# -*- coding: utf-8 -*-
"""
Created on Mon Aug 20 16:51:18 2018

@author: Wf
"""

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
    layer_name='layer%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope('Weight'):
            Weights=tf.Variable(tf.random_normal([in_size,out_size]),name='W')
            tf.summary.histogram(layer_name+'/weights',Weights)
            
        with tf.name_scope('biases'):
            biases=tf.Variable(tf.zeros([1,out_size])+0.1)
            tf.summary.histogram(layer_name+'/biases',biases)
            
        with tf.name_scope('biases'):
            Wx_plus_b=tf.matmul(inputs,Weights)+biases
            
    if activation_function is None:
        outputs=Wx_plus_b
        tf.summary.histogram(layer_name+'/outputs',outputs)
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs


#Make up some real data
    
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

#define placeholder for inputs to network
with tf.name_scope('input'):
    xs=tf.placeholder(tf.float32,[None,1])
    ys=tf.placeholder(tf.float32,[None,1])

#make layer
l1=add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,n_layer=2,activation_function=None) 
 

#loss function
with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),
                                  reduction_indices=[1]))
    tf.summary.scalar('loss',loss)
 
#train contents
with tf.name_scope('train'):
    train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

#initiable variables
init=tf.global_variables_initializer()
sess=tf.Session()
merged=tf.summary.merge_all()
writer=tf.summary.FileWriter("logs/",sess.graph)

sess.run(init)

#show result
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:       
#         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(1)    
#         print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
# =============================================================================
    if i%50==0:
        result=sess.run(merged,feed_dict={xs:x_data,ys:y_data})
        writer.add_summary(result,i)