项目下载链接：链接: https://pan.baidu.com/s/1OfICplwlEtRBz_ta7Nwyyg?pwd=yr5c 提取码: yr5c 复制这段内容后打开百度网盘手机App，操作更方便哦 
--来自百度网盘超级会员v4的分享

1.模型代码：model.py

# -*- coding: utf-8 -*-
# file: model.py
# author: JinTian
# time: 07/03/2017 3:07 PM
# Copyright 2017 JinTian. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
import tensorflow as tf
import numpy as np


def rnn_model(model, input_data, output_data, vocab_size, rnn_size=128, num_layers=2, batch_size=64,
              learning_rate=0.01):
    """
    construct rnn seq2seq model.
    :param model: model class
    :param input_data: input data placeholder
    :param output_data: output data placeholder
    :param vocab_size:
    :param rnn_size:
    :param num_layers:
    :param batch_size:
    :param learning_rate:
    :return:
    """
    end_points = {}

    if model == 'rnn':
        cell_fun = tf.contrib.rnn.BasicRNNCell
    elif model == 'gru':
        cell_fun = tf.contrib.rnn.GRUCell
    elif model == 'lstm':
        cell_fun = tf.contrib.rnn.BasicLSTMCell

    cell = cell_fun(rnn_size, state_is_tuple=True)
    cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers, state_is_tuple=True)

    if output_data is not None:
        initial_state = cell.zero_state(batch_size, tf.float32)
    else:
        initial_state = cell.zero_state(1, tf.float32)

    with tf.device("/cpu"):
        embedding = tf.get_variable('embedding', initializer=tf.random_uniform(
            [vocab_size + 1, rnn_size], -1.0, 1.0))
        inputs = tf.nn.embedding_lookup(embedding, input_data)

    # [batch_size, ?, rnn_size] = [64, ?, 128]
    outputs, last_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=initial_state)
    output = tf.reshape(outputs, [-1, rnn_size])

    weights = tf.Variable(tf.truncated_normal([rnn_size, vocab_size + 1])) # 产生一个正态分布
    bias = tf.Variable(tf.zeros(shape=[vocab_size + 1]))
    # 预测值h
    logits = tf.nn.bias_add(tf.matmul(output, weights), bias=bias)
    # [?, vocab_size+1]

    if output_data is not None:
        # output_data must be one-hot encode 真实标签
        labels = tf.one_hot(tf.reshape(output_data, [-1]), depth=vocab_size + 1)
        # should be [?, vocab_size+1]

        loss = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
        # loss shape should be [?, vocab_size+1]
        total_loss = tf.reduce_mean(loss)
        train_op = tf.train.AdamOptimizer(learning_rate).minimize(total_loss)
        # gvs = train_op.compute_gradients(total_loss) # gvs:[(10000,w1),(10,b1),(0.001,w2),(1,b2)]
        # new_gvs = []
        # for i,j in gvs:
        #     new_gvs.append((tf.clip_by_value(i,-10,10), j))
        # train_op = train_op.apply_gradients(new_gvs)梯度裁剪


        end_points['initial_state'] = initial_state
        end_points['output'] = output
        end_points['train_op'] = train_op
        end_points['total_loss'] = total_loss
        end_points['loss'] = loss
        end_points['last_state'] = last_state
    else:
        prediction = tf.nn.softmax(logits)

        end_points['initial_state'] = initial_state
        end_points['last_state'] = last_state
        end_points['prediction'] = prediction

    return end_points

2.模型保存

小知识：保存模型方式有两种：1.只保存模型参数；2.保存模型结构和参数。

        1.只保存参数：加载的时候一定要实例化模型，不然没有结构。

        2.保存参数和模型：保存的比较大，占用内存大，一般不太使用。

3.model里四个文件：

        1.checkpoint:模型保存路径

        2.第二个模型参数

        3.第三个基本没用

        3.第四个是模型的图（模型结构图）

 

2.主程序代码

# -*- coding: utf-8 -*-
# file: main.py
# author: JinTian
# time: 11/03/2017 9:53 AM
# Copyright 2017 JinTian. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
import os
import numpy as np
import tensorflow as tf
from poems.model import rnn_model
from poems.poems import process_poems, generate_batch

tf.flags.DEFINE_integer('batch_size',64, help='batch size.')#批次带线啊哦
tf.flags.DEFINE_float('learning_rate', 0.01, 'learning rate.')
tf.flags.DEFINE_string('model_dir', os.path.abspath('./model'), 'model save path.')#模型路径
tf.flags.DEFINE_string('file_path', os.path.abspath('./data/poems.txt'), 'file name of poems.')#数据路径
tf.flags.DEFINE_string('model_prefix', 'poems', 'model save prefix.')
tf.flags.DEFINE_integer('epochs', 10, 'train how many epochs.')#训练次数

FLAGS = tf.flags.FLAGS#实例化一下
#定义参数和路径

def run_training():
    if not os.path.exists(FLAGS.model_dir):
        os.makedirs(FLAGS.model_dir)

    poems_vector, word_to_int, vocabularies = process_poems(FLAGS.file_path)
    batches_inputs, batches_outputs = generate_batch(FLAGS.batch_size, poems_vector, word_to_int)

    input_data = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
    output_targets = tf.placeholder(tf.int32, [FLAGS.batch_size, None])

    end_points = rnn_model(model='lstm', input_data=input_data, output_data=output_targets, vocab_size=len(
        vocabularies), rnn_size=128, num_layers=2, batch_size=64, learning_rate=FLAGS.learning_rate)

    saver = tf.train.Saver(tf.global_variables())
    init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
    with tf.Session() as sess:
        # sess = tf_debug.LocalCLIDebugWrapperSession(sess=sess)
        # sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
        sess.run(init_op)

        start_epoch = 0
        checkpoint = tf.train.latest_checkpoint(FLAGS.model_dir)#间接训练，判断之前是否有模型，如果有就在之前基础进行训练
        if checkpoint:
            saver.restore(sess, checkpoint)#读出来模型
            print("## restore from the checkpoint {0}".format(checkpoint))
            start_epoch += int(checkpoint.split('-')[-1])
        print('## start training...')
        try:
            for epoch in range(start_epoch, FLAGS.epochs):
                n = 0
                n_chunk = len(poems_vector) // FLAGS.batch_size
                for batch in range(n_chunk):
                    loss, _, _ = sess.run([
                        end_points['total_loss'],
                        end_points['last_state'],
                        end_points['train_op']
                    ], feed_dict={input_data: batches_inputs[n], output_targets: batches_outputs[n]})
                    n += 1
                    print('Epoch: %d, batch: %d, training loss: %.6f' % (epoch, batch, loss))
                if epoch % 6 == 0:
                    saver.save(sess, os.path.join(FLAGS.model_dir, FLAGS.model_prefix), global_step=epoch)
        except KeyboardInterrupt:
            print('## Interrupt manually, try saving checkpoint for now...')
            saver.save(sess, os.path.join(FLAGS.model_dir, FLAGS.model_prefix), global_step=epoch)
            print('## Last epoch were saved, next time will start from epoch {}.'.format(epoch))


def main(_):
    run_training()


if __name__ == '__main__':
    tf.app.run()