42efaa53edce9b84a98fef1fce24502e6d2ba941,examples/transformer.py,,,#,11

Before Change

                targettxt = [ " ".join([text_database.target_vocab._id_to_token_map_py[i] for i in line]) \
                        for line in target.tolist()]
                writer.add_summary(mgd, global_step=step)
                for index, line in enumerate(zip(source, sourcetxt, target, targettxt)):
                    print("{}:{} source:{} txt:{}".format((step-1)*32+index, step, line[0], line[1]))
                    print("{}:{} target:{} txt:{}".format((step-1)*32+index, step, line[2], line[3]))
                for var in tf.trainable_variables():

After Change

                //    print("{}:{} source:{} txt:{}".format((step-1)*32+index, step, line[0], line[1]))
                //    print("{}:{} target:{} txt:{}".format((step-1)*32+index, step, line[2], line[3]))
                //    print("{}:{} predict:{} txt:{}".format((step-1)*32+index, step, line[4], line[5]))
                print("var cnt:{}".format(len(tf.trainable_variables())))
                for var in tf.trainable_variables():
                     print("name:{}\tshape:{}\ttype:{}".format(var.name, var.shape, var.dtype))
                exit()

Instances