深度学习(2):4-2 Dropout 过拟合

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

#载入数据
mnist=input_data.read_data_sets("E:\MNIST_data",one_hot=True) 
#每个批次的大小
batch_size=100
#计算一共有多少个批次
n_batch=mnist.train.num_examples//batch_size


#定义两个placeholder
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
keep_prob=tf.placeholder(tf.float32)


#创建一个简单的神经网络
W1=tf.Variable(tf.truncated_normal([784,2000],stddev=0.1))
b1=tf.Variable(tf.zeros([2000])+0.1)
L1=tf.nn.tanh(tf.matmul(x,W1)+b1)
L1_drop=tf.nn.dropout(L1,keep_prob)
               
W2=tf.Variable(tf.truncated_normal([2000,2000],stddev=0.1))
b2=tf.Variable(tf.zeros([2000])+0.1)
L2=tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)
L2_drop=tf.nn.dropout(L2,keep_prob)
             
W3=tf.Variable(tf.truncated_normal([2000,1000],stddev=0.1))
b3=tf.Variable(tf.zeros([1000])+0.1)
L3=tf.nn.tanh(tf.matmul(L2_drop,W3)+b3)
L3_drop=tf.nn.dropout(L3,keep_prob)          
               
W4=tf.Variable(tf.truncated_normal([1000,10],stddev=0.1))
b4=tf.Variable(tf.zeros([10])+0.1)
prediction=tf.nn.softmax(tf.matmul(L3_drop,W4)+b4)


#定义二次代价函数
# loss=tf.reduce_mean(tf.square(y-prediction))
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
#使用梯度下降法
train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)


#初始化变量
init=tf.global_variables_initializer()


#结果存放在一个布尔型列表中
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#//返回同一维张量中最大的值所在的位子
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))


with tf.Session() as sess:
    sess.run(init)
    for epoch in range(31):
        for batch in range(n_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0})
            
        test_acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
        train_acc=sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels,keep_prob:1.0})
        print("Tter"+str(epoch)+",Testing Accuracy"+str(test_acc)+"training Accuracy"+str(train_acc))

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转载自blog.csdn.net/ninety_two/article/details/80465908
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