4、递归神经网络（Mnist数据集）

import tensorflow as tf
import numpy as np
from tensorflow.contrib import rnn
from tensorflow.examples.tutorials.mnist import input_data

sess = tf.Session()
mnist = input_data.read_data_sets('data', one_hot=True)


lr = 1e-3
input_size = 28      # 每行输入28个特征点
timestep_size = 28   # 持续输入28行
hidden_size = 256    # 隐含层的数量
layer_num = 2        # LSTM layer 的层数
class_num = 10       # 10分类问题

_X = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, class_num])

batch_size = tf.placeholder(tf.int32, [])
keep_prob = tf.placeholder(tf.float32, [])

X = tf.reshape(_X, [-1, 28, 28])

def lstm_cell():
    cell = rnn.LSTMCell(hidden_size, reuse=tf.get_variable_scope().reuse)
    return rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)

mlstm_cell = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(layer_num)], state_is_tuple = True)

#用全零来初始化状态
init_state = mlstm_cell.zero_state(batch_size, dtype=tf.float32)

#得到每一层的输出结果
outputs = list()
state = init_state
with tf.variable_scope('RNN'):
    for timestep in range(timestep_size):
        if timestep > 0:
            tf.get_variable_scope().reuse_variables()
        (cell_output, state) = mlstm_cell(X[:, timestep, :],state)
        outputs.append(cell_output)
h_state = outputs[-1]

#Softmax层参数
W = tf.Variable(tf.truncated_normal([hidden_size, class_num], stddev=0.1), dtype=tf.float32)
bias = tf.Variable(tf.constant(0.1,shape=[class_num]), dtype=tf.float32)
y_pre = tf.nn.softmax(tf.matmul(h_state, W) + bias)


# 损失和评估函数
cross_entropy = -tf.reduce_mean(y * tf.log(y_pre))
train_op = tf.train.AdamOptimizer(lr).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))


sess.run(tf.global_variables_initializer())
for i in range(2000):
    _batch_size = 128
    batch = mnist.train.next_batch(_batch_size)
    if (i+1)%200 == 0:
        train_accuracy = sess.run(accuracy, feed_dict={
            _X:batch[0], y: batch[1], keep_prob: 1.0, batch_size: _batch_size})
        # 已经迭代完成的 epoch 数: mnist.train.epochs_completed
        print ("Iter%d, step %d, training accuracy %g" % ( mnist.train.epochs_completed, (i+1), train_accuracy))
    sess.run(train_op, feed_dict={_X: batch[0], y: batch[1], keep_prob: 0.5, batch_size: _batch_size})

# 计算测试数据的准确率
print ("test accuracy %g"% sess.run(accuracy, feed_dict={
    _X: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0, batch_size:mnist.test.images.shape[0]}))