weights of each element of the input
size average of batch
indicating whether to accept log-probabilities or probabilities as input. True means accepting log-probabilities as input.
numeric operator
Performs a back-propagation step through the criterion, with respect to the given input.
Performs a back-propagation step through the criterion, with respect to the given input.
input data
target
gradient corresponding to input data
Deep copy this criterion
Takes an input object, and computes the corresponding loss of the criterion,
compared with target.
Takes an input object, and computes the corresponding loss of the criterion,
compared with target.
input data
target
the loss of criterion
Computing the gradient of the criterion with respect to its own input.
Computing the gradient of the criterion with respect to its own input. This is returned in gradInput. Also, the gradInput state variable is updated accordingly.
input data
target data / labels
gradient of input
Computes the loss using input and objective function.
Computes the loss using input and objective function. This function returns the result which is stored in the output field.
input of the criterion
target or labels
the loss of the criterion
The negative log likelihood criterion. It is useful to train a classification problem with n classes. If provided, the optional argument weights should be a 1D Tensor assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set.
The input given through a forward() is expected to contain log-probabilities/probabilities of each class: input has to be a 1D Tensor of size n. Obtaining log-probabilities/probabilities in a neural network is easily achieved by adding a LogSoftMax/SoftMax layer in the last layer of your neural network. You may use CrossEntropyCriterion instead, if you prefer not to add an extra layer to your network. This criterion expects a class index (1 to the number of class) as target when calling forward(input, target) and backward(input, target).
In the log-probabilities case, The loss can be described as: loss(x, class) = -x[class] or in the case of the weights argument it is specified as follows: loss(x, class) = -weights[class] * x[class]
Due to the behaviour of the backend code, it is necessary to set sizeAverage to false when calculating losses in non-batch mode.
Note that if the target is
-1, the training process will skip this sample. In other words, the forward process will return zero output and the backward process will also return zerogradInput.By default, the losses are averaged over observations for each minibatch. However, if the field sizeAverage is set to false, the losses are instead summed for each minibatch.
In particular, when weights=None, size_average=True and logProbAsInput=False, this is same as
sparse_categorical_crossentropyloss in keras.numeric type