TensorFlow 莫烦 手写识别 cross_entry (五)

# -*- coding: utf-8 -*-"""Created on Thu Apr 20 15:40:48 2017同济大学 土木大楼B406@author: Administrator"""from __future__ import print_function import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)def add_layer(inputs,in_size,out_size,activation_function=None):    Weights = tf.Variable(tf.random_normal([in_size,out_size]))    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 outputsdef compute_accuracy(v_xs,v_ys):    y_pre=sess.run(prediction,feed_dict={xs:v_xs})    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})    return resultxs=tf.placeholder(tf.float32,[None,784])ys=tf.placeholder(tf.float32,[None,10])prediction=add_layer(xs,784,10,activation_function=tf.nn.softmax)cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)sess=tf.Session()sess.run(tf.initialize_all_variables())for i in range(1000):   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))