手写数据集识别（方法一）

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

minist=input_data.read_data_sets("D://MNIST_data",one_hot=True)
batch_size=100  #定义一个批次数量大小
nbatch=minist.train.num_examples//batch_size  #计算多少个批次

#定义两个PlaceHolder
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])

#创建一个简单的神经网络
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))
train_step=tf.train.GradientDescentOptimizer(0.01).minimize(loss)

init=tf.global_variables_initializer()

#结果存在一个布尔型类表中
correctPrediction=tf.equal(tf.arg_max(y,1),tf.arg_max(prediction,1))
accuracy=tf.reduce_mean(tf.cast(correctPrediction,tf.float32))

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(1000):
        for batch in range(nbatch):
            batch_xs,batchys=minist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batchys})
        acc=sess.run(accuracy,feed_dict={x:minist.test.images,y:minist.test.labels})
        print("迭代次数："+str(epoch)+ "  "+"测试准确率： "+str(acc))

 

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最后训练打印结果如下：

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