saver.restore(sess, ckpt_file)
else:
print('initializing the model...')
sess.run(initializer)
feed_dict = make_feed_dict(
train_data.next(args.init_batch_size), init=True
) # manually retrieve exactly init_batch_size examples
sess.run(init_pass, feed_dict)
# generate samples from the model
sample_x = []
for i in range(args.num_samples):
sample_x.append(sample_from_model(sess))
sample_x = np.concatenate(sample_x, axis=0)
img_tile = plotting.img_tile(sample_x[:100],
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile, title=args.data_set + ' samples')
plotting.plt.savefig(
os.path.join(args.save_dir,
'%s_sample%d.png' % (args.data_set, epoch)))
plotting.plt.close('all')
np.savez(
os.path.join(args.save_dir,
'%s_sample%d.npz' % (args.data_set, epoch)),
sample_x)
print('starting training')
# train for one epoch
train_losses = []
print(
"Iteration %d, time = %ds, train bits_per_dim = %.4f, test bits_per_dim = %.4f"
% (epoch, time.time() - begin, train_loss_gen, test_loss_gen))
sys.stdout.flush()
if epoch % args.save_interval == 0:
# generate samples from the model
sample_x = []
for i in range(args.num_samples):
sample_x.append(sample_from_model(sess))
# import ipdb; ipdb.set_trace()
sample_x = np.concatenate(sample_x, axis=0)
img_tile = plotting.img_tile(sample_x[:100].reshape(
sample_x.shape[:-1]),
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile, title=args.data_set + ' samples')
plotting.plt.savefig(
os.path.join(args.save_dir,
'%s_sample%d.png' % (args.data_set, epoch)))
plotting.plt.close('all')
np.savez(
os.path.join(args.save_dir,
'%s_sample%d.npz' % (args.data_set, epoch)),
sample_x)
# save params
saver.save(sess,
args.save_dir + '/params_' + args.data_set + '.ckpt')
