load_path = None
output_path = './result/auto_encoder'
dataset = DBHelper('./downloads/bing2/preprocessed_256', 2)
# if load_path is not None:
# generator.load_weights('./%s/model_g' % load_path)
# discriminator.load_weights('./%s/model_d' % load_path)
for epoch in range(EPOCH_NUM):
ae_loss = 0
iter_num = 0
for x_batch in dataset.train_ds:
data_batch = dataset.get_data(x_batch)
ae_loss += trainer.train(data_batch)
iter_num += 1
break
recon_data = trainer.test(data_batch)
recon_data = (recon_data + 1.) / 2.
shown_x_batch = (x_batch + 1.) / 2.
print('epoch-%02d: auto_encoder loss=%.3f' % (epoch, ae_loss / iter_num))
fig, ax = plt.subplots(BATCH_SIZE, 2, figsize=(6, 6))
for i in range(BATCH_SIZE):
ax[i, 0].imshow(shown_x_batch[i])
ax[i, 1].imshow(recon_data[i])
# if load_path is not None:
# generator.load_weights('./%s/model_g' % load_path)
# discriminator.load_weights('./%s/model_d' % load_path)
for epoch in range(EPOCH_NUM):
gen_f_loss = 0
gen_g_loss = 0
dis_f_loss = 0
dis_g_loss = 0
iter_num = 0
for general_batch in dataset_general.train_ds:
celeb_batch = list(dataset.train_ds)[iter_num]
general_data_batch = dataset_general.get_data(general_batch,
augment=True)
celeb_data_batch = dataset.get_data(celeb_batch, augment=True)
cur_gen_f_loss, cur_gen_g_loss, cur_dis_f_loss, cur_dis_g_loss = trainer.train(
general_data_batch, celeb_data_batch)
gen_f_loss += cur_gen_f_loss
gen_g_loss += cur_gen_g_loss
dis_f_loss += cur_dis_f_loss
dis_g_loss += cur_dis_g_loss
iter_num += 1
break
for test_batch in dataset_general.train_ds:
test_data_batch = dataset.get_data(test_batch)
recon_data = generator_general.test(test_data_batch)
recon_data = recon_data.numpy()
cycled_data = generator_celeb.test(recon_data)
