binary_crossentropy

binary_crossentropy

Compute binary crossentropy.

This method is applicable when there are only two label classes (zero and one). There should be a single floating point prediction per example.

This method can be used with TensorFlow tensors:

true = tf.constant([[1], [0], [1], [0]])
pred = tf.constant([[0.9], [0.3], [0.8], [0.1]])
b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true)  # 0.197
b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.105, 0.356, 0.223, 0.105]

This method can be used with PyTorch tensors:

true = torch.tensor([[1], [0], [1], [0]])
pred = torch.tensor([[0.9], [0.3], [0.8], [0.1]])
b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true)  # 0.197
b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.105, 0.356, 0.223, 0.105]

Parameters:

Name	Type	Description	Default
y_pred	Tensor	Prediction with a shape like (batch, ...). dtype: float32.	required
y_true	Tensor	Ground truth class labels with the same shape as y_pred. dtype: int or float32.	required
from_logits	bool	Whether y_pred is from logits. If True, a sigmoid will be applied to the prediction.	False
average_loss	bool	Whether to average the element-wise loss.	True

Returns:

Type	Description
Tensor	The binary crossentropy between y_pred and y_true. A scalar if average_loss is True, else a tensor with
Tensor	the same shape as y_true.

Raises:

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

Source code in fastestimator\fastestimator\backend\binary_crossentropy.py

def binary_crossentropy(y_pred: Tensor, y_true: Tensor, from_logits: bool = False, average_loss: bool = True) -> Tensor:
    """Compute binary crossentropy.

    This method is applicable when there are only two label classes (zero and one). There should be a single floating
    point prediction per example.

    This method can be used with TensorFlow tensors:
    ```python
    true = tf.constant([[1], [0], [1], [0]])
    pred = tf.constant([[0.9], [0.3], [0.8], [0.1]])
    b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true)  # 0.197
    b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.105, 0.356, 0.223, 0.105]
    ```

    This method can be used with PyTorch tensors:
    ```python
    true = torch.tensor([[1], [0], [1], [0]])
    pred = torch.tensor([[0.9], [0.3], [0.8], [0.1]])
    b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true)  # 0.197
    b = fe.backend.binary_crossentropy(y_pred=pred, y_true=true, average_loss=False)  # [0.105, 0.356, 0.223, 0.105]
    ```

    Args:
        y_pred: Prediction with a shape like (batch, ...). dtype: float32.
        y_true: Ground truth class labels with the same shape as `y_pred`. dtype: int or float32.
        from_logits: Whether y_pred is from logits. If True, a sigmoid will be applied to the prediction.
        average_loss: Whether to average the element-wise loss.

    Returns:
        The binary crossentropy between `y_pred` and `y_true`. A scalar if `average_loss` is True, else a tensor with
        the same shape as `y_true`.

    Raises:
        AssertionError: If `y_true` or `y_pred` are unacceptable data types.
    """
    assert type(y_pred) is type(y_true), "y_pred and y_true must be same tensor type"
    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 isinstance(y_pred, tf.Tensor):
        ce = tf.losses.binary_crossentropy(y_pred=y_pred,
                                           y_true=tf.reshape(y_true, y_pred.shape),
                                           from_logits=from_logits)
        ce = tf.reshape(ce, [ce.shape[0], -1])
        ce = tf.reduce_mean(ce, 1)
    else:
        y_true = y_true.to(torch.float)
        if from_logits:
            ce = torch.nn.BCEWithLogitsLoss(reduction="none")(input=y_pred, target=y_true.view(y_pred.size()))
        else:
            ce = torch.nn.BCELoss(reduction="none")(input=y_pred, target=y_true.view(y_pred.size()))
        ce = ce.view(ce.shape[0], -1)
        ce = torch.mean(ce, dim=1)

    if average_loss:
        ce = reduce_mean(ce)
    return ce