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- def make_generator_model():
- model = tf.keras.Sequential()
- model.add(layers.Dense(8*8*256, use_bias=False, input_shape=(100,)))
- model.add(layers.BatchNormalization())
- model.add(layers.LeakyReLU())
- model.add(layers.Reshape((8, 8, 256)))
- assert model.output_shape == (None, 8, 8, 256) # Note: None is the batch size
- model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
- assert model.output_shape == (None, 8, 8, 128)
- model.add(layers.BatchNormalization())
- model.add(layers.LeakyReLU())
- model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
- assert model.output_shape == (None, 16, 16, 64)
- model.add(layers.BatchNormalization())
- model.add(layers.LeakyReLU())
- model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
- assert model.output_shape == (None, 32, 32, 3)
- return model
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