machine learning - What is cross-entropy? - Stack Overflow
In short, cross-entropy (CE) is the measure of how far is your predicted value from the true label. The cross here refers to calculating the entropy between two or more features / true labels (like 0, 1). And the term entropy itself refers to randomness, so large value of it means your prediction is far off from real labels.
Trying to understand cross_entropy loss in PyTorch
2019年7月23日 · This is a very newbie question but I'm trying to wrap my head around cross_entropy loss in Torch so I created the following code: x = torch.FloatTensor([ [1.,0.,0.] ...
How to handle log (0) when using cross entropy - Stack Overflow
2018年4月25日 · xlogy(Y, predY) + xlogy(1 - Y, 1 - predY) If you expect predY to contain very small values, you might get better numerical results using scipy.special.xlog1py in the second term: xlogy(Y, predY) + xlog1py(1 - Y, -predY) Alternatively, knowing that the values in Y are either 0 or 1, you can compute the cost in an entirely different way: Yis1 = Y ...
python - `CrossEntropyLoss()` in PyTorch - Stack Overflow
The combination of nn.LogSoftmax and nn.NLLLoss is equivalent to using nn.CrossEntropyLoss.This terminology is a particularity of PyTorch, as the nn.NLLoss [sic] computes, in fact, the cross entropy but with log probability predictions as inputs where nn.CrossEntropyLoss takes scores (sometimes called logits).
python - How to correctly use Cross Entropy Loss vs Softmax for ...
The definition of CrossEntropyLoss in PyTorch is a combination of softmax and cross-entropy. Specifically. CrossEntropyLoss (x, y) := H (one_hot (y), softmax (x)) Note that one_hot is a function that takes an index y, and expands it into a one-hot vector. Equivalently you can formulate CrossEntropyLoss as a combination of LogSoftmax and ...
Channel wise CrossEntropyLoss for image segmentation in pytorch
2018年6月17日 · The built-in functions do indeed already support KD cross-entropy loss. In the 3D case, the torch.nn.CrossEntropy() functions expects two arguments: a 4D input matrix and a 3D target matrix. The input matrix is in the shape: (Minibatch, Classes, H, W). The target matrix is in the shape (Minibatch, H, W) with numbers ranging from 0 to (Classes-1).
Is there a version of sparse categorical cross entropy in pytorch?
2020年8月13日 · Here is an example of usage of nn.CrossEntropyLoss for image segmentation with a batch of size 1, width 2, height 2 and 3 classes. Image segmentation is a classification problem at pixel level. Of course you can also use nn.CrossEntropyLoss for basic image classification as well. The sudoku problem in the question can be seen as an image ...
What is the difference between a sigmoid followed by the cross …
2017年9月19日 · You're confusing the cross-entropy for binary and multi-class problems. Multi-class cross-entropy. The formula that you use is correct and it directly corresponds to tf.nn.softmax_cross_entropy_with_logits:-tf.reduce_sum(p * tf.log(q), axis=1) p and q are expected to be probability distributions over N classes. In particular, N can be 2, as in ...
How to address warning that …
When attempting to import TensorFlow in a Jupyter Notebook, I encounter the following warning message. import tensorflow as tf WARNING:tensorflow:From C:\Users\bhara\anaconda3\Lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated.
How to choose cross-entropy loss in TensorFlow? - Stack Overflow
Classification problems, such as logistic regression or multinomial logistic regression, optimize a cross-entropy loss. Normally, the cross-entropy layer follows the softmax layer, which produces probability distribution. In tensorflow, there are at least a dozen of different cross-entropy loss functions: tf.losses.softmax_cross_entropy.