Tensorflow实现一个CNN分类的例子

使用mnist中的数据，利用CNN将图片进行分类。

构建了四层的神经网络，训练需要一段时间~

convolutional layer1 + max pooling;

convolutional layer2 + max pooling;

fully connected layer1 + dropout;

fully connected layer2 to prediction.

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

mnist=input_data.read_data_sets('MNIST_data',one_hot=True)

def compulate_accuracy(v_xs,v_ys):
global prediction
y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_pro: 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, keep_pro: 1})
return result

def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.1)
return tf.Variable(initial)

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

def con2d(x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

#define placeholder for inputs to network

xs=tf.placeholder(tf.float32,[None,784])/255
ys=tf.placeholder(tf.float32,[None,10])

keep_pro=tf.placeholder(tf.float32)
x_image=tf.reshape(xs,[-1,28,28,1])

#conv1 layer
W_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(con2d(x_image,W_conv1)+b_conv1)
h_pool1=max_pool_2x2(h_conv1)   #ouputsize=14x14x32

#conv2 layer
W_conv2=weight_variable([5,5,32,64])
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(con2d(h_pool1,W_conv2)+b_conv2)
h_pool2=max_pool_2x2(h_conv2)  #outputsize=7x7x64

#fc1 layer
W_fc1=weight_variable([7*7*64,1024])
b_fc1=bias_variable([1024])
## [n_s_amples, 7, 7, 64] ->> [n_samples, 7*7*64]
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_pro)

#fc2 layer
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

#define error between prediction and real value
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)

init=tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for i in range(500):
batch_x,batch_y=mnist.train.next_batch(100)
sess.run(train_step,feed_dict={xs:batch_x,ys:batch_y,keep_pro:0.5})
if i%50 == 0:
print(compulate_accuracy(mnist.test.images,mnist.test.labels))