python人工智能tensorflow函数tf.nn.dropout使用方法

def dropout(x, keep_prob, noise_shape=None, seed=None, name=None)

def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):
    layer_name = 'layer%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope("Weights"):
            Weights = tf.Variable(tf.random_nORMal([in_size,out_size]),name = "Weights")
            tf.summary.histogram(layer_name+"/weights",Weights)
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")
            tf.summary.histogram(layer_name+"/biases",biases)
        with tf.name_scope("Wx_plus_b"):
            Wx_plus_b = tf.matmul(inputs,Weights) + biases
            #dropout一般加载每个神经层的输出
            Wx_plus_b = tf.nn.dropout(Wx_plus_b,keep_prob)
            #看这里看这里，dropout在这里。
            tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)
        if activation_function == None :
            outputs = Wx_plus_b 
        else:
            outputs = activation_function(Wx_plus_b)
        tf.summary.histogram(layer_name+"/outputs",outputs)
        return outputs

import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
digits = load_digits()
X = digits.data
y = digits.target
y = LabelBinarizer().fit_transform(y)
X_train,X_test,Y_train,Y_test = train_test_split(X,y,test_size = 500)
def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):
    layer_name = 'layer%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope("Weights"):
            Weights = tf.Variable(tf.random_normal([in_size,out_size]),name = "Weights")
            tf.summary.histogram(layer_name+"/weights",Weights)
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")
            tf.summary.histogram(layer_name+"/biases",biases)
        with tf.name_scope("Wx_plus_b"):
            Wx_plus_b = tf.matmul(inputs,Weights) + biases
            Wx_plus_b = tf.nn.dropout(Wx_plus_b,keep_prob)
            tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)
        if activation_function == None :
            outputs = Wx_plus_b 
        else:
            outputs = activation_function(Wx_plus_b)
        tf.summary.histogram(layer_name+"/outputs",outputs)
        return outputs
def compute_accuracy(x_data,y_data,prob = 1):
    global prediction
    y_pre = sess.run(prediction,feed_dict = {xs:x_data,keep_prob:prob})
    correct_prediction = tf.equal(tf.arg_max(y_data,1),tf.arg_max(y_pre,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict = {xs:x_data,ys:y_data,keep_prob:prob})
    return result
keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32,[None,64])
ys = tf.placeholder(tf.float32,[None,10])
l1 = add_layer(xs,64,100,'l1',activation_function=tf.nn.tanh, keep_prob = keep_prob)
l2 = add_layer(l1,100,100,'l2',activation_function=tf.nn.tanh, keep_prob = keep_prob)
prediction = add_layer(l1,100,10,'l3',activation_function = tf.nn.softmax, keep_prob = 1)
with tf.name_scope("loss"):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=ys,logits = prediction),name = 'loss')
    tf.summary.Scalar("loss",loss)
train = tf.train.AdamOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
merged = tf.summary.merge_all()
with tf.Session() as sess:
    sess.run(init)
    train_writer =  tf.summary.FileWriter("logs/strain",sess.graph)
    test_writer = tf.summary.FileWriter("logs/test",sess.graph)
    for i in range(5001):
        sess.run(train,feed_dict = {xs:X_train,ys:Y_train,keep_prob:0.5})
        if i % 500 == 0:
            print("训练%d次的识别率为：%f。"%((i+1),compute_accuracy(X_test,Y_test,prob=0.5)))
            train_result = sess.run(merged,feed_dict={xs:X_train,ys:Y_train,keep_prob:0.5})
            test_result = sess.run(merged,feed_dict={xs:X_test,ys:Y_test,keep_prob:0.5})
            train_writer.add_summary(train_result,i)
            test_writer.add_summary(test_result,i)