def __init__(self, input, conditions, labels, cfg):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
model = CGAN_model(cfg.batch_size)
d_logit = model.network_D(input, conditions, name="D")
self.d_loss = fluid.layers.reduce_mean(
fluid.layers.sigmoid_cross_entropy_with_logits(x=d_logit,
label=labels))
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and (var.name.startswith("D")):
vars.append(var.name)
optimizer = fluid.optimizer.Adam(learning_rate=cfg.learning_rate,
beta1=0.5,
name="net_D")
optimizer.minimize(self.d_loss, parameter_list=vars)
def __init__(self, input, conditions, cfg):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
model = CGAN_model()
self.fake = model.network_G(input, conditions, name="G")
self.infer_program = self.program.clone()
d_fake = model.network_D(self.fake, conditions, name="D")
fake_labels = fluid.layers.fill_constant_batch_size_like(
input=input, dtype='float32', shape=[-1, 1], value=1.0)
self.g_loss = fluid.layers.reduce_mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=d_fake, label=fake_labels))
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and (var.name.startswith("G")):
vars.append(var.name)
optimizer = fluid.optimizer.Adam(
learning_rate=cfg.learning_rate, beta1=0.5, name="net_G")
optimizer.minimize(self.g_loss, parameter_list=vars)
