通过tensorflow分类手写数字

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#number 1 to 10 data

mnist = 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 outputs

#计算准确度

def compute_accuracy(v_xs,v_ys):

    global prediction

    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 result

#define placeholder for inputs to network

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

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

#add output layer

prediction = add_layer(xs,784,10,activation_function=tf.nn.softmax)

#the error between prediction and real data

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()

#important step()

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)