简单的分类任务:
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/5/14 17:20 # @Author : HJH # @Site : # @File : classification.py # @Software: PyCharm #利用独热编码one hot来分类手写数字 import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data def add_layer(input,in_size,out_size,activation_function=None): 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 activation_function is None: outputs=Wx_plus_b else: outputs=activation_function(Wx_plus_b,) return outputs def compute_accuracy(v_xs,v_ys): global prediction y_pre=sess.run(prediction,feed_dict={xs:v_xs}) #在独热编码中哪个位置的概率最高即为哪个数 correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1)) #判断这个手写数字的准确率 accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys}) return result if __name__=='__main__': # 若没有该数据,则从网上下载该数据 mnist = input_data.read_data_sets('MNIST_data', one_hot=True) xs=tf.placeholder(tf.float32,[None,784])#28*28 ys=tf.placeholder(tf.float32,[None,10]) prediction=add_layer(xs,784,10,activation_function=tf.nn.softmax) cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])) train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess=tf.Session() sess.run(tf.global_variables_initializer()) for i in range(1000): batch_xs,batch_ys=mnist.train.next_batch(100) sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys}) if i%50==0: print(compute_accuracy(mnist.test.images,mnist.test.labels))
使用dropout处理overfitting:
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/5/14 20:20 # @Author : HJH # @Site : # @File : overfitting.py # @Software: PyCharm import tensorflow as tf from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer def add_layer(input,in_size,out_size,layer_name,activation_function=None): global keep_prob 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 #克服过拟合 Wx_plus_b=tf.nn.dropout(Wx_plus_b,keep_prob) if activation_function==None: outputs=Wx_plus_b else: outputs=activation_function(Wx_plus_b) tf.summary.histogram(layer_name+'/outputs',outputs) return outputs if __name__=='__main__': 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=3) keep_prob=tf.placeholder(tf.float32) xs=tf.placeholder(tf.float32,[None,64]) ys=tf.placeholder(tf.float32,[None,10]) l1=add_layer(xs,64,50,'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])) tf.summary.scalar('loss',cross_entropy) train_step=tf.train.GradientDescentOptimizer(0.6).minimize(cross_entropy) sess=tf.Session() merged=tf.summary.merge_all() train_writer=tf.summary.FileWriter('logs/train',sess.graph) test_wirter=tf.summary.FileWriter('logs/test',sess.graph) sess.run(tf.global_variables_initializer()) for i in range(1000): #保持60%的数据不被drop掉的 sess.run(train_step,feed_dict={xs:X_train,ys:y_train,keep_prob:0.5}) if i%50==0: #记录的时候不需要drop掉 train_result=sess.run(merged,feed_dict={xs:X_train,ys:y_train,keep_prob:1}) test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test,keep_prob:1}) train_writer.add_summary(train_result,i) test_wirter.add_summary(test_result,i)