tf.keras.losses.BinaryFocalCrossentropy | TensorFlow v2.16.1 (original) (raw)

Computes focal cross-entropy loss between true labels and predictions.

Inherits From: Loss

tf.keras.losses.BinaryFocalCrossentropy(
    apply_class_balancing=False,
    alpha=0.25,
    gamma=2.0,
    from_logits=False,
    label_smoothing=0.0,
    axis=-1,
    reduction='sum_over_batch_size',
    name='binary_focal_crossentropy'
)

Binary cross-entropy loss is often used for binary (0 or 1) classification tasks. The loss function requires the following inputs:

y_true (true label): This is either 0 or 1.
y_pred (predicted value): This is the model's prediction, i.e, a single floating-point value which either represents alogit, (i.e, value in [-inf, inf] when from_logits=True) or a probability (i.e, value in [0., 1.] whenfrom_logits=False).

According to Lin et al., 2018, it helps to apply a "focal factor" to down-weight easy examples and focus more on hard examples. By default, the focal tensor is computed as follows:

focal_factor = (1 - output) ** gamma for class 1focal_factor = output ** gamma for class 0 where gamma is a focusing parameter. When gamma=0, this function is equivalent to the binary crossentropy loss.

Args
apply_class_balancing	A bool, whether to apply weight balancing on the binary classes 0 and 1.
alpha	A weight balancing factor for class 1, default is 0.25 as mentioned in reference Lin et al., 2018. The weight for class 0 is1.0 - alpha.
gamma	A focusing parameter used to compute the focal factor, default is2.0 as mentioned in the referenceLin et al., 2018.
from_logits	Whether to interpret y_pred as a tensor oflogit values. By default, we assume that y_pred are probabilities (i.e., values in [0, 1]).
label_smoothing	Float in [0, 1]. When 0, no smoothing occurs. When > 0, we compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of label_smoothing correspond to heavier smoothing.
axis	The axis along which to compute crossentropy (the features axis). Defaults to -1.
reduction	Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size" or None.
name	Optional name for the loss instance.

Examples:

With the compile() API:

model.compile(
    loss=keras.losses.BinaryFocalCrossentropy(
        gamma=2.0, from_logits=True),
    ...
)

As a standalone function:

# Example 1: (batch_size = 1, number of samples = 4) y_true = [0, 1, 0, 0] y_pred = [-18.6, 0.51, 2.94, -12.8] loss = keras.losses.BinaryFocalCrossentropy( gamma=2, from_logits=True) loss(y_true, y_pred) 0.691

# Apply class weight loss = keras.losses.BinaryFocalCrossentropy( apply_class_balancing=True, gamma=2, from_logits=True) loss(y_true, y_pred) 0.51

# Example 2: (batch_size = 2, number of samples = 4) y_true = [[0, 1], [0, 0]] y_pred = [[-18.6, 0.51], [2.94, -12.8]] # Using default 'auto'/'sum_over_batch_size' reduction type. loss = keras.losses.BinaryFocalCrossentropy( gamma=3, from_logits=True) loss(y_true, y_pred) 0.647

# Apply class weight loss = keras.losses.BinaryFocalCrossentropy( apply_class_balancing=True, gamma=3, from_logits=True) loss(y_true, y_pred) 0.482

# Using 'sample_weight' attribute with focal effect loss = keras.losses.BinaryFocalCrossentropy( gamma=3, from_logits=True) loss(y_true, y_pred, sample_weight=[0.8, 0.2]) 0.133

# Apply class weight loss = keras.losses.BinaryFocalCrossentropy( apply_class_balancing=True, gamma=3, from_logits=True) loss(y_true, y_pred, sample_weight=[0.8, 0.2]) 0.097

# Using 'sum' reduction` type. loss = keras.losses.BinaryFocalCrossentropy( gamma=4, from_logits=True, reduction="sum") loss(y_true, y_pred) 1.222

# Apply class weight loss = keras.losses.BinaryFocalCrossentropy( apply_class_balancing=True, gamma=4, from_logits=True, reduction="sum") loss(y_true, y_pred) 0.914

# Using 'none' reduction type. loss = keras.losses.BinaryFocalCrossentropy( gamma=5, from_logits=True, reduction=None) loss(y_true, y_pred) array([0.0017 1.1561], dtype=float32)

# Apply class weight loss = keras.losses.BinaryFocalCrossentropy( apply_class_balancing=True, gamma=5, from_logits=True, reduction=None) loss(y_true, y_pred) array([0.0004 0.8670], dtype=float32)

Methods

`call`

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call(
    y_true, y_pred
)

`from_config`

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@classmethod from_config( config )

`get_config`

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get_config()

`call`

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__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.