Next, in this class development, we'll create two methods—a block method and a  model creation method:

1. The block method is used in the model generation method—it focuses on creating a template for a block reused in this model architecture of a Deconv3D layer, BatchNorm, and a ReLU activation:

def block(self,first_layer,filter_size=512,stride_size=(2,2,2),kernel_size=
          (4,4,4),padding='same'):

        x = Deconv3D(filters=filter_size, kernel_size=kernel_size,
                    strides=stride_size, kernel_initializer='glorot_normal',
                    bias_initializer='zeros', padding=padding)(first_layer)
        x = BatchNormalization()(x)
        x = Activation(activation='relu')(x)

        return x

2. Create the model method that uses the input shape we defined in the initialization step and that starts with one block:

    def model(self):
        input_layer = Input(shape=self.INPUT_SHAPE)

        x = self.block(input_layer,filter_size=256,stride_size=
                       (1,1,1),kernel_size=(4,4,4),padding='valid')

3. Create a second block and halve the number of filters:

x = self.block(x,filter_size=128,stride_size=(2,2,2),kernel_size=(4,4,4))

4. The final block requires a few changes, so we'll define it explicitly  here—namely, the padding:

x = Deconv3D(filters=3, kernel_size=(4,4,4),
             strides=(2,2,2), kernel_initializer='glorot_normal',
             bias_initializer='zeros', padding='same')(x)
x = BatchNormalization()(x)

5. At the end of this method, use a sigmoid activation and create the model by explicitly pointing the input and output layers:

output_layer = Activation(activation='sigmoid')(x)
model = Model(inputs=input_layer, outputs=output_layer)
return model

6. The final action is to define the summary method (this should start to look familiar by now):

def summary(self):
    return self.Generator.summary()

The next recipe will focus on developing the discriminator class!