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import tensorflow as tf from tensorflow.keras import layers def build_generator(z_dim): model = tf.keras.Sequential([ layers.Dense(256, input_dim=z_dim), layers.LeakyReLU(alpha=0.2), layers.Dense(512), layers.LeakyReLU(alpha=0.2), layers.Dense(1024), layers.LeakyReLU(alpha=0.2), layers.Dense(28 * 28 * 1, activation='tanh'), layers.Reshape((28, 28, 1)) ]) return model Use code with caution. Step 2: Define the Discriminator

Instead of hard labels (1 for real, 0 for fake), use smooth labels (0.9 for real, 0.1 for fake) to prevent the Discriminator from becoming too powerful early on. gans in action pdf github

Scale your training images to a range between -1 and 1, and use tanh as the activation function for the final layer of your Generator. import tensorflow as tf from tensorflow

The training process of GANs is typically done using an alternating optimization approach, where the discriminator network is trained for one or several iterations, followed by the generator network. The training process of GANs is typically done

The most critical resource for the book is its Official GitHub Repository . This companion repo contains: