mnist手写数字识别（TensorFlow-GPU）------原理及源码

# -*- coding: utf-8 -*-
# !/usr/bin/env python
# @Time    : 2019/5/17 17:03
# @Author  : xhh
# @Desc    :  minist数据集下载
# @File    : mnist_data_load.py
# @Software: PyCharm
from tensorflow.examples.tutorials.mnist import  input_data
import pylab

mnist = input_data.read_data_sets("MINST_daya/", one_hot=True)
print("输入数据：",mnist.train.images)
print("数据的shape：",mnist.train.images.shape)

# 展示数据集中的一张图片
im = mnist.train.images[1]
im = im.reshape(-1,28)
pylab.imshow(im)
pylab.show()

from tensorflow.examples.tutorials.mnist import  input_data
import pylab
import tensorflow as tf

mnist = input_data.read_data_sets("MINST_daya/", one_hot=True)

tf.reset_default_graph()

# 定义占位符
x = tf.placeholder(tf.float32, [None, 784])  # mnist data 维度28*28=784
y = tf.placeholder(tf.float32, [None, 10])  #0-9 数字 ==>10class

# 定义学习参数
# 设置模型的权重
W = tf.Variable(tf.random_normal([784, 10]))  # W的维度是[784, 10]
b = tf.Variable(tf.zeros([10]))

# 定义输出节点， 构建模型
pred = tf.nn.softmax(tf.matmul(x, W) + b)  # softmax分类

# 定义反向传播的结构，编译训练模型，得到合适的参数
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

# 参数设置
learning_rate = 0.01
# 使用梯度下降优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

training_epochs = 25   # 将整个训练样本迭代25次
batch_size = 100    # 在训练过程中每次随机抽取100条数据进行训练
display_step = 1   # 迭代的步数
saver = tf.train.Saver()
model_path = "mnist/521model.ckpt" # 模型保存的位置

# 开始训练
with tf.Session()  as sess:
    # 初始化节点
    sess.run(tf.global_variables_initializer())

    # 启动循环开始训练
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # 遍历全部的数据集
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # 运行和优化节点的损失函数值
            _, c = sess.run([optimizer, cost], feed_dict={x:batch_xs,
                                                          y: batch_ys})
            # 计算平均损失值
            avg_cost += c / total_batch

        # 显示训练中的详细信息
        if (epoch+1) % display_step ==0:
            print("Epoch:","%04d"%(epoch+1), "cost=",'{:.9f}'.format(avg_cost))

    print("训练成功！！")

    # 模型测试
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("准确度：",accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))

    # 保存模型的权重
    save_path = saver.save(sess, model_path)
    print("模型文件在：%s"%save_path)

# 读取模型
print("启动第二次session")
with tf.Session() as sess2:
    # 初始化参数
    sess2.run(tf.global_variables_initializer())
    #从保存的模型中获取权重
    saver.restore(sess2, model_path)

    # 测试 model
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("准确度：",accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))

    output = tf.argmax(pred, 1)
    batch_xs, batch_ys = mnist.train.next_batch(2)
    outputval, predv = sess2.run([output, pred], feed_dict={x:batch_xs})
    print(outputval, pred, batch_ys)

    im = batch_xs[0]
    im = im.reshape(-1, 28)
    pylab.imshow(im)
    pylab.show()

    im = batch_xs[1]
    im = im.reshape(-1, 28)
    pylab.imshow(im)
    pylab.show()