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')