数据可视化

传送门：https://www.bilibili.com/video/av16001891/?p=17

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs,input_size,output_size,activate_function=None):
    Weights=tf.Variable(tf.random_normal([input_size,output_size]))
    biases=tf.Variable(tf.zeros([1,output_size])+0.1)
    Wx_plus_b=tf.matmul(inputs,Weights)+biases
    if activate_function is None:
        out=Wx_plus_b
    else:
        out=activate_function(Wx_plus_b)
    return out
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,activate_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activate_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()#生成图片框
ax=fig.add_subplot(1,1,1)#长宽比例，所在位置
ax.scatter(x_data,y_data)
plt.ion()#连续画图，因为show()之后会停止程序所以要加
print("prediction",prediction)
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(prediction,feed_dict={xs:x_data,ys:y_data}))
        #print(x_data.shape,prediction.shape)
        try:
            ax.lines.remove(lines[0])
        except Exception:
           pass

        prediction_value=sess.run(prediction,feed_dict={xs:x_data,ys:y_data})
        lines=ax.plot(x_data,prediction_value,'r-',lw=5)
        plt.pause(0.2)
plt.ioff()
plt.show()