In this lab, we will review how to make a prediction for Linear Regression with Multiple Output.
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from torch import nn
import torch
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Set the random seed:
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torch.manual_seed(1)
Set the random seed:
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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 yhat
create a linear regression object, as our input and output will be two we set the parameters accordingly
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model=linear_regression(2,2)
we can use the diagram to represent the model or object
we can see the parameters
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list(model.parameters())
we can create a tensor with two rows representing one sample of data
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x=torch.tensor([[1.0,3.0]])
we can make a prediction
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yhat=model(x)
yhat
each row in the following tensor represents a different sample
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X=torch.tensor([[1.0,1.0],[1.0,2.0],[1.0,3.0]])
we can make a prediction using multiple samples
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Yhat=model(X)
Yhat
the following figure represents the operation, where the red and blue represents the different parameters, and the different shades of green represent different samples.
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. Joseph has been working for IBM since he completed his PhD.
Other contributors: Michelle Carey
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