def discriminator(height, width, channels, learning_rate, embedding_dim=64):
input_image = Input((height, width, channels))
disc = Conv2D(128, 4, strides=2, padding="same")(input_image)
disc = LeakyReLU()(disc)
disc = Conv2D(256, 4, strides=2, padding="same")(disc)
disc = LeakyReLU()(disc)
#disc = Conv2D(256, 4,strides=2,padding="same")(disc)
#disc = LeakyReLU()(disc)
disc = Conv2D(512, 4, strides=2, padding="same")(disc)
#disc = BatchNormalization()(disc)
disc = LeakyReLU()(disc)
disc = Conv2D(1024, 4, strides=2, padding="same")(disc)
disc = LeakyReLU()(disc)
disc = Flatten()(disc)
disc = Dropout(0.4)(disc)
disc = Dense(1, activation="sigmoid")(disc)
disc = Model(input_image, disc)
discriminator_optimizer = Adam(lr=LEARNING_RATE, beta_1=0.5)
disc.compile(discriminator_optimizer,
loss="binary_crossentropy",
metrics=['binary_accuracy'])
return disc
# end of the graph creation
discriminator = Model(inputs=dis_input, outputs=discriminator)
# Compilation
#############
# generator
# the optimizer
optimizer = Adam(lr=2E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# load & compilation
if LOAD:
generator.load_weights(SAVE_PATH + 'generator_model.h5')
_gnerator = generator # save an uncompile version of the generator
generator.compile(loss=vgg_loss, optimizer=optimizer)
# discriminator (use the same opti then the generator)
# load & compile
if LOAD:
discriminator.loss_weights(SAVE_PATH + 'discriminator_model.h5')
discriminator.compile(loss="binary_crossentropy",
optimizer=optimizer,
metrics=["accuracy"])
# gann
# the optimizer
optimizer = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# creat the graph based on generator and discriminator
