# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs,in_size,out_size,activation_function=None): #先不定义激励函数
    #初始权重为随机变量要比全零要好;矩阵一般开头大写，后面的向量biase一般小写
    Weight = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size])+0.1)
    #未激活的预测值
    Wx_plus_b = tf.matmul(inputs,Weight)+biases
    if activation_function is None:
        outputs=Wx_plus_b
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs

#linspace(x1,x2,N) 用于产生x1,x2之间的N点行线性的矢量。
#其中x1、x2、N分别为起始值、终止值、元素个数
x_data = np.linspace(-1,1,300)[:,np.newaxis]
#numpy.random.normal(loc=0.0, scale=1.0, size=None),均值，标准差，形状
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data) - 0.5 + noise
#定义传入
xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])
#定义输入层1个，隐藏层10个，输出1个神经元的神经网络
l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
predition = add_layer(l1,10,1,activation_function=None)
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

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

#画图
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(predition,feed_dict={xs:x_data})
        lines = ax.plot(x_data,prediction_value,'r-',lw=5) #lw=lineweith
        plt.pause(0.1)

