总的来说，思路较为清晰，关键搞清卷积过程以及过程中张量维度的变化，还需注意求正确率的方法——利用平均值求解
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
#随机化权值变量tensor，高斯分布
def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.1)
return tf.Variable(initial)
#随机化偏置，高斯分布
def bias_variable(shape):
initial=tf.constant(0.1,shape=shape)
return tf.Variable(initial)
#定义二维图像卷积
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')
###start here!###
sess=tf.InteractiveSession()
mnist=input_data.read_data_sets('MNIST_data',one_hot=True)
#接收mnist中真实数据
x=tf.placeholder("float",shape=[None,784])
y_=tf.placeholder("float",shape=[None,10])
#layer 1: convolution + relu + max pooling
W_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])
x_image=tf.reshape(x,[-1,28,28,1])#[batch, height, width, channels]
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h_pool1=max_pool_2x2(h_conv1)
#layer 2: convolution + relu + max pooling
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])
#第三层 全连接层
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)
#dropout层
keep_prob=tf.placeholder("float")
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
#全连接层
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])
#softmax 判定层
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
cross_entropy= -tf.reduce_sum(y_*tf.log(y_conv))#交叉熵cost计算方法
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)#ada优化
correct_prediction=tf.equal(tf.arg_max(y_conv,1),tf.arg_max(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
sess.run(tf.global_variables_initializer())
for i inrange(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}))