INTRODUCE
It can be difficult to identify which loss function in deep learning or what role it plays in neural network training.
This post explains the relevance of loss and loss function in deep learning neural network training and how to choose the appropriate loss function for predictive modelling.
Neural networks are trained with a loss function in deep learning to calculate model error.
Maximum Likelihood helps pick a loss function in deep learning for neural networks and machine learning models.
Cross-entropy and mean squared error are used to train neural network models.
Better Deep Learning provides Python source code files and step-by-step explanations.
Neural network optimization
What’s the difference between a loss function in deep learning and Loss?
Probability
Cross-entropy and ML
What deep learning loss function to use?
loss function in deep learning
Model Performance and Loss
We’ll discuss loss function in deep learning theory.
For help selecting and implementing loss functions, see this post.
Neural network optimization
Deep learning neural networks map inputs to outputs using training data.
Too many unknowns exist to calculate neural network weights. Instead, the learning problem is framed as a search or optimization problem, and an algorithm is employed to navigate the universe of weight values the model may utilise to make accurate predictions.
A neural network model is trained via stochastic gradient descent and backpropagation of error.
Erroneous gradients are called “gradient decline.” The model predicts using a given set of weights, and its error is computed.
Gradient descent tries to change weights such the next assessment minimises error, meaning the optimization method is going down the error gradient (or slope).
Now that we know neural network training addresses optimization issues, we can calculate the error of a set of weights.
loss function in deep learning vs. loss
In optimization, the objective function evaluates a potential solution (a set of weights).
We may try to maximise or reduce the goal function, seeking for the highest or lowest score.
Neural networks reduce error. The objective function is sometimes called a cost function or a loss function in deep learning and the value it computes is called “loss” in deep learning.
We want to minimise or maximise the goal function. When minimized, it’s called the cost, loss, or error function.
The cost or loss function in deep learning must distil all model characteristics into a single number, with increases indicating a better model.
The cost function reduces positive and negative system elements to a single number, allowing candidate solutions to be rated and compared.
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, 1999, page 155.
Deep learning’s optimization phase requires a loss function to calculate model error.
This can be a difficult job because the function must embody the properties of the problem and be motivated by project and stakeholder concerns.
The function must accurately reflect our design goals. If we apply a poor error function and get disappointing results, it is our fault for not adequately stating the search goal.
Page 155, Supervised Learning in Feedforward Artificial Neural Networks, Neural Smithing, 1999.
We need to know which functions to exploit now that we are familiar with the loss function in deep learning and loss.
Wha tloss function in deep learning Should I Use?
We may condense the preceding portion and quickly offer the loss functions that you should utilise within a maximum likelihood framework.
Importantly, the loss function you choose is intricately related to the activation function you choose in your neural network’s output layer. These two design aspects are tied together.
Consider the output layer configuration to be a choice concerning the framing of your prediction problem, and the loss function selection to be the mechanism for calculating the error for a given framing of your problem.
