Tensorflow通过CNN实现MINST数据分类

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prob:1})
    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

def weight_varirable(shape):
    inital=tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(inital)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

def max_poo_(x):
     return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

xs=tf.placeholder(tf.float32,[None,784])
ys=tf.placeholder(tf.float32,[None,10])
keep_prob=tf.placeholder(tf.float32)

x_image=tf.reshape(xs,[-1,28,28,1])

W_conv1=weight_varirable([5,5,1,32])
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h_pool1=max_poo_(h_conv1)

W_conv2=weight_varirable([5,5,32,64])
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
h_pool2=max_poo_(h_conv2)



W_fc1=weight_varirable([7*7*64,1024])
b_fc1=bias_variable([1024])

h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])
h_fcl=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
h_fc1_drop=tf.nn.dropout(h_fcl,keep_prob)

W_fc2=weight_varirable([1024,10])
b_fc2=bias_variable([10])

prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

cross_entropy=tf.reduce_mean(
    -tf.reduce_sum(ys*tf.log(prediction),
    reduction_indices=[1]))

train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess=tf.Session()

sess.run(tf.global_variables_initializer())

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,keep_prob:0.5})
    if i % 50 == 0:
        print(compute_accuracy(
            mnist.test.images, mnist.test.labels))

 如果有同学没有MINST数据，请到http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_download.html下载，或者QQ问我