train_deep_g_m.py 文件源码

def batch_dev_step(x_batch, y_batch, writer=None):
            """
            Evaluates model on a dev set
            """
            batches = data_helpers.batch_iter(
            zip(x_batch, y_batch), FLAGS.batch_size, 1)
            t_acc = 0.0
            t_acc = float(t_acc)
            t_loss = 0.0
            t_loss = float(t_loss)
            t = 0
            f_r = open(file_name, "a+")
            step1 = 0
            for batch in batches:
                x_batch, y_batch = zip(*batch)
                feed_dict = {
                  cnn.input_x: x_batch,
                  cnn.input_y: y_batch,
                  cnn.dropout_keep_prob: 1.0
                }
                step, summaries, loss, accuracy = sess.run(
                    [global_step, dev_summary_op, cnn.loss, cnn.accuracy],
                    feed_dict)
                time_str = datetime.datetime.now().isoformat()
                print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                t_acc += accuracy
                t_loss += loss
                t += 1
                step1 = step
                if writer:
                    writer.add_summary(summaries, step)
            f_r.write(str(step1) + " step " + " accuracy " + str(t_acc/t) + " loss " + str(t_loss/t) + "\n")
            print "total ", t_acc/t, "loss", t_loss/t

        # Generate batches