dropout.py

import tensorflow as tf
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits

digits = load_digits()
X = digits.data
Y = digits.target
Y = LabelBinarizer().fit_transform(Y)
print(Y.shape)
trainx,testx,trainy,testy = train_test_split(X,Y,test_size=0.3)


def add_layer(input,input_size,output_size,layer_name,activation_function=None):
    Weights = tf.Variable(tf.random_normal([input_size,output_size]))
    biases = tf.Variable(tf.zeros([1,output_size])+0,1)
    Wx_plus_bias = tf.add(tf.matmul(input,Weights),biases)
    Wx_plus_bias = tf.nn.dropout(Wx_plus_bias,keep_prob)
    if activation_function == None:
        outputs = Wx_plus_bias
    else:
        outputs = activation_function(Wx_plus_bias)
    #这里的output是一个二维的，所以每一步对应一个线（或者说小的矩形，颜色越深的地方表示这个地方的数越多，可以认为纵向上表示train到这一步的时候的一个数据分布
    tf.summary.histogram(layer_name+'/outputs',outputs)
    return outputs

xs = tf.placeholder(tf.float32,[None,64])
ys = tf.placeholder(tf.float32,[None,10])
keep_prob = tf.placeholder(tf.float32)

l1 = add_layer(xs,64,50,"l1",activation_function=tf.nn.tanh)
prediction = add_layer(l1,50,10,"l2",activation_function=tf.nn.softmax)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1]))
#因为cross_entropy是一个标量，所以定义tf.summary.scalar
tf.summary.scalar("loss",cross_entropy)

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    #合并所有的summary
    merged = tf.summary.merge_all()
    #得到summary的FileWriter
    train_writer = tf.summary.FileWriter('logs/train/',sess.graph)
    test_writer = tf.summary.FileWriter('logs/test/',sess.graph)
    sess.run(init)
    for i in range(1000):
        sess.run(train_step,feed_dict={xs:trainx,ys:trainy,keep_prob:0.5})
        if i % 50 == 0:
            #print(sess.run(cross_entropy,feed_dict={xs:trainx,ys:trainy}))
            #这里要运行merged
            train_loss = sess.run(merged,feed_dict = {xs:trainx,ys:trainy,keep_prob:0.5})
            test_loss = sess.run(merged,feed_dict={xs:testx,ys:testy,keep_prob:0.5})
            #将loss写入FileWriter中
            train_writer.add_summary(train_loss,i)
            test_writer.add_summary(test_loss,i)