The following are the Generator and Discriminator model architectures for a (modified) WGAN model that I have come up with for downsampling rainfall data by a factor of 4 (only as a test). The input shape of the dataset is (8030, 14, 21) and the output shape I want is (8030, 28, 42).
def build_generator(input_shape): inputs = Input(shape=input_shape, name='generator_input') # Downsampling downsample_3 = conv_block(inputs, 128, (3, 3), 'downsample_3') downsample_2 = conv_block(downsample_3, 64, (3, 3), 'downsample_2') downsample_1 = conv_block(downsample_2, 32, (3, 3), 'downsample_1') # Bottleneck bottleneck_0 = conv_block(downsample_1, 16, (3, 3), 'bottleneck_0') bottleneck_00 = conv_block(bottleneck_0, 16, (3, 3), 'bottleneck_00') # Upsampling upsample_1 = deconv_block(bottleneck_00, 32, (3, 3), 'upsample_1') upsample_2 = deconv_block(upsample_1, 64, (3, 3), 'upsample_2') upsample_3 = deconv_block(upsample_2, 128, (3, 3), 'upsample_3') # Final Upsample to required shape outputs = Conv2DTranspose(filters=1, kernel_size=(3, 3), kernel_initializer=he_normal(), padding='same', strides=(2, 2), activation='relu', name='final_upsample_conv')(upsample_3) model = Model(inputs=inputs, outputs=outputs, name='generator') return modeldef build_discriminator(input_shape): input_layer = Input(shape=input_shape, name='discriminator_input') x = conv_block(input_layer, filters=16, kernel_size=(3, 3), name='conv1')#, use_batch_norm=False) x = conv_block(x, filters=32, kernel_size=(3, 3), name='conv2')# use_batch_norm=False) x = conv_block(x, filters=128, kernel_size=(3, 3), name='conv3')# use_batch_norm=False) x = MaxPooling2D()(x) x = Dropout(0.25)(x) output_layer = Dense(1, activation='linear')(x) model = Model(inputs=input_layer, outputs=output_layer, name='discriminator') return model'''Going by WGAN implementations in the literature I use the RMSprop optimizer instead of ADAM with a learning rate of 5e-5 and momentum of 0.5. Additionally I clip the discriminator weights to fall between -1e-2 and 1e-2. Traditionally (to the best of my knowledge), discriminator models output a single value loss conveying whether it is able to distinguish real output from fake outputs. In my case the discriminator output has the shape (None, 14, 21, 1) and it seems to work better but I don't understand why. Does anyone have an idea why this might happen? Thanks in advance!
Edit: These are the architectures of conv_block and deconv_block
def conv_block(x, filters, kernel_size, name, use_batch_norm=True): x = Conv2D(filters=filters, kernel_size=kernel_size, padding='same', kernel_initializer=he_normal(), name=name+'_conv')(x) x = LeakyReLU(alpha=0.2, name=name+'_lrelu')(x) if use_batch_norm: x = BatchNormalization(name=name+'_bn')(x) x = Dropout(0.25, name=name+'_dropout')(x) return xdef deconv_block(x, filters, kernel_size, name, use_batch_norm=True): x = Conv2DTranspose(filters=filters, kernel_size=kernel_size, padding='same', kernel_initializer=he_normal(), name=name+'_deconv')(x) x = LeakyReLU(alpha=0.2, name=name+'_lrelu')(x) if use_batch_norm: x = BatchNormalization(name=name+'_bn')(x) return x