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Training Multiple Output Linear Regression
Linear Regression Multiple Outputs
Objective
- How to create a complicated models using pytorch build in functions.
Table of Contents
In this lab, you will create a model the Pytroch way. This will help you as models get more complicated.Estimated Time Needed: 20 min
Import the following libraries:
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch import nn,optim
from mpl_toolkits.mplot3d import Axes3D
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transformsSet the random seed:
torch.manual_seed(1)Make Some Data
Create a dataset class with two-dimensional features and two targets:from torch.utils.data import Dataset, DataLoader
class Data(Dataset):
def __init__(self):
self.x=torch.zeros(20,2)
self.x[:,0]=torch.arange(-1,1,0.1)
self.x[:,1]=torch.arange(-1,1,0.1)
self.w=torch.tensor([ [1.0,-1.0],[1.0,3.0]])
self.b=torch.tensor([[1.0,-1.0]])
self.f=torch.mm(self.x,self.w)+self.b
self.y=self.f+0.001*torch.randn((self.x.shape[0],1))
self.len=self.x.shape[0]
def __getitem__(self,index):
return self.x[index],self.y[index]
def __len__(self):
return self.lencreate a dataset object
data_set=Data()Create the Model, Optimizer, and Total Loss Function (cost)
Create a custom module:
class linear_regression(nn.Module):
def __init__(self,input_size,output_size):
super(linear_regression,self).__init__()
self.linear=nn.Linear(input_size,output_size)
def forward(self,x):
yhat=self.linear(x)
return yhatCreate an optimizer object and set the learning rate to 0.1. Don't forget to enter the model parameters in the constructor.
model=linear_regression(2,2)Create an optimizer object and set the learning rate to 0.1. Don't forget to enter the model parameters in the constructor.
optimizer = optim.SGD(model.parameters(), lr = 0.1)Create the criterion function that calculates the total loss or cost:
criterion = nn.MSELoss()Create a data loader object and set the batch_size to 5:
train_loader=DataLoader(dataset=data_set,batch_size=5)Train the Model via Mini-Batch Gradient Descent
Run 100 epochs of Mini-Batch Gradient Descent and store the total loss or cost for every iteration. Remember that this is an approximation of the true total loss or cost.
LOSS=[]
epochs=100
for epoch in range(epochs):
for x,y in train_loader:
#make a prediction
yhat=model(x)
#calculate the loss
loss=criterion(yhat,y)
#store loss/cost
LOSS.append(loss.item())
#clear gradient
optimizer.zero_grad()
#Backward pass: compute gradient of the loss with respect to all the learnable parameters
loss.backward()
#the step function on an Optimizer makes an update to its parameters
optimizer.step()Plot the cost:
plt.plot(LOSS)
plt.xlabel("iterations ")
plt.ylabel("Cost/total loss ")
plt.show()
About the Authors:
Joseph Santarcangelo has a PhD in Electrical Engineering. His research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition.
Other contributors: Michelle Carey