with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print('start training')
for epoch in range(num_epoch):
sess.run(model.tr_iterator.initializer)
while True:
try:
for _ in range(k):
_, loss_D = sess.run([train_D, model.loss_D])
_, loss_G, global_step = sess.run(
[train_G, model.loss_G, model.global_step])
if not global_step % print_step:
print('Epoch: %d, Step: %d' % (epoch, global_step))
model.print_sample(10)
if not global_step % summary_step:
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str,
global_step=global_step)
except tf.errors.OutOfRangeError:
print('end of dataset')
break
if not epoch % save_epoch:
print('Saving model in %s. Global step: %d' %
(train_dir + 'model.ckpt', global_step))
saver.save(sess,
train_dir + 'model.ckpt',
