What is a loss function?
In PyTorch, a loss function is a function used to measure the prediction error of a model on a training dataset. It can be used to guide the update of model parameters, making the model perform better on the training data. The loss function is usually used in conjunction with the last layer of the network to compute the impact of each parameter on the loss through automatic differentiation, guiding parameter optimization.
Commonly used loss functions in PyTorch include:
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MSELoss - Mean Squared Error loss, used for regression problems.
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CrossEntropyLoss - Cross-entropy loss, used for classification problems, combining softmax activation and negative log-likelihood loss.
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NLLLoss - Negative Log Likelihood loss, used for multi-classification problems.
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BCELoss - Binary Cross-Entropy loss, used for binary classification problems.
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L1Loss - L1 norm loss, making the model more robust to outliers. Used for linear regression, logistic regression.
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SmoothL1Loss - Smooth L1 loss, combining the advantages of mean squared error loss and L1 loss. Used for object detection.
What are regression problems, classification problems, multi-classification problems, and binary classification problems?
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Regression problems: The goal of regression problems is to predict continuous numerical target variables, with the prediction result being a continuous value. Examples include house price prediction, sales prediction, etc. For example, the loss function used in the previous stock price prediction is MSELoss.
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Classification problems: The goal of classification problems is to predict discrete class labels, with the output being a single category. Examples include image classification (cat or dog), spam email classification.
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Multi-classification problems: The goal of multi-classification problems is to classify samples into multiple categories, with the prediction result being one of the multiple categories. The number of classification categories is greater than 2. Examples include handwritten digit recognition (0~9 categories).
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Binary classification problems: The goal of binary classification problems is to classify samples into two categories, with only two categories. Examples include yes or no classification, spam email detection (spam or non-spam).
Using loss functions
Using loss functions in PyTorch is very simple. You can instantiate a loss function object as follows:
loss_fn = nn.MSELoss()
loss = loss_fn(prediction, target)
Choosing the appropriate loss function has a significant impact on model performance and training speed. In practice, you can test different loss functions and choose the one that performs best in terms of validation metrics.