Your understanding is correct but pytorch doesn't compute cross entropy in that way. Pytorch uses the following formula. loss (x, class) = -log (exp (x [class]) / (\sum_j exp (x [j]))) = -x [class] + log (\sum_j exp (x [j])) Since, in your scenario, x = [0, 0, 0, 1] and class = 3, if you evaluate the above expression, you would get:
12.07.2020 · Yes, pytorch’s cross_entropy_loss()is a special case of cross-entropy that requires integer categorical labels (“hard targets”) for its targets. (It also takes logits, rather than probabilities, for its predictions.) It does sound like you want a general cross-entropy loss that takes probabilities (“soft tagets”) for its targets.
12.09.2018 · Hi. I think Pytorch calculates the cross entropy loss incorrectly while using the ignore_index option. The problem is that currently when specifying the ignore_index (say, = k), the function just ignores the value of the target y = k (in fact, it calculates the cross entropy at k but returns 0) but it still makes full use of the logit at index k to calculate the normalization term for …
By default, PyTorch's cross_entropy takes logits (the raw outputs from the model) as the input. I know that CrossEntropyLoss combines LogSoftmax (log (softmax (x))) and NLLLoss (negative log likelihood loss) in one single class. So, I think I can use NLLLoss to get cross-entropy loss from probabilities as follows: where, y_i,j denotes the true ...
The method used in the paper works by mixing two inputs and their respective targets. This requires the targets to be smooth (float/double). However, PyTorch's ...
Cross entropy loss for classification. Initialize metric. Parameters. name (str) – metric name. Defaults to class name. quantiles (List[float], optional) ...
The latter is useful for higher dimension inputs, such as computing cross entropy loss per-pixel for 2D images. The target that this criterion expects should contain either: Class indices in the range [ 0 , C − 1 ] [0, C-1] [ 0 , C − 1 ] where C C C is the number of classes; if ignore_index is specified, this loss also accepts this class index (this index may not necessarily be in the ...
17.03.2020 · Hi all, I am a newbie to pytorch and am trying to build a simple claasifier by my own. I am trying to train a tensor classifier with 4 classes, the inputs are one dimensional tensors with a length of 1000. This is the architecture of my neural network, I have used BatchNorm layer: class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv1d(1, 6, 5) …
14.12.2021 · 1 Cross entropy loss in pytorch nn.CrossEntropyLoss() . maybe someone is able to help me here. I am trying to compute the cross entropy loss of a given output of my network and the desired label, which i ...
There solution was to use .float() when entering into the loss Stack Exchange Network Stack Exchange network consists of 178 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.
07.02.2018 · The method used in the paper works by mixing two inputs and their respective targets. This requires the targets to be smooth (float/double). However, PyTorch’s nll_loss(used by CrossEntropyLoss) requires that the target tensors will be in the Long format. One idea is to do weighted sum of hard loss for each non zero label.