python2.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#add layer
def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    print('3--',Weights)
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)
    print('4--',biases)
    #y=Weights*inputs+biases
    y=tf.matmul(inputs,Weights)+biases
    print('5--',y)
    if activation_function is None:
        outputs=y
    else:
        outputs=activation_function(y)
    return outputs


x_data=np.linspace(-1,1,300)[:,np.newaxis]
print('1--',x_data)
noise =np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)-0.5+noise
print('2--',y_data)

xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])
#one layer
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
#two layer
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)


fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion()
plt.show()

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}))
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value=sess.run(prediction,feed_dict={xs:x_data})
        lines=ax.plot(x_data,prediction_value,'r-',lw=5)
        plt.pause(0.1)