.1870 - accuracy: 0.7241 - val_loss: 2535.2139 - val_accuracy: 0.7234\n",
"Epoch 18/50\n",
"120/120 [==============================] - 6s 54ms/step - loss: 2513.3220 - accuracy: 0.7242 - val_loss: 2535.3301 - val_accuracy: 0.7251\n",
"Epoch 19/50\n",
"120/120 [==============================] - 4s 37ms/step - loss: 2512.9832 - accuracy: 0.7244 - val_loss: 2534.8098 - val_accuracy: 0.7248\n",
"Epoch 20/50\n",
"120/120 [==============================] - 6s 47ms/step - loss: 2513.0337 - accuracy: 0.7242 - val_loss: 2534.9275 - val_accuracy: 0.7254\n",
"Epoch 21/50\n",
"120/120 [==============================] - 4s 29ms/step - loss: 2512.9780 - accuracy: 0.7243 - val_loss: 2535.0823 - val_accuracy: 0.7244\n",
"Epoch 22/50\n",
"120/120 [==============================] - 3s 27ms/step - loss: 2512.9197 - accuracy: 0.7243 - val_loss: 2535.7205 - val_accuracy: 0.7259\n",
"Epoch 23/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2512.9058 - accuracy: 0.7242 - val_loss: 2534.9150 - val_accuracy: 0.7263\n",
"Epoch 24/50\n",
"120/120 [==============================] - 6s 51ms/step - loss: 2512.7317 - accuracy: 0.7243 - val_loss: 2534.7439 - val_accuracy: 0.7251\n",
"Epoch 25/50\n",
"120/120 [==============================] - 7s 57ms/step - loss: 2512.6001 - accuracy: 0.7245 - val_loss: 2534.6074 - val_accuracy: 0.7256\n",
"Epoch 26/50\n",
"120/120 [==============================] - 6s 51ms/step - loss: 2512.3757 - accuracy: 0.7246 - val_loss: 2534.1340 - val_accuracy: 0.7263\n",
"Epoch 27/50\n",
"120/120 [==============================] - 6s 46ms/step - loss: 2512.3596 - accuracy: 0.7248 - val_loss: 2534.4324 - val_accuracy: 0.7257\n",
"Epoch 28/50\n",
"120/120 [==============================] - 3s 27ms/step - loss: 2512.1997 - accuracy: 0.7250 - val_loss: 2533.9912 - val_accuracy: 0.7269\n",
"Epoch 29/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2512.2307 - accuracy: 0.7250 - val_loss: 2534.5369 - val_accuracy: 0.7255\n",
"Epoch 30/50\n",
"120/120 [==============================] - 4s 32ms/step - loss: 2512.0952 - accuracy: 0.7251 - val_loss: 2534.7773 - val_accuracy: 0.7250\n",
"Epoch 31/50\n",
"120/120 [==============================] - 7s 55ms/step - loss: 2512.1694 - accuracy: 0.7250 - val_loss: 2534.6777 - val_accuracy: 0.7254\n",
"Epoch 32/50\n",
"120/120 [==============================] - 3s 26ms/step - loss: 2512.2139 - accuracy: 0.7251 - val_loss: 2534.5293 - val_accuracy: 0.7276\n",
"Epoch 33/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2512.1941 - accuracy: 0.7250 - val_loss: 2534.3464 - val_accuracy: 0.7258\n",
"Epoch 34/50\n",
"120/120 [==============================] - 3s 25ms/step - loss: 2512.0667 - accuracy: 0.7250 - val_loss: 2534.1162 - val_accuracy: 0.7266\n",
"Epoch 35/50\n",
"120/120 [==============================] - 4s 35ms/step - loss: 2512.0762 - accuracy: 0.7251 - val_loss: 2534.6384 - val_accuracy: 0.7258\n",
"Epoch 36/50\n",
"120/120 [==============================] - 4s 34ms/step - loss: 2511.9568 - accuracy: 0.7254 - val_loss: 2534.5615 - val_accuracy: 0.7262\n",
"Epoch 37/50\n",
"120/120 [==============================] - 3s 26ms/step - loss: 2512.0317 - accuracy: 0.7253 - val_loss: 2534.4421 - val_accuracy: 0.7278\n",
"Epoch 38/50\n",
"120/120 [==============================] - 3s 27ms/step - loss: 2511.8762 - accuracy: 0.7254 - val_loss: 2534.2661 - val_accuracy: 0.7280\n",
"Epoch 39/50\n",
"120/120 [==============================] - 6s 53ms/step - loss: 2511.9585 - accuracy: 0.7252 - val_loss: 2534.5027 - val_accuracy: 0.7244\n",
"Epoch 40/50\n",
"120/120 [==============================] - 4s 35ms/step - loss: 2512.0022 - accuracy: 0.7251 - val_loss: 2534.1260 - val_accuracy: 0.7255\n",
"Epoch 41/50\n",
"120/120 [==============================] - 3s 26ms/step - loss: 2511.8323 - accuracy: 0.7252 - val_loss: 2534.0422 - val_accuracy: 0.7269\n",
"Epoch 42/50\n",
"120/120 [==============================] - 3s 26ms/step - loss: 2511.9565 - accuracy: 0.7252 - val_loss: 2533.8274 - val_accuracy: 0.7254\n",
"Epoch 43/50\n",
"120/120 [==============================] - 3s 24ms/step - loss: 2511.9214 - accuracy: 0.7252 - val_loss: 2535.0791 - val_accuracy: 0.7252\n",
"Epoch 44/50\n",
"120/120 [==============================] - 6s 51ms/step - loss: 2511.8621 - accuracy: 0.7252 - val_loss: 2534.1047 - val_accuracy: 0.7248\n",
"Epoch 45/50\n",
"120/120 [==============================] - 7s 59ms/step - loss: 2511.8953 - accuracy: 0.7249 - val_loss: 2534.0784 - val_accuracy: 0.7260\n",
"Epoch 46/50\n",
"120/120 [==============================] - 6s 47ms/step - loss: 2511.9561 - accuracy: 0.7251 - val_loss: 2534.5005 - val_accuracy: 0.7257\n",
"Epoch 47/50\n",
"120/120 [==============================] - 5s 39ms/step - loss: 2511.8926 - accuracy: 0.7251 - val_loss: 2534.0894 - val_accuracy: 0.7247\n",
"Epoch 48/50\n",
"120/120 [==============================] - 3s 26ms/step - loss: 2511.9226 - accuracy: 0.7251 - val_loss: 2534.2139 - val_accuracy: 0.7252\n",
"Epoch 49/50\n",
"120/120 [==============================] - 4s 31ms/step - loss: 2511.9207 - accuracy: 0.7252 - val_loss: 2534.4153 - val_accuracy: 0.7256\n",
"Epoch 50/50\n",
"120/120 [==============================] - 5s 39ms/step - loss: 2511.9026 - accuracy: 0.7252 - val_loss: 2534.3445 - val_accuracy: 0.7262\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f0d7949ca60>"
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"loss, accuracy = my_new_model.evaluate(y, x)\n",
"print(\"Train Loss:\", loss)\n",
"print(\"Train Accuracy:\", accuracy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b8z6lHBi9C73",
"outputId": "5628ca65-e892-4c35-de9f-dcac7e1c74c6"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1875/1875 [==============================] - 7s 3ms/step - loss: 2511.7312 - accuracy: 0.7256\n",
"Train Loss: 2511.731201171875\n",
"Train Accuracy: 0.7256483435630798\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"loss, accuracy = my_new_model.evaluate(yt, xt)\n",
"print(\"Test Loss:\", loss)\n",
"print(\"Test Accuracy:\", accuracy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4T0V8G9C9wak",
"outputId": "52f264cb-0d2b-4fbc-ed6b-4e68ab0ba441"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"313/313 [==============================] - 1s 3ms/step - loss: 2534.3447 - accuracy: 0.7262\n",
"Test Loss: 2534.3447265625\n",
"Test Accuracy: 0.7262243628501892\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(\"Validation Loss:\", loss)\n",
"print(\"Validation Accuracy:\", accuracy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8i88WodE9x_q",
"outputId": "1ff6da26-c337-4dcc-9d64-5525eac0c807"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Validation Loss: 2534.3447265625\n",
"Validation Accuracy: 0.7262243628501892\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"digit_number = 9\n",
"\n",
"plt.imshow(xt[digit_number])\n",
"\n",
"print('Predicted label:', np.argmax(my_new_model.predict(yt[digit_number:digit_number+1])))\n",
"print('Real label:', np.argmax(xt[digit_number]))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 482
},
"id": "PFtUMvBEvN_O",
"outputId": "1d7c323b-5124-4e98-e7e9-a08c1bb3e999"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 0s 23ms/step\n",
"Predicted label: 463\n",
"Real label: 357\n"
]
},
{
"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(my_new_model.predict(yt[9:10]).squeeze(), cmap='magma')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465
},
"id": "E11eh-GFsVHi",
"outputId": "e3a575ca-40a7-4636-d927-59aae2e6e5e9"
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 0s 89ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f0d7aee8100>"
]
},
"metadata": {},
"execution_count": 31
},
{
"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": [
"print(my_new_model.evaluate(yt, xt))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JhipRyhVpyk5",
"outputId": "2ca691b2-2045-4345-96bb-0c18a33627d1"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"313/313 [==============================] - 1s 3ms/step - loss: 2540.6208 - accuracy: 0.7223\n",
"[2540.620849609375, 0.7223355174064636]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from keras.utils import plot_model\n",
"\n",
"plot_model(my_new_model, expand_nested=True, dpi=60, show_shapes=True, show_layer_activations=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 563
},
"id": "kApeGPRzq6B1",
"outputId": "8fb55e8e-4dfb-4775-f65c-c0e0427589a6"
},
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"my_new_model.save(f'/content/drive/MyDrive/jopa_kakayato')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a-2nkYDK3x3U",
"outputId": "dcc766bf-1f04-4a51-d966-a85867cb55e3"
},
"execution_count": 35,
"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"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"id": "A6zjyPiT6Uev",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "397c70dc-fdfb-4655-893d-c312139b9f56"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
}
]
} |