记录：Mnist手写识别

环境：anaconda64位+python3.6+TensorFlow 1.9.0

重点：

（1）如果你实现此代码，加载数据集Mnist失败，请前往：https://blog.csdn.net/landcruiser007/article/details/79346982，手动下载mnist数据集

（2）分类问题，当然要用到softmax和交叉熵，softmax我也只是简单的了解了一下，大家想知道细节的自己找一下。

交叉熵j精品！！传送门：https://blog.csdn.net/tsyccnh/article/details/79163834

话不多说，贴上代码

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("D:/BaiduNetdiskDownload/MNIST_data/",one_hot=True)

#增加一个层
def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))
    #bias维度：1*outsize
    biases=tf.Variable(tf.zeros([1,out_size])+0.1) 
    Wx_plus_b = tf.matmul(inputs,Weights)+biases
    if activation_function is None:
        outputs=Wx_plus_b
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs

#计算准确率
def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre=sess.run(prediction,feed_dict={xs:v_xs})
    #tf.argmax()返回各行元素最大值的位置，形成一维向量
    #tf.equal（）两个值相等返回True
    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    #计算准确率
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
    print(y_pre.shape)
    return result

xs=tf.placeholder(tf.float32,[None,784])#28*28
ys=tf.placeholder(tf.float32,[None,10])

#激活函数是softmax
prediction=add_layer(xs,784,10,activation_function=tf.nn.softmax)

#两个tf的一维向量相乘，每个元素相乘
cross_entroy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entroy)

init=tf.global_variables_initializer()#variable需要初始化
with tf.Session() as sess:
    sess.run(init)
    for i in range(500):
        #取100个数据进行学习
        batch_xs,batch_ys=mnist.train.next_batch(100)
        sess.run(train_step,feed_dict={xs: batch_xs,ys:batch_ys})
        if i%50 ==0:
            print(compute_accuracy(mnist.test.images,mnist.test.labels))