compute_loss

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

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(tf.keras.Model):

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

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

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

loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y)) loss += tf.add_n(self.losses) self.loss_tracker.update_state(loss) return loss

def reset_metrics(self):

self.loss_tracker.reset_states()

@property def metrics(self):

return [self.loss_tracker]

tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,)) dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)

inputs = tf.keras.layers.Input(shape=(10,), name=’my_input’) outputs = tf.keras.layers.Dense(10)(inputs) model = MyModel(inputs, outputs) model.add_loss(tf.reduce_sum(outputs))

optimizer = tf.keras.optimizers.SGD() model.compile(optimizer, loss=’mse’, steps_per_execution=10) model.fit(dataset, epochs=2, steps_per_epoch=10) print(‘My custom loss: ‘, model.loss_tracker.result().numpy()) ```

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.

Returns:

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