tf.nn.dropout(x, keep_prob)
x：指輸入
keep_prob: 設定神經元被選中的概率

import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

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


input_num=784
hidden_num=300
w1=tf.
 
Variable(tf.truncated_normal([input_num,hidden_num], stddev=0.1))
b1=tf.Variable(tf.zeros([hidden_num]))
w2=tf.Variable(tf.zeros([hidden_num,10]))
b2=tf.Variable(tf.zeros([10]))
x=tf.placeholder(tf.float32, [None, input_num])
keep_prob = tf.placeholder(tf.float32)
hidden1=tf.nn.relu(tf.matmul(x,w1)+b1)
 

hidden1_drop=tf.nn.dropout(hidden1,keep_prob)
y=tf.nn.softmax(tf.matmul(hidden1_drop,w2)+b2)
y_=tf.placeholder(tf.float32, [None,10])
cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),
                                            reduction_indices=[1]))
train_step = tf.train.AdagradOptimizer(0.3
 
).minimize(cross_entropy)
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for i in range(3000):
        batch_xs, batch_ys=mnist.train.next_batch(100)
        _,loss=sess.run([train_step,cross_entropy],feed_dict=
                        {x:batch_xs,y_:batch_ys,keep_prob:0.75})

        if i%100==0: print('Loss:',loss)
    correct_pre=tf.equal(tf.argmax(y_,1),tf.argmax(y,1))
    accuracy=tf.reduce_mean(tf.cast(correct_pre,tf.float32))
    print('accuracy:',accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,
                                     keep_prob:1.0}))