Mnist之RNN实现方式二

1、Mnist 之 RNN 实现方式二# -*- coding: utf-8 -*-import tensorflow as tf import numpy as np# 导入模型数据 from tensorflow.examples.tutorials.mnist import input_data mnist=input_data.read_data_sets(Mnist_Data,one_hot=T rue)# 输入图片是 28*28 n_inputs = 28 # 输入一行，一行有 28 个数据 max_time = 28 # 一共 28 行 lstm_size = 100 # 隐层单元

2、hiddenn_classes = 10 #10 个分类 batch_size = 10 # 每个批次 100 个样本 #总的训练次数 training_iters=20000# 这里的 none 表示第一个维度可以是 任意的长度x = tf.placeholder(tf.float32, None, 784)y = tf.placeholder(tf.float32, None, 10)# 初始化权值 lstm_size 隐层单元weights = tf.Variable(tf.truncated_normal(lstm_size, n_classes, stddev=0.1) #100,1

3、0# 初始化偏置值biases = tf.Variable(tf.constant(0.1, shape=n_classes)# 定义 RNN 网络 def RNN(X, weights, biases):#input=batch_size, max_time, n_inputs#输入变成10, 28, 28inputs = tf.reshape(X, -1, max_time, n_inputs)#定义 LSTM 基本 CELL# lstm_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)lstm_cell = tf.n

4、n.rnn_cell.BasicLSTMCell(lstm_size, forget_bias=0.1, state_is_tuple=True)#final_state0 是 cell state#final_state1 是 hidden_state outputs,final_state = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32)print(outputs: + str(outputs) print(final_state: + str(final_state)results = tf.nn.softmax(tf.ma

5、tmul(final_state1, weights)+biases)return results# 计算 RNN 的返回结果 prediction = RNN(x, weights, biases) #损失代价函数 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(lo gits=prediction, labels=y)#使用 AdamOptimizer 进行优化 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #结

6、果存放在一个布尔列表中correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1) #argmax 返回一维张量中最大的值所在的 位置#求准确率accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)# 开始训练with tf.Session() as sess: sess.run(tf.global_variables_initializer()for step inrange(training_iters):batch_xs, batch_ys = mnist.train.next_batch(batch_size)sess.run(train_step, feed_dict=x: batch_xs, y: batch_ys) if step % 10 = 0:acc = sess.run(accuracy, feed_