BP_tensor

import tensorflow as tf   
import numpy as np  


#添加层
def addLayer(inputData,inSize,outSize,activity_function = None):  
    Weights = tf.Variable(tf.random_normal([inSize,outSize]))   
    basis = tf.Variable(tf.zeros([1,outSize])+0.1)    
    weights_plus_b = tf.matmul(inputData,Weights)+basis  
    if activity_function is None:  
        ans = weights_plus_b  
    else:  
        ans = activity_function(weights_plus_b)  
    return ans  
  
#训练的数据
x_data = np.linspace(-1,1,300)[:,np.newaxis] # 转为列向量  
noise = np.random.normal(0,0.05,x_data.shape)  
y_data = np.square(x_data)+0.5+noise  
  
#定义节点准备接受数据  
xs = tf.placeholder(tf.float32,[None,1]) # 样本数未知，特征数为1，占位符最后要以字典形式在运行中填入  
ys = tf.placeholder(tf.float32,[None,1])  

#定义神经层：隐藏层和预测层
l1 = addLayer(xs,1,10,activity_function=tf.nn.relu) #relu作为激活函数
l2 = addLayer(l1,10,1,activity_function=None)  

#定义 loss 表达式
loss = tf.reduce_mean(tf.reduce_sum(tf.square((ys-l2)),reduction_indices = [1]))#需要向相加索引号，redeuc执行跨纬度操作  
#优化loss  
train =  tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 选择梯度下降法  

#开始训练
init = tf.initialize_all_variables()  
sess = tf.Session()  
sess.run(init)  
  
for i in range(10000):  
    sess.run(train,feed_dict={xs:x_data,ys:y_data})  
    if i%50 == 0:  
        print sess.run(loss,feed_dict={xs:x_data,ys:y_data}) flowflow