Logistic Regression

In this lab, you will cover logistic regression by using Pytorch.

Estimated Time Needed: 15 min

Import all the necessary modules:



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import torch.nn as nn
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt

Logistic Function



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torch.manual_seed(2)

Create a tensor ranging from -10 to 10:



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z=torch.arange(-10,10,0.1).view(-1, 1)

When you use sequential, you can create a sigmoid object:



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sig=nn.Sigmoid()

Apply the element-wise function Sigmoid with the object:



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yhat=sig(z)



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sig(torch.tensor(-1.0))

Plot the results:



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plt.plot(z.numpy(),yhat.numpy())
plt.xlabel('z')
plt.ylabel('yhat')

For custom modules, call the sigmoid from the torch (nn.functional for the old version), which applies the element-wise sigmoid from the function module and plots the results:



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yhat=torch.sigmoid(z)
plt.plot(z.numpy(),yhat.numpy())

w=torch.te

Tanh

When you use sequential, you can create a tanh object:



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TANH=nn.Tanh()

Call the object and plot it:



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yhat=TANH(z)
plt.plot(z.numpy(),yhat.numpy())

For custom modules, call the Tanh object from the torch (nn.functional for the old version), which applies the element-wise sigmoid from the function module and plots the results:



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yhat=torch.tanh(z)
plt.plot(z.numpy(),yhat.numpy())

Relu

When you use sequential, you can create a Relu object:



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RELU=nn.ReLU()
yhat=RELU(z)
plt.plot(z.numpy(),yhat.numpy())

For custom modules, call the relu object from the nn.functional, which applies the element-wise sigmoid from the function module and plots the results:



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yhat=F.relu(z)
plt.plot(z.numpy(),yhat.numpy())

Compare Activation Functions



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x=torch.arange(-2,2,0.1).view(-1, 1)
plt.plot(x.numpy(),F.relu(x).numpy(),label='relu')
plt.plot(x.numpy(),torch.sigmoid(x).numpy(),label='sigmoid')
plt.plot(x.numpy(),torch.tanh(x).numpy(),label='tanh')
plt.legend()

Practice

Compare the activation function with a tensor in the range (-1, 1)



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Double-click here for the solution.

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, Mavis Zhou