当前位置:网站首页>1. Linear regression
1. Linear regression
2022-07-08 01:02:00 【booze-J】
The code running platform is jupyter-notebook, Code blocks in the article , According to jupyter-notebook Written in the order of division in , Run article code , Glue directly into jupyter-notebook that will do .
1. Import third-party library
import keras
import numpy as np
import matplotlib.pyplot as plt
# Sequential Sequential model
from keras.models import Sequential
# Dense Fully connected layer
from keras.layers import Dense
2. Randomly generate data sets
# Use numpy Generate 100 A random point
x_data = np.random.rand(100)
# Noise shape and x_data The shape of is the same
noise = np.random.normal(0,0.01,x_data.shape)
# Set up w=0.1 b=0.2
y_data = x_data*0.1+0.2+noise
# y_data_no_noisy = x_data*0.1+0.2
# Show random points
plt.scatter(x_data,y_data)
# plt.scatter(x_data,y_data_no_noisy)
Running effect :
This is the case of adding noise y_data = x_data*0.1+0.2+noise
:
Without adding noise y_data_no_noisy = x_data*0.1+0.2
(w=0.1,b=0.2):
Linear regression is based on the scatter plot with added noise , Fit a straight line that is similar to the scatter diagram without adding noise .
3. Linear regression
# Build a sequential model
model = Sequential()
# Add a full connection layer to the model stay jupyter-notebook in , Press shift+tab Parameters can be displayed
model.add(Dense(units=1,input_dim=1))
# sgd:Stochastic gradient descent , Random gradient descent method
# mse:Mean Squared Error , Mean square error
model.compile(optimizer='sgd',loss='mse')
# Training 3001 Lots
for step in range(3001):
# One batch at a time The loss of
cost = model.train_on_batch(x_data,y_data)
# Every time 500 individual batch Print once cost
if step%500==0:
print("cost:",cost)
# Print weights and batch values
W,b = model.layers[0].get_weights()
print("W:",W)
print("b:",b)
# x_data Input the predicted value in the network
y_pred = model.predict(x_data)
# Show random points
plt.scatter(x_data,y_data)
# Show forecast results
plt.plot(x_data,y_pred,"r-",lw=3)
plt.show()
Running effect :
You can see the prediction w and b Are very close to what we set w and b.
Be careful
- stay jupyter-notebook in , Press shift+tab Parameters can be displayed
- train_on_batch Use
- compile Use
边栏推荐
- STL--String类的常用功能复写
- 7. Regularization application
- 130. 被围绕的区域
- ABAP ALV LVC template
- 串口接收一包数据
- Where is the big data open source project, one-stop fully automated full life cycle operation and maintenance steward Chengying (background)?
- Mathematical modeling -- knowledge map
- Leetcode brush questions
- SDNU_ ACM_ ICPC_ 2022_ Summer_ Practice(1~2)
- Lecture 1: the entry node of the link in the linked list
猜你喜欢
[OBS] the official configuration is use_ GPU_ Priority effect is true
利用GPU训练网络模型
新库上线 | CnOpenData中国星级酒店数据
From starfish OS' continued deflationary consumption of SFO, the value of SFO in the long run
12.RNN应用于手写数字识别
y59.第三章 Kubernetes从入门到精通 -- 持续集成与部署(三二)
AI遮天传 ML-初识决策树
6.Dropout应用
Analysis of 8 classic C language pointer written test questions
Course of causality, taught by Jonas Peters, University of Copenhagen
随机推荐
1.线性回归
tourist的NTT模板
Basic mode of service mesh
13. Enregistrement et chargement des modèles
【GO记录】从零开始GO语言——用GO语言做一个示波器(一)GO语言基础
Marubeni official website applet configuration tutorial is coming (with detailed steps)
Implementation of adjacency table of SQLite database storage directory structure 2-construction of directory tree
130. 被圍繞的區域
德总理称乌不会获得“北约式”安全保障
【愚公系列】2022年7月 Go教学课程 006-自动推导类型和输入输出
新库上线 | CnOpenData中国星级酒店数据
基础篇——整合第三方技术
Huawei switch s5735s-l24t4s-qa2 cannot be remotely accessed by telnet
AI遮天传 ML-初识决策树
[note] common combined filter circuit
Where is the big data open source project, one-stop fully automated full life cycle operation and maintenance steward Chengying (background)?
股票开户免费办理佣金最低的券商,手机上开户安全吗
【笔记】常见组合滤波电路
Service mesh introduction, istio overview
Malware detection method based on convolutional neural network