org.nd4j.linalg.lossfunctions.impl
LossComm
Related Doc:
package impl
computedLdYHat Compute the gradient of the loss function with respect to the prediction: dLdYHat
computedLdYHat Compute the gradient of the loss function with respect to the prediction: dLdYHat
Label/expected output
Output of the model (neural network), before the activation function is applied
Activation function that should be applied to preOutput
Mask array; may be null
Gradient dL/dYHat
needs modification
scoreArray Calculates the loss for a single data point or in other words a batch size of one
scoreArray Calculates the loss for a single data point or in other words a batch size of one
Labels/expected output
Output of the model (neural network)
Activation function that should be applied to preOutput
Mask associated with the labels
An array the shape and size of the output of the neural net.
needs modification based on the actual loss function.
Compute the gradient of the loss function with respect to the inputs: dL/dOutput
Compute the gradient of the loss function with respect to the inputs: dL/dOutput
Label/expected output
Output of the model (neural network), before the activation function is applied
Activation function that should be applied to preOutput
Mask array; may be null
Gradient dL/dPreOut
fixed, no need to change
computeGradientAndScore Compute both the score (loss function value) and gradient.
computeGradientAndScore Compute both the score (loss function value) and gradient. This is equivalent to calling computeScore and computeGradient individually
Label/expected output
Output of the model (neural network)
Activation function that should be applied to preOutput
Mask array; may be null
Whether the score should be averaged (divided by number of rows in labels/output) or not
The score (loss function value) and gradient
fixed, no need to change
computeScore Compute the score (loss function value) for the given inputs.
computeScore Compute the score (loss function value) for the given inputs.
Label/expected preOutput
Output of the model (neural network)
Activation function that should be applied to preOutput
Mask array; may be null
Whether the score should be averaged (divided by number of rows in labels/preOutput) or not
Loss function value
fixed, no need to change
computeScoreArray Compute the score (loss function value) for each example individually.
computeScoreArray Compute the score (loss function value) for each example individually. For input [numExamples,nOut] returns scores as a column vector: [numExamples,1]
Labels/expected output
Output of the model (neural network)
Activation function that should be applied to preOutput
Loss function value for each example; column vector
fixed, no need to change
LossComm LossFunction Implementation Common Utilities Only 3 parts for modification: 1) scoreArray and 2) computedLdYHat, 3) name