The role of loss functions in neural networks and deep learning

The loss function in deep learning is used to evaluate the performance of the neural network model. In neural networks, there are two main mathematical operations namely forward propagation and gradient descent back propagation. Regardless of the operation, the goal of the neural network is to minimize the loss function. This is because minimizing the loss function automatically causes the neural network model to make more accurate predictions.

In the above, we have learned about two types of number operations of neural networks. Forward propagation refers to calculating the output given an input vector, while backpropagation and gradient descent are used to improve the weights and biases of the network to improve prediction accuracy. These two operations work together to allow the neural network to continuously optimize itself and make more accurate predictions.

Generally, neural networks solve tasks without being explicitly programmed or using specific rules. This is because they achieve a general goal by minimizing a loss function that does not depend on the specific task or environment.

Therefore, we need to have a deeper understanding of the loss function in order to correctly choose the appropriate loss function to solve various problems.

3 Main Types of Loss Functions in Neural Networks

• Mean Square Error Loss Function
• Cross Entropy Loss Function
• Mean Absolute Percent Error

1. Mean Squared Error Loss Function

The mean squared error (MSE) loss function is the sum of the squared differences between the entries in the predicted vector and the actual true value vector.

2. Cross-entropy loss function

Regression and classification are two popular areas in feedforward networks. In classification tasks, we need to deal with probabilistic predictions, which requires the output of the neural network to be in the range of 0 to 1. To measure the error between the predicted probability and the actual label, we use the cross-entropy loss function.

3. Mean Absolute Percent Error

Finally, let’s take a look at the Mean Absolute Percent Error (MAPE) loss function. This loss function has not received much attention in deep learning. In most cases, we use it to measure the performance of neural networks in demand forecasting tasks.

After you know the loss function, please remember the following key principles when using the loss function.

Principles for using loss functions

1. The loss function measures how well the neural network model performs a specific task. To make a neural network better, we must minimize the value of the loss function during the backpropagation step.

2. When using neural networks to predict probabilities, only use the cross-entropy loss function in classification tasks.

3. For regression tasks, when you want the network to predict continuous numbers, you must use the mean square error loss function.

4. We use the average absolute percentage error loss function during demand forecasting to focus on the performance of the network during training.

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