tensorflow使用RNN分析mnist手写体数字数据集

import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
#设置训练的超参数，分别设置学习率、训练次数和每轮训练的数据大小
# 设置训练的超参数
lr = 0.001
training_iters = 100000
batch_size = 128
#为了使用RNN来分类图片，我们把每张图片的行看成是一个像素序列（sequence）。因为MNIST图片的大小是28×28像素，
# 所以我们把每一个图像样本看成一行行的序列。因此，共有（28个元素的序列）×（28行），然后每一步输入的序列长度是28，输入的步数是28步
# 神经网络的参数
n_inputs = 28  # 输入层的n
n_steps = 28  # 28长度
n_hidden_units = 128   # 隐藏层的神经元个数
n_classes = 10   # 输出的数量，即分类的类别，0～9个数字，共有10个
#定义输入数据及权重
# 输入数据占位符
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_classes])

# 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]))
}
biases = {
    # (128, )
    'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
    # (10, )
    'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
}
#定义RNN模型
def RNN(X, weights, biases):
    # 把输入的X转换成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, 128 hidden)
    X_in = tf.matmul(X, weights['in']) + biases['in']
    # X_in ==> (128 batch, 28 steps, 128 hidden)
    X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
    # 这里采用基本的LSTM循环网络单元：basic LSTM Cell
    lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units, forget_bias=1.0,
                                             state_is_tuple=True)
    # 初始化为零值，lstm单元由两个部分组成：(c_state, h_state)
    init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32)

    # dynamic_rnn 接收张量(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，优化器采用AdamOptimizer
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(lr).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))
#在一个会话中启动图，开始训练，每20次输出1次准确率的大小
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    step = 0
    while 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