tensorflow dropout函數應用

1、dropout

dropout 是指在深度學習網絡的訓練過程中，按照一定的概率將一部分神經網絡單元暫時從網絡中丟棄，相當於從原始的網絡中找到一個更瘦的網絡，這篇博客中講的非常詳細

 

2、tensorflow實現

 

用dropout：

import tensorflow as tf
import numpy as np

x_data=np.linspace(-1.,1.,300)[:, np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)+noise

def add_layer(input,in_size,out_size,activation_function=None,dropout_function=False):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_plus_b=tf.matmul(input,Weights)+biases

    if dropout_function==True:
        Wx_plus_b=tf.nn.dropout(Wx_plus_b,keep_prob=0.5)
    else:
        pass

    if activation_function is None:
        outputs=Wx_plus_b
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs

xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

layer1=add_layer(xs,1,10,activation_function=tf.nn.relu,dropout_function=True)
predict=add_layer(layer1,10,1)

loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-predict),reduction_indices=[1]))
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)

for i in range(100):
    sess.run(train_step,feed_dict={xs: x_data,ys: y_data})
    if i%5==0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

輸出結果：

1.87953
0.294975
0.173229
0.122831
0.0859246
0.0712419
0.0839021
0.0572673
0.0676766
0.0584741
0.0556422
0.0576011
0.0534098
0.0472549
0.0528496
0.0427771
0.0485248
0.0502765
0.0454531
0.0516633

 

不用dropout：

import tensorflow as tf
import numpy as np

x_data=np.linspace(-1.,1.,300)[:, np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)+noise

def add_layer(input,in_size,out_size,activation_function=None,dropout_function=False):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_plus_b=tf.matmul(input,Weights)+biases

    if dropout_function==True:
        Wx_plus_b=tf.nn.dropout(Wx_plus_b,keep_prob=1)
    else:
        pass

    if activation_function is None:
        outputs=Wx_plus_b
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs

xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

layer1=add_layer(xs,1,10,activation_function=tf.nn.relu)
predict=add_layer(layer1,10,1)

loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-predict),reduction_indices=[1]))
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)

for i in range(100):
    sess.run(train_step,feed_dict={xs: x_data,ys: y_data})
    if i%5==0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

輸出結果：

0.256632
0.0532672
0.0364148
0.0264385
0.0202409
0.0164314
0.0141813
0.0129079
0.0121878
0.0117478
0.0114399
0.0111906
0.0109715
0.0107702
0.0105785
0.010393
0.0102144
0.0100426
0.00987562
0.00971407

 

這里舉例 用過dropout后，loss變得更大，是因為我這里所用數據較少，用更多的訓練集去測試，dropout會變現的很好！