first neural (tensorflow)

# -*- coding: utf-8 -*-
"""
Created on Tue Jul  9 21:55:06 2019

@author: txx
"""

import tensorflow as tf
import numpy as np


def add_layer(inputs,in_size, out_size, activation_function=None):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))

    biases=tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_plus_b=tf.matmul(inputs,Weights)+biases
    
    if activation_function is None:
        output=Wx_plus_b
    else: 	
        output=activation_function(Wx_plus_b)
    return output

x_data=np.linspace(-1,1,300)[:,np.newaxis]
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])
ys=tf.placeholder(tf.float32,[None,1])

l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)

loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))

train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)


init=tf.initialize_all_variables()

sess=tf.Session()
sess.run(init)

for i in range(1000):
	sess.run(train_step,feed_dict={xs: x_data,ys: y_data})

	if i % 50==0:
		print(sess.run(loss,feed_dict={xs: x_data,ys: y_data}))