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- builtins.object
-
- CuPyBCEWithLogitsLoss
- CuPyCrossEntropyLoss
class CuPyBCEWithLogitsLoss(builtins.object) |
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Optimized binary cross entropy loss with logits implementation using cupy.
Formula: -mean(y * log(sigmoid(x)) + (1 - y) * log(1 - sigmoid(x)))
Methods:
__call__(self, logits, targets): Calculate the binary cross entropy loss.
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Methods defined here:
- __call__(self, logits, targets)
- Calculate the binary cross entropy loss.
Args:
logits (cp.ndarray): The logits (predicted values) of shape (num_samples,).
targets (cp.ndarray): The target labels of shape (num_samples,).
Returns:
float: The binary cross entropy loss.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class CuPyCrossEntropyLoss(builtins.object) |
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Optimized cross entropy loss implementation using cupy for multi-class classification.
Formula: -sum(y * log(p)) / m
Methods:
__call__(self, logits, targets): Calculate the cross entropy loss.
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Methods defined here:
- __call__(self, logits, targets)
- Calculate the cross entropy loss.
Args:
logits (cp.ndarray): The logits (predicted values) of shape (num_samples, num_classes).
targets (cp.ndarray): The target labels of shape (num_samples, num_classes) or (num_samples,).
Returns:
float: The cross entropy loss.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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