#加载TF并导入数据集

import tensorflow as tf

from tensorflow.contrib import rnn

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("E:\\MNIST_data\\", one_hot=True)

#设置训练的超参数，学习率 训练迭代最大次数，输入数据的个数

learning_rate= 0.001   #(learning_rate)

training_iters = 100000

batch_size = 128

# 神经网络参数

n_inputs = 28 #输出层的n

n_steps = 28 # 长度

n_hidden = 128 # 隐藏层的神经元个数

n_classes = 10 # MNIST的分类类别 (0-9)

# 定义输出数据及其权重

# 输入数据的占位符

x = tf.placeholder("float", [None, n_steps, n_inputs])

y = tf.placeholder("float", [None, n_classes])

# 定义权重

weights ={

    'in': tf.Variable(tf.random_normal([n_inputs, n_hidden])),

    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))

}

biases = {

    'in': tf.Variable(tf.random_normal([n_hidden,])),

    'out': tf.Variable(tf.random_normal([n_classes, ]))

}

#定义RNN模型

def RNN(X, weights, biases):

    #把输入的X转化成X (128 batch * 28 steps ,28 inputs)

    X = tf.reshape(X,[-1,n_inputs])

    #  进入隐藏层

    # X_in = (128 batch * 28 steps ,28 hidden)

    X_in = tf.matmul(X,weights['in']) + biases['in']

    # X_in = (128 batch * 28 steps ,28 hidden)

    X_in=tf.reshape(X_in,[-1,n_steps,n_hidden])

    #采用LSTM循环神经网络单元 basic LSTM Cell

    lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0,state_is_tuple=True)

    # 初始化为0  lstm 单元 由 h_cell，h_state两部分组成

    init_state=lstm_cell.zero_state(batch_size,dtype=tf.float32)

    # dynamic_rnn接受张量(batch ,steps,inputs)或者(steps,batch,inputs) 作为X_in

    outputs,final_state=tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=init_state,time_major=False)

    results=tf.matmul(final_state[1], weights['out']) + biases['out']

    return results

#定义损失函数和优化器，采用AdamOptimizer优化器

pred=RNN(x,weights,biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))

train_op= tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 定义模型预测结果及准确率计算方法

correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 训练模型及评估模型

# 定义一个会话，启动图，每20次输出一次准确率

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    step = 0

    # 训练，达到最大迭代次数

    while step * batch_size < training_iters:

        batch_xs, batch_ys = mnist.train.next_batch(batch_size)

        # Reshape data to get 28 seq of 28 elements

        batch_xs = batch_xs.reshape((batch_size, n_steps, n_inputs))

        sess.run(train_op, feed_dict={x: batch_xs, y: batch_ys})

        if step % 20 == 0:

            print(sess.run(accuracy,feed_dict={x:batch_xs, y:batch_ys}))

        step +=1