15.11.2019 · I prefer to use binary cross entropy as the loss function. The function version of binary_cross_entropy (as distinct from the class (function object) version, BCELoss), supports a fine-grained, per-individual-element-of-each-sample weight argument. So, using this, you could weight the loss contribution of each frame
Function that measures the Binary Cross Entropy between the target and input probabilities. See BCELoss for details. input – Tensor of arbitrary shape as probabilities. target – Tensor of the same shape as input with values between 0 and 1. weight ( Tensor, optional) – a manual rescaling weight if provided it’s repeated to match input ...
CrossEntropyLoss accepts logits and targets, a.k.a X should be logits, ... and I have used both binary cross entropy loss and cross entropy loss of pytorch.
19.05.2019 · In PyTorch, these refer to implementations that accept different input arguments (but compute the same thing). This is summarized below. PyTorch Loss-Input Confusion (Cheatsheet) torch.nn.functional.binary_cross_entropy takes logistic sigmoid values as inputs; torch.nn.functional.binary_cross_entropy_with_logits takes logits as inputs
Aug 18, 2018 · In the pytorch docs, it says for cross entropy loss: input has to be a Tensor of size (minibatch, C) Does this mean that for binary (0,1) prediction, the input must be converted into an (N,2) tensor where the second dimension is equal to (1-p)?
Function that measures the Binary Cross Entropy between the target and input probabilities. See BCELoss for details. input – Tensor of arbitrary shape as probabilities. target – Tensor of the same shape as input with values between 0 and 1. weight ( Tensor, optional) – a manual rescaling weight if provided it’s repeated to match input ...
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 ...
PyTorch chooses to set log (0) = − ∞ \log (0) = -\infty lo g (0) = − ∞, since lim x → 0 log (x) = − ∞ \lim_{x\to 0} \log (x) = -\infty lim x → 0 lo g (x) = − ∞. However, an infinite term in the loss equation is not desirable for several reasons.
Nov 15, 2019 · The function version of binary_cross_entropy(as distinct from the class (function object) version, BCELoss), supports a fine-grained, per-individual-element-of-each-sample weightargument. So, using this, you could weight the loss contribution of each frame separately, and, in particular, give the padding frames a weight of zero.
torch.nn.functional.binary_cross_entropy_with_logits. Function that measures Binary Cross Entropy between target and input logits. See BCEWithLogitsLoss for details. input – Tensor of arbitrary shape as unnormalized scores (often referred to as logits). weight ( Tensor, optional) – a manual rescaling weight if provided it’s repeated to ...
BCELoss (weight=None, size_average=None, reduce=None, reduction='mean')[source]. Creates a criterion that measures the Binary Cross Entropy between the ...
BCELoss. class torch.nn.BCELoss(weight=None, size_average=None, reduce=None, reduction='mean') [source] Creates a criterion that measures the Binary Cross Entropy between the target and the input probabilities: The unreduced (i.e. …