#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
tensorflow之RNN
循环神经网络做手写数据集分类
"""import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data#设置随机数来比较两种计算结果
tf.set_random_seed(1)#导入手写数据集
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)#设置参数
lr = 0.001
training_iters = 100000
batch_size = 128n_inputs = 28   # MNIST 输入为图片(img shape: 28*28)对应到图片像素的一行
n_steps = 28    # time steps 对应到图片有多少列
n_hidden_units = 128   # 隐藏层神经元个数
n_classes = 10      # MNIST分类结果为10#定义权重
weights = {#(28,128)'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units]))#(128,10)'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))}
#定义bias
biases = {# (128, )'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),# (10, )'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
}def RNN(X, weights, biases):#作为cell输入的隐藏层#######################################################输入层#将输入shape从X三维输入变为二维(128 batch * 28 steps, 128 hidden)X = tf.reshape(X, [-1,n_inputs])#隐藏层# X_in = (128 batch * 28 steps, 128 hidden)X_in = tf.matmul(X, weights['in']) + biases['in']# 传给cell时需要将二维转为三维X_in ==> (128 batch, 28 steps, 128 hidden)X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])#cell########################################################LSTM cell forget_bias=1.0表示最开始学习我们不希望忘掉任何state, 　　 #state_is_tuple=True这个为true表示记录每个时间点的cell状态和输出值,以后会默认为truecell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)#将lstm cell 分成两部分(c_state, h_state)，对应到lstm一个是主线c_state（没有cell的遗忘），　　 #支线是h_state（有cell的遗忘），zero_state将每个t时间的cell初始化为0，init_state = cell.zero_state(batch_size, dtype=tf.float32)#outputs为lstm所有输出结果包括每个时刻cell的state，和输出值，final_state为最后的结果，　　 #time_major参数表示时间序列的位置是否为输入数据的第一个维度，由于我们是在第二个维度，所以为falseoutputs, final_state = tf.nn.dynamic_rnn(cell, X_in, initial_state=init_state, time_major=False)#1.将隐藏层的输出作为最后结果，只有一个结果#results = tf.matmul(final_state[1], weights['out']) + biases['out']#2.将每一步的结果输出到lists，在对outputs unstack后[1,0, 2]是将outputs list中每个tuple中元素对应展开tf.unstack(tf.transpose(outputs, [1, 0, 2]))results = tf.matmul(outputs[-1], weights['out']) + biases['out'] # shape = (128, 10)return resultspred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) with tf.Session() as sess:init = tf.global_variables_initializer()sess.run(init)step = 0while step * batch_size < training_iters:batch_xs, batch_ys = mnist.train.next_batch(batch_size)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