Function train

chatbotv5/demo.py:162–196

训练过程

()

def train():
    """
    训练过程
    """
    # train_set = [[[5, 7, 9], [11, 13, 15, EOS_ID]], [[7, 9, 11], [13, 15, 17, EOS_ID]],
    #              [[15, 17, 19], [21, 23, 25, EOS_ID]]]
    train_set = get_train_set()
    with tf.Session() as sess:

        encoder_inputs, decoder_inputs, target_weights, outputs, loss, update, saver, learning_rate_decay_op, learning_rate = get_model()

        # 全部变量初始化
        sess.run(tf.global_variables_initializer())

        # 训练很多次迭代，每隔10次打印一次loss，可以看情况直接ctrl+c停止
        previous_losses = []
        for step in xrange(20000):
            sample_encoder_inputs, sample_decoder_inputs, sample_target_weights = get_samples(train_set, 1000)
            input_feed = {}
            for l in xrange(input_seq_len):
                input_feed[encoder_inputs[l].name] = sample_encoder_inputs[l]
            for l in xrange(output_seq_len):
                input_feed[decoder_inputs[l].name] = sample_decoder_inputs[l]
                input_feed[target_weights[l].name] = sample_target_weights[l]
            input_feed[decoder_inputs[output_seq_len].name] = np.zeros([len(sample_decoder_inputs[0])], dtype=np.int32)
            [loss_ret, _] = sess.run([loss, update], input_feed)
            if step % 10 == 0:
                print 'step=', step, 'loss=', loss_ret, 'learning_rate=', learning_rate.eval()

                if len(previous_losses) > 5 and loss_ret > max(previous_losses[-5:]):
                    sess.run(learning_rate_decay_op)
                previous_losses.append(loss_ret)

                # 模型持久化
                saver.save(sess, './model/demo')


def predict():