Reformatting after focal loss implementation

keras/backend.py

@@ -5578,41 +5578,41 @@ def categorical_crossentropy(target, output, from_logits=False, axis=-1):
 @tf.__internal__.dispatch.add_dispatch_support
 @doc_controls.do_not_generate_docs
 def categorical_focal_crossentropy(
-  target,
-  output,
-  alpha=0.25,
-  gamma=2.0,
-  from_logits=False,
-  axis=-1,
+ target,
+ output,
+ alpha=0.25,
+ gamma=2.0,
+ from_logits=False,
+ axis=-1,
 ):
  """Categorical focal crossentropy (alpha balanced) between an output tensor and a target tensor.
-  According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), 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:
-  It has pt defined as:
-  pt = p, if y = 1 else 1 - p
-  The authors use alpha-balanced variant of focal loss in the paper:
-  FL(pt) = −α_t * (1 − pt)^gamma * log(pt)
-  Extending this to multi-class case is straightforward:
-  FL(pt) = α_t * (1 − pt)^gamma * CE, where minus comes from negative log-likelihood and included in CE.
-  `modulating_factor` is (1 − pt)^gamma,
-  where `gamma` is a focusing parameter. When `gamma` = 0, there is no focal
-  effect on the categorical crossentropy.
-  Args:
-  target: A tensor with the same shape as `output`.
-  output: A tensor.
-  alpha: A weight balancing factor for all classes, default is `0.25` as
-  mentioned in the reference. It can be a list of floats or a scalar.
-  In the multi-class case, alpha may be set by inverse class frequency by
-  using `compute_class_weight` from `sklearn.utils`.
-  gamma: A focusing parameter, default is `2.0` as mentioned in the
-  reference. It helps to gradually reduce the importance given to
-  simple examples in a smooth manner.
-  from_logits: Whether `output` is expected to be a logits tensor. By
-  default, we consider that `output` encodes a probability distribution.
-  Returns:
-  A tensor.
-  """
+ According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), 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:
+ It has pt defined as:
+ pt = p, if y = 1 else 1 - p
+ The authors use alpha-balanced variant of focal loss in the paper:
+ FL(pt) = −α_t * (1 − pt)^gamma * log(pt)
+ Extending this to multi-class case is straightforward:
+ FL(pt) = α_t * (1 − pt)^gamma * CE, where minus comes from negative log-likelihood and included in CE.
+ `modulating_factor` is (1 − pt)^gamma,
+ where `gamma` is a focusing parameter. When `gamma` = 0, there is no focal
+ effect on the categorical crossentropy.
+ Args:
+ target: A tensor with the same shape as `output`.
+ output: A tensor.
+ alpha: A weight balancing factor for all classes, default is `0.25` as
+ mentioned in the reference. It can be a list of floats or a scalar.
+ In the multi-class case, alpha may be set by inverse class frequency by
+ using `compute_class_weight` from `sklearn.utils`.
+ gamma: A focusing parameter, default is `2.0` as mentioned in the
+ reference. It helps to gradually reduce the importance given to
+ simple examples in a smooth manner.
+ from_logits: Whether `output` is expected to be a logits tensor. By
+ default, we consider that `output` encodes a probability distribution.
+ Returns:
+ A tensor.
+ """
  target = tf.convert_to_tensor(target)
  output = tf.convert_to_tensor(output)
  target.shape.assert_is_compatible_with(output.shape)
@@ -5642,6 +5642,7 @@ def categorical_focal_crossentropy(
  focal_cce = tf.reduce_sum(focal_cce, axis=axis)
  return focal_cce
 
+
 @keras_export("keras.backend.sparse_categorical_crossentropy")
 @tf.__internal__.dispatch.add_dispatch_support
 @doc_controls.do_not_generate_docs