}) inference[inference > 1.0] = 1.0 inference[inference < 0.0] = 0.0 inference = inference * 255.0 metric = tool.psnr(inference, test_data[j][2]) format_time = str( time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) log_info = format_time + ' ' + 'iters:%d, img:%d, loss:%.6f, psnr:%.6f' % ( i, j, loss, metric) print(log_info) val_log.write(log_info + '\n') writer.add_summary(train_summary, i) writer.close() if __name__ == '__main__': cfg = Config('SRCNN') tool = Tools() batch_size = 64 # train data datasets_path = './datasets/training_91_image_patches.h5' data, label = tool.read_h5_file(datasets_path) data_loder = tool.data_iterator(data, label, batch_size) # val data path = './datasets/Test/Set5' test_data = tool.read_test_data(path, cfg) train(cfg, data_loder, test_data)