基于tensorflow的简单BP神经网络的结构搭建

tensorflow的构建封装较为完善，可以任意加入中间层，只要注意好维度即可，不过numpy版的神经网络代码经过适当地改动也可以做到这一点，这里最重要的思想就是层的模型的分离。
原文

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})