matplotlib
看了莫烦python 例3matplotlib可视化
解决红色动态迭代线不显示的问题
加上下面2句（方法来自bilibili）
import matplotlib
matplotlib.use(‘TkAgg’)
import tensorflow as tf
import matplotlib
matplotlib.use(‘TkAgg’)
import numpy as np
import matplotlib.pyplot as plt
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) #mean steddev
y_data=np.square(x_data)-0.5+noise
xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1]) #表示有1列，行不定
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)
loss=tf.reduce_mean(tf.reduce_sum(tf.square(prediction-ys),reduction_indices=[1])) #缩减维度
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()
for i in range(1000):
sess.run(train_step,feed_dict={xs:x_data,ys:y_data}) #not good ,because use all sample
if i%50==0:
# print(i,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(1)
prediction_value = sess.run(prediction, feed_dict={xs: x_data})
# 绘制预测数据
lines = ax.plot(x_data, prediction_value, ‘r-’, lw=5) # x轴数据，y轴数据，红色的线，线的宽度为5
plt.pause(0.1) # 绘制曲线的时间间隔为0.1秒
plt.show()