机器学习的“hello world”之mnist数据集

环境：ubuntu16.04+python3.6+spyder+tensorflow1.3.

代码亲测有效。tensorflow自带mnist数据集无需下载。

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf 
#读取数据 
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 
sess=tf.InteractiveSession() 
#构建cnn网络结构 #自定义卷积函数
def conv2d(x,w): 
  return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME')
#自定义池化函数 
def max_pool_2x2(x): 
  return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') 
#设置占位符，尺寸为样本输入和输出的尺寸 
x=tf.placeholder(tf.float32,[None,784]) 
y_=tf.placeholder(tf.float32,[None,10]) 
x_img=tf.reshape(x,[-1,28,28,1]) 
#设置第一个卷积层和池化层 
w_conv1=tf.Variable(tf.truncated_normal([3,3,1,32],stddev=0.1)) 
b_conv1=tf.Variable(tf.constant(0.1,shape=[32])) 
h_conv1=tf.nn.relu(conv2d(x_img,w_conv1)+b_conv1) 
h_pool1=max_pool_2x2(h_conv1) 
#设置第二个卷积层和池化层 
w_conv2=tf.Variable(tf.truncated_normal([3,3,32,50],stddev=0.1)) 
b_conv2=tf.Variable(tf.constant(0.1,shape=[50])) 
h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2) 
h_pool2=max_pool_2x2(h_conv2) 
#设置第一个全连接层 
w_fc1=tf.Variable(tf.truncated_normal([7*7*50,1024],stddev=0.1)) 
b_fc1=tf.Variable(tf.constant(0.1,shape=[1024])) 
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*50]) 
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1)+b_fc1) 
#dropout（随机权重失活） 
keep_prob=tf.placeholder(tf.float32) 
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) 
#设置第二个全连接层 
w_fc2=tf.Variable(tf.truncated_normal([1024,10],stddev=0.1)) 
b_fc2=tf.Variable(tf.constant(0.1,shape=[10])) 
y_out=tf.nn.softmax(tf.matmul(h_fc1_drop,w_fc2)+b_fc2) 
#建立loss function，采用交叉熵 
loss=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y_out),reduction_indices=[1])) 
#配置Adam优化器，学习速率为1e-4 
train_step=tf.train.AdamOptimizer(1e-4).minimize(loss) 
#建立正确率计算表达式 
correct_prediction=tf.equal(tf.argmax(y_out,1),tf.argmax(y_,1)) 
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) 
#开始喂数据，训练
tf.global_variables_initializer().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})
        print("step %d,train_accuracy= %g" %(i,train_accuracy))
        train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5}) 
      #训练完成之后，使用测试集进行测试，输出最终结果 
print ("test_accuracy= %g"%accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1}))

很快运行完20000次迭代后，得到结果，测试集伤准确率为test_accuracy= 0.8671。