Generative Adversarial Networks Projects

The residual blocks

The residual blocks contain two 2D convolutional layers, each followed by a batch normalization layer and an activation function.

1. Let's define the residual blocks. This code describes the residual blocks completely:

def residual_block(input):    """    Residual block in the generator network    :return:    """    x = Conv2D(128 * 4, kernel_size=(3, 3), padding='same', strides=1)(input)    x = BatchNormalization()(x)    x = ReLU()(x)    x = Conv2D(128 * 4, kernel_size=(3, 3), strides=1, padding='same')(x)    x = BatchNormalization()(x)    x = add([x, input])    x = ReLU()(x)    return x

The initial input is added to the output of the second 2D convolutional layer. The resultant tensor will be the output of the block.

2. Next, add a 2D convolutional block ...