tensorflow构建BP神经网络的方法

import tensorflow as tf  import numpy as np   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   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   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])  l1 = addLayer(xs,1,10,activity_function=tf.nn.relu) # relu是激励函数的一种 l2 = addLayer(l1,10,1,activity_function=None) 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  train = tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 选择梯度下降法  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})