compute_loss

FullyConnected.compute_loss(x=None, y=None, y_pred=None, sample_weight=None, allow_empty=False)

Compute the total loss, validate it, and return it.

Subclasses can optionally override this method to provide custom loss computation logic.

Example:

```python class MyModel(Model):

def __init__(self, *args, **kwargs):

super().__init__(*args, **kwargs) self.loss_tracker = metrics.Mean(name=’loss’)

def compute_loss(self, x, y, y_pred, sample_weight):

loss = ops.means((y_pred - y) ** 2) loss += ops.sum(self.losses) self.loss_tracker.update_state(loss) return loss

def reset_metrics(self):

self.loss_tracker.reset_state()

@property def metrics(self):

return [self.loss_tracker]

inputs = layers.Input(shape=(10,), name=’my_input’) outputs = layers.Dense(10)(inputs) model = MyModel(inputs, outputs) model.add_loss(ops.sum(outputs))

optimizer = SGD() model.compile(optimizer, loss=’mse’, steps_per_execution=10) dataset = … model.fit(dataset, epochs=2, steps_per_epoch=10) print(f”Custom loss: {model.loss_tracker.result()}”) ```

Parameters:

• x – Input data.

• y – Target data.

• y_pred – Predictions returned by the model (output of model(x))

• sample_weight – Sample weights for weighting the loss function.

• allow_empty – If False, the method will error out if no loss has been computed by the model. If True, then if no loss is computed, the method returns 0.

Returns:

The total loss as a scalar tensor, or None if no loss results (which is the case when called by Model.test_step).