def GeneratorDeconv(image_size = 64):
L = int(image_size)
inputs = Input(shape = (100, ))
x = Dense(512*int(L/16)**2)(inputs) #shape(512*(L/16)**2,)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Reshape((int(L/16), int(L/16), 512))(x) # shape(L/16, L/16, 512)
x = Conv2DTranspose(256, (4, 4), strides = (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L/8, L/8, 256)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(128, (4, 4), strides = (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L/4, L/4, 128)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(64, (4, 4), strides = (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L/2, L/2, 64)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(3, (4, 4), strides= (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L, L, 3)
images = Activation('tanh')(x)
model = Model(inputs = inputs, outputs = images)
model.summary()
return model
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