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})