The `ignite.metrics.Loss` module in Python Ignite provides a collection of loss functions commonly used in machine learning tasks. These loss functions measure the dissimilarity between predicted and target values, quantifying the quality of a model's predictions. They are widely utilized in various optimization algorithms for training models, such as gradient descent. The `Loss` module offers different loss functions such as mean squared error, binary cross entropy, and softmax cross entropy, allowing users to effortlessly assess and optimize their models' performance.
Python Loss - 30 examples found. These are the top rated real world Python examples of ignite.metrics.Loss extracted from open source projects. You can rate examples to help us improve the quality of examples.