return gradient_penalty
if __name__ == "__main__":
MAX_EPOCH = 100
if torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
netG = Generator().to(device)
netD = discriminator().to(device)
optimizerD = optim.RMSprop(netD.parameters(), lr=1e-5)
optimizerG = optim.RMSprop(netG.parameters(), lr=1e-5)
config = clstm_config()
train, test = get_dataloader(config.batch_size)
#----------------------------- train
for epoch in range(100):
print(f'====== epoch {epoch} ======')
netD.train()
netG.train()
# ----------------------------- train
#lossD, lossG = 0, 0
for x, y in train:
# -------- train D
#print('input shape == >',x.shape)
for parm in netD.parameters():
def display_progression_epoch(j, id_max):
'''See epoch progression
'''
batch_progression = int((j / id_max) * 100)
sys.stdout.write(str(batch_progression) + ' % epoch' + chr(13))
_ = sys.stdout.flush
def create_logdir(method, weight, rd):
""" Directory to save training logs, weights, biases, etc."""
return "gan/train_logs/kdd/{}/{}/{}".format(weight, method, rd)
gen_config = generator_config()
dis_config = discriminator_config()
train_config = gan_training_config()
data_config = clstm_config()
data_loader = DataLoader(data_config)
rng = np.random.RandomState(RANDOM_SEED)
with tf.Graph().as_default():
with tf.Session() as sess:
generator = Generator(gen_config)
discriminator = Discriminator(dis_config)
generator.build()
#Pretrain csltm classifier in generator
# Train procedure
global_step = tf.Variable(0, name='global_step', trainable=False)
# Learning rate decay
starter_learning_rate = train_config.pretrain_learning_rate
learning_rate = tf.train.exponential_decay(starter_learning_rate,
