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- builtins.object
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- JITBCEWithLogitsLoss
- JITCrossEntropyLoss
- JITHuberLoss
- JITMeanAbsoluteErrorLoss
- JITMeanSquaredErrorLoss
class JITBCEWithLogitsLoss(builtins.object) |
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Custom binary cross entropy loss with logits implementation using numba.
Formula: -mean(y * log(p) + (1 - y) * log(1 - p))
Methods:
calculate_loss(self, logits, targets): Calculate the binary cross entropy loss.
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Methods defined here:
- __init__(self)
- Initializes the class with default values for logits and targets.
Attributes:
logits (numpy.ndarray): A 2D array initialized to zeros with shape (1, 1),
representing the predicted values.
targets (numpy.ndarray): A 2D array initialized to zeros with shape (1, 1),
representing the true target values.
- calculate_loss(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 JITCrossEntropyLoss(builtins.object) |
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Custom cross entropy loss implementation using numba for multi-class classification.
Formula: -sum(y * log(p) + (1 - y) * log(1 - p)) / m
Methods:
calculate_loss(self, logits, targets): Calculate the cross entropy loss.
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Methods defined here:
- __init__(self)
- Initializes the instance variables for the class.
Args:
logits: (np.ndarray) - A 2D array initialized to zeros with shape (1, 1),
representing the predicted values or outputs of the model.
targets: (np.ndarray) - A 2D array initialized to zeros with shape (1, 1),
representing the ground truth or target values.
- calculate_loss(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 JITHuberLoss(builtins.object) |
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JITHuberLoss(delta=1.0)
Custom Huber loss implementation using numba.
Attributes:
delta (float): The threshold parameter for Huber loss. Default is 1.0.
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Methods defined here:
- __init__(self, delta=1.0)
- Initializes the JITHuberLoss instance.
Args:
delta (float): The threshold at which the loss function transitions
from quadratic to linear. Default is 1.0.
- calculate_loss(self, y_pred, y_true)
- Calculate the Huber loss using the stored delta.
Args:
y_pred (np.ndarray): Predicted values.
y_true (np.ndarray): True target values.
Returns:
float: The calculated Huber loss.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class JITMeanAbsoluteErrorLoss(builtins.object) |
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Custom mean absolute error loss implementation using numba.
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Methods defined here:
- calculate_loss(self, y_pred, y_true)
- Calculate 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 JITMeanSquaredErrorLoss(builtins.object) |
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Custom mean squared error loss implementation using numba.
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Methods defined here:
- calculate_loss(self, y_pred, y_true)
- Calculate 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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