_sparse_categorical_crossentropy

sparse_categorical_crossentropy

Compute sparse categorical crossentropy.

Note that if any of the y_pred values are exactly 0, this will result in a NaN output. If from_logits is False, then each entry of y_pred should sum to 1. If they don't sum to 1 then tf and torch backends will result in different numerical values.

This method can be used with TensorFlow tensors:

true = tf.constant([[1], [0], [2]])
pred = tf.constant([[0.1, 0.8, 0.1], [0.9, 0.05, 0.05], [0.1, 0.2, 0.7]])
weights = tf.lookup.StaticHashTable(
    tf.lookup.KeyValueTensorInitializer(tf.constant([1, 2]), tf.constant([2.0, 3.0])), default_value=1.0)
b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true)  # 0.228
b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.22, 0.11, 0.36]
b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False, class_weights=weights)
# [0.44, 0.11, 1.08]

This method can be used with PyTorch tensors:

true = torch.tensor([[1], [0], [2]])
pred = torch.tensor([[0.1, 0.8, 0.1], [0.9, 0.05, 0.05], [0.1, 0.2, 0.7]])
weights = {1: 2.0, 2: 3.0}
b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true)  # 0.228
b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.22, 0.11, 0.36]
b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False, class_weights=weights)
# [0.44, 0.11, 1.08]

Parameters:

Name	Type	Description	Default
y_pred	Tensor	Prediction with a shape like (Batch, ..., C) for tensorflow and (Batch, C, ...) for PyTorch. dtype: float32 or float16.	required
y_true	Tensor	Ground truth class labels with a shape like (Batch, ...), with each element representing the label index starting from 0. dtype: int.	required
from_logits	bool	Whether y_pred is from logits. If True, a softmax will be applied to the prediction.	False
average_loss	bool	Whether to average the element-wise loss.	True
class_weights	Optional[Weight_Dict]	Mapping of class indices to a weight for weighting the loss function. Useful when you need to pay more attention to samples from an under-represented class.	None

Returns:

Type	Description
Tensor	The sparse categorical crossentropy between y_pred and y_true. A scalar if average_loss is True, else a
Tensor	tensor with the shape (Batch).

Raises:

Type	Description
AssertionError	If y_true or y_pred are unacceptable data types.

Source code in fastestimator/fastestimator/backend/_sparse_categorical_crossentropy.py

def sparse_categorical_crossentropy(y_pred: Tensor,
                                    y_true: Tensor,
                                    from_logits: bool = False,
                                    average_loss: bool = True,
                                    class_weights: Optional[Weight_Dict] = None) -> Tensor:
    """Compute sparse categorical crossentropy.

    Note that if any of the `y_pred` values are exactly 0, this will result in a NaN output. If `from_logits` is
    False, then each entry of `y_pred` should sum to 1. If they don't sum to 1 then tf and torch backends will
    result in different numerical values.

    This method can be used with TensorFlow tensors:
    ```python
    true = tf.constant([[1], [0], [2]])
    pred = tf.constant([[0.1, 0.8, 0.1], [0.9, 0.05, 0.05], [0.1, 0.2, 0.7]])
    weights = tf.lookup.StaticHashTable(
        tf.lookup.KeyValueTensorInitializer(tf.constant([1, 2]), tf.constant([2.0, 3.0])), default_value=1.0)
    b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true)  # 0.228
    b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.22, 0.11, 0.36]
    b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False, class_weights=weights)
    # [0.44, 0.11, 1.08]
    ```

    This method can be used with PyTorch tensors:
    ```python
    true = torch.tensor([[1], [0], [2]])
    pred = torch.tensor([[0.1, 0.8, 0.1], [0.9, 0.05, 0.05], [0.1, 0.2, 0.7]])
    weights = {1: 2.0, 2: 3.0}
    b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true)  # 0.228
    b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.22, 0.11, 0.36]
    b = fe.backend.sparse_categorical_crossentropy(y_pred=pred, y_true=true, average_loss=False, class_weights=weights)
    # [0.44, 0.11, 1.08]
    ```

    Args:
        y_pred: Prediction with a shape like (Batch, ..., C) for tensorflow and (Batch, C, ...) for PyTorch. dtype:
            float32 or float16.
        y_true: Ground truth class labels with a shape like (Batch, ...), with each element representing the label index
            starting from 0. dtype: int.
        from_logits: Whether y_pred is from logits. If True, a softmax will be applied to the prediction.
        average_loss: Whether to average the element-wise loss.
        class_weights: Mapping of class indices to a weight for weighting the loss function. Useful when you need to pay
            more attention to samples from an under-represented class.

    Returns:
        The sparse categorical crossentropy between `y_pred` and `y_true`. A scalar if `average_loss` is True, else a
        tensor with the shape (Batch).

    Raises:
        AssertionError: If `y_true` or `y_pred` are unacceptable data types.
    """
    assert isinstance(y_pred, (tf.Tensor, torch.Tensor)), "only support tf.Tensor or torch.Tensor as y_pred"
    assert isinstance(y_true, (tf.Tensor, torch.Tensor)), "only support tf.Tensor or torch.Tensor as y_true"
    if tf.is_tensor(y_pred):
        ce = tf.losses.sparse_categorical_crossentropy(y_pred=y_pred, y_true=y_true, from_logits=from_logits)
        if class_weights is not None:
            sample_weights = class_weights.lookup(
                tf.cast(tf.reshape(y_true, tf.shape(ce)), dtype=class_weights.key_dtype))
            ce = ce * sample_weights
    else:
        if from_logits:
            ce = torch.nn.CrossEntropyLoss(reduction="none")(input=y_pred, target=y_true.long())
        else:
            ce = torch.nn.NLLLoss(reduction="none")(input=torch.log(y_pred), target=y_true.long())

        if class_weights is not None:
            sample_weights = torch.ones_like(y_true, dtype=torch.float)
            for key in class_weights.keys():
                sample_weights[y_true == key] = class_weights[key]
            ce = ce * sample_weights.reshape(ce.shape)
    if average_loss:
        ce = reduce_mean(ce)
    return ce