[Python] How to match the output shape of generator and the input shape of discriminator in...

I am working on my first GANs model, I've followed Tensorflow's official documentation using MNIST dataset. I've run it smoothly. I tried to replace MNIST with my own dataset, I've prepared it to match the same size as MNIST: 28 * 28, it works.

However, my dataset is more complicated than MNIST so I tried to make the image size of my dataset larger: 512 * 512, but I keep getting errors related to input & output shape. I couldn't figure out the relationship between all these input and output shapes of the discriminator and generator. Assuming I want to change my dataset from 28 * 28 (MNIST size) to y*y (custom size) which input/output shapes exactly I need to tune in these layers? and why? anyone could clarify this flow?

This is my code where I reshape my datasets to match MNIST size:

train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')


and here I normalize it :

train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]


This is the generator model where the output shape of the last layer indicating something 28 * 28:

def make_generator_model():
model = tf.keras.Sequential()
model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())

model.add(layers.Reshape((7, 7, 256)))
assert model.output_shape == (None, 7, 7, 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, 7, 7, 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, 14, 14, 64)
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())

model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
assert model.output_shape == (None, 28, 28, 1)

return model


This is the discriminator model, where the input of the first layer indicating something 28 * 28:

def make_discriminator_model():
model = tf.keras.Sequential()
model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
input_shape=[28, 28, 1]))
model.add(layers.LeakyReLU())
model.add(layers.Dropout(0.3))

model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
model.add(layers.LeakyReLU())
model.add(layers.Dropout(0.3))

model.add(layers.Flatten())
model.add(layers.Dense(1))

return model

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