{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "61abc7e2", "metadata": { "id": "61abc7e2" }, "outputs": [], "source": [ "from keras.datasets import mnist\n", "from keras.layers import Input, Dense, Reshape, Flatten\n", "from keras.layers import BatchNormalization\n", "from keras.layers import LeakyReLU\n", "from keras.models import Sequential, Model\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tensorflow as tf\n" ] }, { "cell_type": "code", "execution_count": null, "id": "abfff1b0", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "abfff1b0", "outputId": "badab57c-b7fc-4e54-9aa9-6398184a7fcf" }, "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", "Train (60000, 28, 28) (60000,)\n", "Test (10000, 28, 28) (10000,)\n" ] } ], "source": [ "# load the images into memory\n", "(X_train, trainy), (X_test, testy) = mnist.load_data()\n", "# summarize the shape of the dataset\n", "print('Train', X_train.shape, trainy.shape)\n", "print('Test', X_test.shape, testy.shape)" ] }, { "cell_type": "code", "execution_count": null, "id": "712c460e", "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 406 }, "id": "712c460e", "outputId": "357dc7c9-9664-486a-c7ee-9e14398b98ef" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ], "source": [ "# Display one of the images\n", "plt.imshow(X_train[0])\n", "plt.axis('off') # Hide axis\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "816e7a0a", "metadata": { "id": "816e7a0a" }, "outputs": [], "source": [ "#Define input image dimensions\n", "#Large images take too much time and resources.\n", "img_rows = 28\n", "img_cols = 28\n", "channels = 1\n", "img_shape = (img_rows, img_cols, channels)" ] }, { "cell_type": "code", "execution_count": null, "id": "407c462c", "metadata": { "id": "407c462c" }, "outputs": [], "source": [ "def build_generator():\n", " noise_shape = (100,) # 1D array of size 100 (latent vector / noise)\n", " img_shape = (28, 28, 1) # Shape of the image\n", "\n", " # input layer for the noise\n", " noise = Input(shape=noise_shape)\n", "\n", " # generator network\n", " den = tf.keras.layers.Dense(256, activation=tf.keras.layers.LeakyReLU(alpha=0.1))(noise)\n", " den = tf.keras.layers.BatchNormalization(momentum=0.8)(den)\n", " den = tf.keras.layers.Dense(512, activation=tf.keras.layers.LeakyReLU(alpha=0.1))(den)\n", " den = tf.keras.layers.BatchNormalization(momentum=0.8)(den)\n", " den = tf.keras.layers.Dense(1024, activation=tf.keras.layers.LeakyReLU(alpha=0.1))(den)\n", " den = tf.keras.layers.BatchNormalization(momentum=0.8)(den)\n", " den = tf.keras.layers.Dense(np.prod(img_shape), activation='tanh')(den)\n", " den = tf.keras.layers.Reshape(img_shape)(den)\n", "\n", " # integrating\n", " model = tf.keras.Model(inputs=noise, outputs=den)\n", "\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "id": "2667bf82", "metadata": { "id": "2667bf82" }, "outputs": [], "source": [ "def build_generator():\n", " noise_shape = (100,) # 1D array of size 100 (latent vector / noise)\n", " img_shape = (28, 28, 1) # Shape of the image\n", "\n", " # input layer for the noise\n", " noise = tf.keras.layers.Input(shape=noise_shape)\n", "\n", " # generator network\n", " den = tf.keras.layers.Dense(7 * 7 * 128, activation=tf.keras.layers.LeakyReLU(alpha=0.1))(noise)\n", " den = tf.keras.layers.Reshape((7, 7, 128))(den)\n", " den = tf.keras.layers.BatchNormalization(momentum=0.8)(den)\n", " den = tf.keras.layers.Conv2DTranspose(64, kernel_size=(5, 5), strides=(2, 2), padding='same', activation=tf.keras.layers.LeakyReLU(alpha=0.1))(den)\n", " den = tf.keras.layers.BatchNormalization(momentum=0.8)(den)\n", " den = tf.keras.layers.Conv2DTranspose(1, kernel_size=(5, 5), strides=(2, 2), padding='same', activation='tanh')(den)\n", "\n", " # integrating\n", " model = tf.keras.Model(inputs=noise, outputs=den)\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "id": "fa3a194f", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fa3a194f", "outputId": "f473c05c-bb4e-4607-aac7-f1827e4a3dfc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"model\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_1 (InputLayer) [(None, 100)] 0 \n", " \n", " dense (Dense) (None, 6272) 633472 \n", " \n", " reshape (Reshape) (None, 7, 7, 128) 0 \n", " \n", " batch_normalization (Batch (None, 7, 7, 128) 512 \n", " Normalization) \n", " \n", " conv2d_transpose (Conv2DTr (None, 14, 14, 64) 204864 \n", " anspose) \n", " \n", " batch_normalization_1 (Bat (None, 14, 14, 64) 256 \n", " chNormalization) \n", " \n", " conv2d_transpose_1 (Conv2D (None, 28, 28, 1) 1601 \n", " Transpose) \n", " \n", "=================================================================\n", "Total params: 840705 (3.21 MB)\n", "Trainable params: 840321 (3.21 MB)\n", "Non-trainable params: 384 (1.50 KB)\n", "_________________________________________________________________\n" ] } ], "source": [ "gan =build_generator()\n", "gan.summary()" ] }, { "cell_type": "code", "execution_count": null, "id": "1697c297", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1697c297", "outputId": "f6c964c0-a77e-454c-feb2-16ebe9f1d15c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"model_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_2 (InputLayer) [(None, 100)] 0 \n", " \n", " dense_1 (Dense) (None, 6272) 633472 \n", " \n", " reshape_1 (Reshape) (None, 7, 7, 128) 0 \n", " \n", " batch_normalization_2 (Bat (None, 7, 7, 128) 512 \n", " chNormalization) \n", " \n", " conv2d_transpose_2 (Conv2D (None, 14, 14, 64) 204864 \n", " Transpose) \n", " \n", " batch_normalization_3 (Bat (None, 14, 14, 64) 256 \n", " chNormalization) \n", " \n", " conv2d_transpose_3 (Conv2D (None, 28, 28, 1) 1601 \n", " Transpose) \n", " \n", "=================================================================\n", "Total params: 840705 (3.21 MB)\n", "Trainable params: 840321 (3.21 MB)\n", "Non-trainable params: 384 (1.50 KB)\n", "_________________________________________________________________\n" ] } ], "source": [ "gan =build_generator()\n", "gan.summary()" ] }, { "cell_type": "code", "execution_count": null, "id": "7bfb4ff9", "metadata": { "id": "7bfb4ff9" }, "outputs": [], "source": [ "def build_discriminator():\n", " img_shape = (28, 28, 1) # Assuming this is defined somewhere\n", "\n", " # Define input layer for the image\n", " img = tf.keras.layers.Input(shape=img_shape)\n", "\n", " # Define your discriminator network\n", " dis = tf.keras.layers.Conv2D(128, kernel_size=(3, 3), padding='same')(img)\n", " dis = tf.keras.layers.BatchNormalization()(dis)\n", " dis = tf.keras.layers.LeakyReLU(alpha=0.1)(dis)\n", "\n", " dis = tf.keras.layers.Conv2D(64, kernel_size=(3, 3), padding='same')(dis)\n", " dis = tf.keras.layers.LeakyReLU(alpha=0.1)(dis)\n", "\n", " dis = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), padding='same')(dis)\n", " dis = tf.keras.layers.LeakyReLU(alpha=0.1)(dis)\n", "\n", " dis = tf.keras.layers.Flatten()(dis)\n", " dis = tf.keras.layers.Dense(16, activation=tf.keras.layers.LeakyReLU(alpha=0.1))(dis)\n", " dis = tf.keras.layers.Dense(8, activation=tf.keras.layers.LeakyReLU(alpha=0.1))(dis)\n", " dis = tf.keras.layers.Dense(4, activation=tf.keras.layers.LeakyReLU(alpha=0.1))(dis)\n", " validity = tf.keras.layers.Dense(1, activation='sigmoid')(dis)\n", "\n", " # Combine the input and output into a model\n", " model = tf.keras.Model(inputs=img, outputs=validity)\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "id": "5d079ed0", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5d079ed0", "outputId": "e744a054-602a-4aaa-be80-67e17008550b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"model_2\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_3 (InputLayer) [(None, 28, 28, 1)] 0 \n", " \n", " conv2d (Conv2D) (None, 28, 28, 128) 1280 \n", " \n", " batch_normalization_4 (Bat (None, 28, 28, 128) 512 \n", " chNormalization) \n", " \n", " leaky_re_lu_4 (LeakyReLU) (None, 28, 28, 128) 0 \n", " \n", " conv2d_1 (Conv2D) (None, 28, 28, 64) 73792 \n", " \n", " leaky_re_lu_5 (LeakyReLU) (None, 28, 28, 64) 0 \n", " \n", " conv2d_2 (Conv2D) (None, 28, 28, 32) 18464 \n", " \n", " leaky_re_lu_6 (LeakyReLU) (None, 28, 28, 32) 0 \n", " \n", " flatten (Flatten) (None, 25088) 0 \n", " \n", " dense_2 (Dense) (None, 16) 401424 \n", " \n", " dense_3 (Dense) (None, 8) 136 \n", " \n", " dense_4 (Dense) (None, 4) 36 \n", " \n", " dense_5 (Dense) (None, 1) 5 \n", " \n", "=================================================================\n", "Total params: 495649 (1.89 MB)\n", "Trainable params: 495393 (1.89 MB)\n", "Non-trainable params: 256 (1.00 KB)\n", "_________________________________________________________________\n" ] } ], "source": [ "dis =build_discriminator()\n", "dis.summary()" ] }, { "cell_type": "code", "execution_count": null, "id": "4d2f93ad", "metadata": { "id": "4d2f93ad" }, "outputs": [], "source": [ "def train(X_train, epochs, batch_size=256, save_interval=50):\n", "\n", "\n", " half_batch = int(batch_size / 2)\n", "\n", "\n", " for epoch in range(epochs):\n", "\n", " # ---------------------\n", " # Train Discriminator\n", " # ---------------------\n", "\n", " # Select a random half batch of real images\n", " idx = np.random.randint(0, X_train.shape[0], half_batch)\n", " imgs = X_train[idx]\n", "\n", "\n", " noise = np.random.normal(0, 1, (half_batch, 100))\n", "\n", " # Generate a half batch of fake images\n", " gen_imgs = generator.predict(noise)\n", "\n", " # Train the discriminator on real and fake images, separately\n", " #Research showed that separate training is more effective.\n", " x_dis_train = np.concatenate((imgs, gen_imgs), axis=0)\n", " y_dis_train = np.concatenate((np.ones((half_batch, 1)), np.zeros((half_batch, 1))), axis=0)\n", "\n", " indices = np.arange(x_dis_train.shape[0])\n", " np.random.shuffle(indices)\n", "\n", " x_dis_train = x_dis_train[indices]\n", " y_dis_train = y_dis_train[indices]\n", "\n", " # Train the discriminator\n", " i = 0\n", " discriminator.trainable = True\n", " while i < 20:\n", " d_history = discriminator.fit(x_dis_train,\n", " y_dis_train,\n", " #batch_size=batch_size,\n", " epochs=5,\n", " verbose=0\n", " )\n", " d_loss = d_history.history['accuracy'][-1]\n", " if d_loss > 0.90:\n", " print('yes')\n", " break\n", " i += 1\n", "\n", "\n", "#And within the same loop we train our Generator, by setting the input noise and\n", "#ultimately training the Generator to have the Discriminator label its samples as valid\n", "#by specifying the gradient loss.\n", " # ---------------------\n", " # Train Generator\n", " # ---------------------\n", "#Create noise vectors as input for generator.\n", "#Create as many noise vectors as defined by the batch size.\n", "#Based on normal distribution. Output will be of size (batch size, 100)\n", " input_noise = np.random.normal(0, 1, (batch_size, 100))\n", "\n", " # The generator wants the discriminator to label the generated samples\n", " # as valid (ones)\n", " #This is where the genrator is trying to trick discriminator into believing\n", " #the generated image is true (hence value of 1 for y)\n", " valid_y = np.array([1] * batch_size) #Creates an array of all ones of size=batch size\n", "\n", " # Generator is part of combined where it got directly linked with the discriminator\n", " # Train the generator with noise as x and 1 as y.\n", " # Again, 1 as the output as it is adversarial and if generator did a great\n", " #job of folling the discriminator then the output would be 1 (true)\n", " noise = tf.keras.layers.Input(shape=(100,))\n", " generated_img = generator(noise)\n", "\n", " discriminator.trainable = False\n", " validity = discriminator(generated_img)\n", "\n", " # The combined model (stacking generator and discriminator)\n", " combined_model = tf.keras.Model(inputs=noise, outputs=validity)\n", " combined_model.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n", " loss=\"binary_crossentropy\",\n", " metrics=[\"accuracy\"]\n", " )\n", "\n", "\n", "\n", " i=0\n", " while i < 20:\n", " g_history = combined_model.fit(input_noise,\n", " valid_y,\n", " #batch_size=batch_size,\n", " epochs=10,\n", " verbose=0\n", " )\n", " g_loss = g_history.history['accuracy'][-1]\n", " if g_loss > 0.95:\n", " print('no')\n", " break\n", " i += 1\n", "\n", "\n", " r, c = 1, 5\n", " noise = np.random.normal(0, 1, (1 * 1, 100))\n", " gen_imgs = generator.predict(noise)\n", "\n", " # Display one of the images\n", " plt.imshow(gen_imgs[0])\n", " plt.axis('off') # Hide axis\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "4659d759", "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "4659d759", "outputId": "54b34701-3b9e-4c39-f629-7d87028ca305" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1/1 [==============================] - 1s 805ms/step\n", "yes\n", "no\n", "1/1 [==============================] - 0s 159ms/step\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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UAEhKAYC0/ZXUVTfRqmhn8XSYz+vnLHuLojtSZ832hRfqmWMvljPhc2KH8aQAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoApO0P4s3qo24rPxY2DPXMuKwfs2dv/ZjNjXLmW4d13lNjTHCqc7oar68zXDjs2V3OjBu9z3ZcNq5f436d7H7omvI+OgN5UgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACApBQDSMI7WpQD4Fk8KACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgCk/wXGxuxwq6FH2wAAAABJRU5ErkJggg==\n" 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\n" 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IV1760XLmTdf9eDlz6Nz6Surua3p3xDue9cflzJGV+jromZP66uRndjfLmfe8/YXlTETEheMt9VBj1TfG+tLuUg2n7y/LsL7eC84b17y5KH3Kw+7LUQF4UlIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoApIUH8VpW6gNj7TGuJY1kjSfrQ2vf+oG76yf6x916JiKue+6Ly5ln/eYXy5kjm4+VMz9/3ofLmYiI8ybTcmbWOM/HTx4uZ37v115Rzpx//Y3lTETvPT0VDY0huHFWv3rDWn0EdNztfW9b5vvzm+dJAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEiLD+KtrZcPPsyb43YNw2p9268zkhWTeo+Oj9bH46J57TbuOlbO3Pyxy8uZ6QtuL2d+8pOvKmciIg6/d6ucOefG+8uZYa8+vLd5783lzHyclzMREcPqWj00NsbjpvXr0DJpDGZG77s+bNTH7YYjF5Qzce8D9UxEjNvb9VBjGHChw+7LUQF4UlIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoApIWXpcYTJ8oH7wxXzXd2ypmIiElj8Gq+fbKcGVYaI14rjRG95ijZeFt9qO7ydx0oZ2Z/Wn9Pl9zzuXImIiLG+jhgY+rwtNcacGyO7y3FvPcpzXfq72nl/PPq5zlcH2IcvnpvOdPWuR8W4EkBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGApBQASEoBgLT4SmpjkW/cqy99jtO9ciYiYtY41zAZ6idqZCbnnlPOzO5/sJyJiIihvigat3y5HGltbzbWTvkvOquiQ+MeX5bma+ssFQ+Nc21fsFnObN1WX2uO6P1WDgd65zoVTwoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAWngQb1hfLx+8M0I13y5HIiJisnmgHlpb+O2n4eDBcmbv4nPLmdWdnXImImLc2S1nhtX6dRi3Gx9Ud5ztqTak170OQ/1vuM7o4zivX++VM7bKmbbOezp5spzZONb4Lh0+VM5EREwag6PRGAZchCcFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUAklIAIA3j+FRbGwOgy5MCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgDpPwB0EozroBm82AAAAABJRU5ErkJggg==\n" 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\n" 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\n" 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8iYjoHvhufTSp/xzefvmry5srrrulvImIeOOa7eXNkX6hvJnsfrC8adHN1f8uIiK2vf2C8uacd91Uf9AS/S1xbPJOAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIAaVEP4jUft2t62NIc5Brv3l3eDNetLW82/kHb8bjutFPKm9H2e+sP2revPPn0hfVjghERF2y9r7y5cO5QeTNYv6686efrh/cGZ7cdO3zye24rbyajUdOzeOLyTgGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAGlRD+LFYFjfTMY/+K/jB6nhexrvrR+P62Zmy5uIxuN2S6RvPM72J09+ennzwFtfUN6cuuPG8qYbdOXNeOu28iYi2v6eoMg7BQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoApMU9iHesH7drsUTfU78wvyTPebw69c++siTP6SdL8pijHo9/TxxzvFMAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQDS4l5J5ajBsL5xERP4IfBOAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIAafqDeA1H3bpBV97048ZDcH3ftlsC3Wz97mC/0Pgwh/SO6uqvvSV7DbV8bRHRDet/g/1o1PQsnri8UwAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQOr6/hi+JAfAkvJOAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYD0P5vHLLGzJKmIAAAAAElFTkSuQmCC\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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Hy5uIiOmh+m5oeR+1vOZzPYBW//66paXypum14yXh3v7O0/43PikAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBpYeb/sqv3o+ni6Va+XDpPra/D0DeMxuXFwv7HypvJidXyJiIaf6aW52zx917D9+fiKVU+KQCQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIM18EK8bdeUvPky2+IGxbWiYTOqj6bT+nI2N+nO2+sE5wCcFAH5EFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAUjcMrpQB8AKfFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABI/wsqUvSwHsnIjAAAAABJRU5ErkJggg==\n" 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\n" 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\n" 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\n" 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\n" 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AQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASFOf2etbLjs2XE4cbN1af05ExGBYngx37ypv+uMr5c1kpb7h/5nh5cmqpgu4k7Z/Tz9Xv5y796ZHypvJk0fKm5aLp82vw4n69eV+ZbXhQafu911E++t3Mt4pAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgTX0Qb3j6zvIH7+bny5vVi/aVNxER68v1g3jbbrqnvOnX1sqbblj/3PpR/fgZL1DDUcVuYaH+nPW2r203P/WPa+pXG75flxbrz2k4mNlFw5HNiOiWt9c3DUf0xg3HDru5+u+8iMbXr+H3yjS8UwAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQOr6vu9f6k8CgFODdwoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoApP8D+xESkv0CQvkAAAAASUVORK5CYII=\n" 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\n" 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UAEiiAEASBQCSKACQpj6IF5PxSfwYACwH3hQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJC6vu/7U/0hAFgevCkAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkP4XVUK2Wb6isIQAAAAASUVORK5CYII=\n" 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\n" }, "metadata": {} } ], "source": [ "(X_train, trainy), (X_test, testy) = mnist.load_data()\n", "\n", "# Convert to float and Rescale -1 to 1 (Can also do 0 to 1)\n", "X_train = (X_train.astype(np.float32) - 127.5) / 127.5\n", "\n", "#Add channels dimension. As the input to our gen and discr. has a shape 28x28x1.\n", "X_train = np.expand_dims(X_train, axis=3)\n", "\n", "\n", "# Build and compile the discriminator first.\n", "discriminator = build_discriminator()\n", "discriminator.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n", " loss=\"binary_crossentropy\",\n", " metrics=[\"accuracy\"]\n", ")\n", "\n", "#build and compile our Discriminator, pick the loss function\n", "\n", "#SInce we are only generating (faking) images, let us not track any metrics.\n", "generator = build_generator()\n", "generator.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.0002),\n", ")\n", "\n", "##This builds the Generator and defines the input noise.\n", "#In a GAN the Generator network takes noise z as an input to produce its images.\n", "z = Input(shape=(100,)) #Our random input to the generator\n", "img = generator(z)\n", "\n", "#This ensures that when we combine our networks we only train the Generator.\n", "#While generator training we do not want discriminator weights to be adjusted.\n", "#This Doesn't affect the above descriminator training.\n", "discriminator.trainable = False\n", "\n", "#This specifies that our Discriminator will take the images generated by our Generator\n", "#and true dataset and set its output to a parameter called valid, which will indicate\n", "#whether the input is real or not.\n", "valid = discriminator(img) #Validity check on the generated image\n", "\n", "\n", "#Here we combined the models and also set our loss function and optimizer.\n", "#Again, we are only training the generator here.\n", "#The ultimate goal here is for the Generator to fool the Discriminator.\n", "# The combined model (stacked generator and discriminator) takes\n", "# noise as input => generates images => determines validity\n", "\n", "# The intended number for generation (0-9)\n", "X_train_label_1 = X_train[trainy == 8]\n", "\n", "train(X_train_label_1, epochs=100, batch_size=32, save_interval=10)" ] }, { "cell_type": "code", "execution_count": null, "id": "c97f8e7b", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c97f8e7b", "outputId": "e1755a1b-4f65-4ae4-9e47-6d1b85ca272f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(5851, 28, 28, 1)" ] }, "metadata": {}, "execution_count": 13 } ], "source": [ "X_train_label_1.shape" ] }, { "cell_type": "code", "execution_count": null, "id": "6b14ba33", "metadata": { "id": "6b14ba33" }, "outputs": [], "source": [] } ], "metadata": { "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": "3.9.7" }, "colab": { "provenance": [] } }, "nbformat": 4, "nbformat_minor": 5 }