基於 TensorFlow 的第一個神經網路

#!/usr/bin/env python# -*- coding: utf-8 -*-import tensorflow as tffrom numpy.random import RandomStatebatch_size=8w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))x=tf.placeholder(tf.float32,shape=(None,2),name=x-input)y_=tf.placeholder(tf.float32,shape=(None,1),name="y-input")a=tf.matmul(x,w1)y=tf.matmul(a,w2)cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))train_step=tf.train.AdamOptimizer(0.001).minimize(cross_entropy)rdm=RandomState(1)dataset_size=128X=rdm.rand(dataset_size,2)print XY=[[int(x1+x2<1)] for (x1,x2) in X]print Ywith tf.Session() as sess: init_op=tf.initialize_all_variables() sess.run(init_op) print sess.run(w1) print sess.run(w2) STEPS=5000 for i in range(STEPS): start=(i*batch_size)%dataset_size end=min(start+batch_size,dataset_size) sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]}) if(i%1000==0): total_cross_entropy=sess.run(cross_entropy,feed_dict={x:X,y_:Y}) print("After %d training step,cross entrypy on all data is %g"%(i,total_cross_entropy)) print sess.run(w1) print sess.run(w2)

原文鏈接：基於 TensorFlow 的第一個神經網路

代碼參考《TensorFlow：實戰Google深度學習框架》，本地手打，調試後複製出來，和原文會有差別。