tensorflow进行MNIST手写数字识别-LSTM

调用LSTM进行MNIST手写数字识别。

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datafrom tensorflow.contrib import rnnmnist = input_data.read_data_sets('MNIST_data', one_hot=True)#输入图片格式是28*28n_inputs = 28#输入一行，一行有28个数据max_time = 28#序列长度lstm_size = 200#隐层单元n_class = 10#分类个数batch_size = 50#每个批次样本大小n_batch = mnist.train.num_examples // batch_size #批次个数x = tf.placeholder(tf.float32, [None, 784])y = tf.placeholder(tf.float32, [None, 10])#初始化权值weights = tf.Variable(tf.truncated_normal([lstm_size, n_class], stddev = 0.1))biase = tf.Variable(tf.constant(0.1, shape=[n_class]))def LSTM(X, weights, biase):    #inputs format : [batch_size, max_time, n_inputs]    inputs = tf.reshape(X, [-1, max_time, n_inputs])    #定义LSTM基本cell    lstm_cell = rnn.BasicLSTMCell(lstm_size)    outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32)    results = tf.nn.softmax(tf.matmul(final_state[1], weights) + biase)    return results#返回结果prediction = LSTM(x, weights, biase)#损失函数cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))#优化器train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)#计算准确率correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    for epoch in range(60):        for batch in range(batch_size):            batch_xs, batch_ys = mnist.train.next_batch(batch_size)            sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})        acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})        print('iter',epoch,'accuracy',acc)