Tensorflow手写数字识别训练，梯度下降法

# coding: utf-8

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#print("hello")

#载入数据集

mnist = input_data.read_data_sets("F:\\TensorflowProject\\MNIST_data",one_hot=True)

#每个批次的大小，训练时一次100张放入神经网络中训练

batch_size = 100

#计算一共有多少个批次

n_batch = mnist.train.num_examples//batch_size

#定义两个placeholder

x = tf.placeholder(tf.float32,[None,784])

#0-9十个数字

y = tf.placeholder(tf.float32,[None,10])

#创建一个神经网络

W = tf.Variable(tf.zeros([784,10]))

b = tf.Variable(tf.zeros([10]))

prediction = tf.nn.softmax(tf.matmul(x,W)+b)

#二次代价函数

loss = tf.reduce_mean(tf.square(y-prediction))

#使用梯度下降法

train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量

init = tf.global_variables_initializer()

#结果存放在一个布尔型列表中

correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))

#求准确率

accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

#

with tf.Session() as sess:

　　sess.run(init)

　　for epoch in range(100):

　　　　for batch in range(n_batch):

　　　　　　batch_xs,batch_ys = mnist.train.next_batch(batch_size)

　　　　　　sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})

　　　　#测试准确率

　　　　acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})

　　　　print("Iter: "+str(epoch)+" ,Testing Accuracy "+str(acc))

#运行结果

Extracting F:\TensorflowProject\MNIST_data\train-images-idx3-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\train-labels-idx1-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\t10k-images-idx3-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\t10k-labels-idx1-ubyte.gz
Iter: 0  ,Testing Accuracy  0.8322
Iter: 1  ,Testing Accuracy  0.872
Iter: 2  ,Testing Accuracy  0.8808
Iter: 3  ,Testing Accuracy  0.888
Iter: 4  ,Testing Accuracy  0.8938
Iter: 5  ,Testing Accuracy  0.8969
Iter: 6  ,Testing Accuracy  0.899
Iter: 7  ,Testing Accuracy  0.9015
Iter: 8  ,Testing Accuracy  0.9038
Iter: 9  ,Testing Accuracy  0.9055
Iter: 10  ,Testing Accuracy  0.9063
Iter: 11  ,Testing Accuracy  0.9077
Iter: 12  ,Testing Accuracy  0.9078
......
Iter: 38  ,Testing Accuracy  0.9192
Iter: 39  ,Testing Accuracy  0.9195
Iter: 40  ,Testing Accuracy  0.92
Iter: 41  ,Testing Accuracy  0.9199
Iter: 42  ,Testing Accuracy  0.9205
Iter: 43  ,Testing Accuracy  0.9201
Iter: 44  ,Testing Accuracy  0.921
Iter: 45  ,Testing Accuracy  0.9207
Iter: 46  ,Testing Accuracy  0.9214
Iter: 47  ,Testing Accuracy  0.9212
Iter: 48  ,Testing Accuracy  0.9215
Iter: 49  ,Testing Accuracy  0.9213
.....
Iter: 93  ,Testing Accuracy  0.9254
Iter: 94  ,Testing Accuracy  0.9259
Iter: 95  ,Testing Accuracy  0.926
Iter: 96  ,Testing Accuracy  0.9262
Iter: 97  ,Testing Accuracy  0.9263
Iter: 98  ,Testing Accuracy  0.9262
Iter: 99  ,Testing Accuracy  0.926