import tensorflow as tf
import numpy as np 
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

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:
		outputs = Wx_plus_b
	else:
		outputs = activation_function(Wx_plus_b)
	return outputs

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)



# the error between prediction and real data
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.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    # training
    sess.run(train_step, feed
    	