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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
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