TensorFlow-简单的卷积神经网络

先弄懂卷积神经网络的原理，推荐这两篇博客：http://blog.csdn.net/yunpiao123456/article/details/52437794   http://blog.csdn.net/qq_25762497/article/details/51052861#%E6%A6%82%E6%8F%BD

简单的测试程序如下（具体各参数代表什么可以百度）：

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

 mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
 sess=tf.InteractiveSession()

 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 conv2d(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')

 x=tf.placeholder(tf.float32,[None,784])
 y_=tf.placeholder(tf.float32,[None,10])
 x_image=tf.reshape(x,[-1,28,28,1])

 w_conv1=weight_variable([5,5,1,32])
 b_conv1=bias_variable([32])
 h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)
 h_pool1=max_pool_2x2(h_conv1)

 w_conv2=weight_variable([5,5,32,64])
 b_conv2=bias_variable([64])
 h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2)
 h_pool2=max_pool_2x2(h_conv2)

 w_fc1=weight_variable([7*7*64,1024])
 b_fc1=bias_variable([1024])
 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)

 keep_prob=tf.placeholder(tf.float32)
 h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)

 w_fc2=weight_variable([1024,10])
 b_fc2=bias_variable([10])
 y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop,w_fc2)+b_fc2)

 cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y_conv),reduction_indices=[1]))
 train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

 correct_prediction=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
 accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

 tf.initialize_all_variables().run()
 for i in range(20000):
     batch=mnist.train.next_batch(50)
     if i%100==0:
         train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
         print("step %d,training accuracy %g"%(i,train_accuracy))
     train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})

 print("test accuracy %g"%accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))

运行结果：