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- builtins.object
-
- BCEWithLogitsLoss
- CrossEntropyLoss
- HuberLoss
- MeanAbsoluteErrorLoss
- MeanSquaredErrorLoss
class BCEWithLogitsLoss(builtins.object) |
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Custom binary cross entropy loss with logits implementation using numpy.
Formula: -mean(y * log(p) + (1 - y) * log(1 - p))
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 (np.ndarray): The logits (predicted values) of shape (num_samples,).
targets (np.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 CrossEntropyLoss(builtins.object) |
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Custom cross entropy loss implementation using numpy for multi-class classification.
Formula: -sum(y * log(p) + (1 - y) * log(1 - 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 (np.ndarray): The logits (predicted values) of shape (num_samples, num_classes).
targets (np.ndarray): The target labels of shape (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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class HuberLoss(builtins.object) |
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Custom Huber loss implementation using numpy.
Formula: mean(0.5 * (y_true - y_pred)**2) if abs(y_true - y_pred) <= delta else mean(delta * (abs(y_true - y_pred) - delta / 2))
Methods:
__call__(self, y_true, y_pred, delta=1.0): Calculate the Huber loss.
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Methods defined here:
- __call__(self, y_true, y_pred, delta=1.0)
- Calculate the Huber loss.
Args:
y_true (np.ndarray): The true labels of shape (num_samples,).
y_pred (np.ndarray): The predicted values of shape (num_samples,).
delta (float): The threshold for the Huber loss.
Returns:
float: The Huber loss.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class MeanAbsoluteErrorLoss(builtins.object) |
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Custom mean absolute error loss implementation using numpy.
Formula: mean(abs(y_true - y_pred))
Methods:
__call__(self, y_true, y_pred): Calculate the mean absolute error loss.
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Methods defined here:
- __call__(self, y_true, y_pred)
- Calculate the mean absolute error loss.
Args:
y_true (np.ndarray): The true labels of shape (num_samples,).
y_pred (np.ndarray): The predicted values of shape (num_samples,).
Returns:
float: The mean absolute error loss.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class MeanSquaredErrorLoss(builtins.object) |
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Custom mean squared error loss implementation using numpy.
Formula: mean((y_true - y_pred) ** 2)
Methods:
__call__(self, y_true, y_pred): Calculate the mean squared error loss.
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Methods defined here:
- __call__(self, y_true, y_pred)
- Calculate the mean squared error loss.
Args:
y_true (np.ndarray): The true labels of shape (num_samples,).
y_pred (np.ndarray): The predicted values of shape (num_samples,).
Returns:
float: The mean squared error loss.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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