Loss Function - ( Linear Regression with PyTorch )

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Syntax

import torch.nn as nn
loss_function = nn.MSELoss()

Example

import torch
import torch.nn as nn

# Dummy data
X = torch.tensor([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], requires_grad=True)
y = torch.tensor([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], requires_grad=True)

# Linear Regression Model
model = nn.Linear(1, 1)

# Loss Function
loss_function = nn.MSELoss()

# Forward Pass
y_pred = model(X)

# Calculate Loss
loss = loss_function(y_pred, y)

# Print Loss
print(f"Loss: {loss.item()}")

Output

Loss: 14.821429252624512

Explanation

In Machine Learning, a loss function measures how well the model is performing. During training, the goal is to minimize this loss function.

MSE (Mean Squared Error) loss function is a popular choice for Regression problems. It measures the average squared difference between the predicted and actual values.

In PyTorch, the nn.MSELoss() function can be used to define the MSE loss function.

Use

Loss functions are an essential component of the training process in Machine Learning. They are used to evaluate the performance of a model and update the parameters to minimize the loss.

In Linear Regression with PyTorch, the MSE loss function can be used to calculate the difference between the predicted and actual values.

Important Points

• A loss function measures how well the model is performing
• MSE loss is a popular choice for Regression problems
• nn.MSELoss() can be used to define the MSE loss function in PyTorch
• During training, the goal is to minimize the loss function to improve the model's performance

Summary

In conclusion, the Loss Function plays a crucial role in evaluating the performance of a Machine Learning model. The MSE loss function is commonly used in Regression problems to measure the average squared difference between the predicted and actual values. In PyTorch, the nn.MSELoss() function can be used to define the MSE loss function. The goal of training a model is to minimize the loss function to improve the model's performance.