How do you implement a custom loss function in Keras?

A custom loss function can be implemented in Keras by creating a function that takes two arguments: y_true and y_pred. The function should return a single tensor value representing the loss.

The function should be written using TensorFlow operations so that it can be used in the Keras model. The function should also be written to accept the same arguments as the standard Keras losses, such as mean_squared_error or binary_crossentropy.

Once the function is written, it can be used in the model by passing it to the model.compile() method as the loss argument. For example:

model.compile(optimizer=’adam’, loss=custom_loss_function)

The custom loss function can also be used in the model.fit() method by passing it as the loss argument. For example:

model.fit(x_train, y_train, loss=custom_loss_function)

Finally, the custom loss function can be used in the model.evaluate() method by passing it as the loss argument. For example:

model.evaluate(x_test, y_test, loss=custom_loss_function)