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When to use loss functions in a model?
Loss functions applied to the output of a model aren’t the only way to create losses. When writing the call method of a custom layer or a subclassed model, you may want to compute scalar quantities that you want to minimize during training (e.g. regularization losses). You can use the add_loss () layer method to keep track of such loss terms.
What’s the difference between objective function, cost function, loss function?
In machine learning, people talk about objective function, cost function, loss function. Are they just different names of the same thing? When to use them? If they are not always refer to the same thing, what are the differences?
How are loss and cost functions related in machine learning?
The loss function computes the error for a single training example, while the cost function is the average of the loss functions of the entire training set.
Loss functions and optimizers are both related to model accuracy, which is a key component of AI/ML governance. AI/ML governance is the overall process for how an organization controls access, implements policy, and tracks activity for models and their results.
Can a loss be passed to the compile function?
Any callable with the signature loss_fn (y_true, y_pred) that returns an array of losses (one of sample in the input batch) can be passed to compile () as a loss. Note that sample weighting is automatically supported for any such loss.
What’s the difference between a loss and a cost function?
A loss function is for a single training example. It is also sometimes called an error function. A cost function, on the other hand, is the average loss over the entire training dataset. The optimization strategies aim at minimizing the cost function.
How is the loss function used in optimal control?
In optimal control, the loss is the penalty for failing to achieve a desired value. In financial risk management, the function is mapped to a monetary loss. In classical statistics (both frequentist and Bayesian), a loss function is typically treated as something of a background mathematical convention.