ValueError: Input 0 of layer "sequential_36" is incompatible with the layer: expected shape=(None, 256, 1), found shape=(None, 128, 1)

[Harsh-2200]

def build_generator():
model = Sequential()

#take in random vlaues and reshape it to 7x7x128
#Beginnings of the generated images

model.add(Dense(77128 , input_dim = 128))
model.add(LeakyReLU(0.2))
model.add(Reshape((7,7,128)))

Upsampling block 1

model.add(UpSampling2D()) # 7x7x128 --> 14x14x128
model.add(Conv2D(128 , 5 , padding = 'same'))
model.add(LeakyReLU(0.2))

this make more suffication in layer and create more parameter

'''
model.add(UpSampling2D())
model.add(Conv2D(1 , 5 , padding = 'same'))
model.add(LeakyReLU(0.2))
'''

Upsampling block 2

model.add(UpSampling2D())
model.add(Conv2D(128 , 5 , padding = 'same'))
model.add(LeakyReLU(0.2))

Convolutional block 1

model.add(Conv2D(128, 4, padding='same'))
model.add(LeakyReLU(0.2))

Convolutional block 2

model.add(Conv2D(128, 4, padding='same'))
model.add(LeakyReLU(0.2))

Conv layer to get to one channel

model.add(Conv2D(1, 4, padding='same', activation='sigmoid'))

return model

and i use weight of given h5 file and it giving me error of

[SamHeuer]

Were you able to resolve it?