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Can last layer (classification layer) output can be negative?,bcz I am getting all values as negative
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My last layer looks like (batchnorm->dropout->Linear)
Yes, it can be negative, if the last layer output can be understood as logits, which are not bounded.
Thanks @ptrblck for reply, I have heard about softmax function in classification, I am very new to this .from author of my GitHub repo I have confirmed that he is using softmax in cross entropy loss , as softmax fcn outputs will be in probability, but the value I am getting are integer, floating point values
My concern is does(softmax) is included in very last layer as I am getting non probability values
This would be wrong, since nn.CrossEntropyLoss expects raw logits and applies log_softmax internally as described in the docs.
I have read the docs , but my confusion is when I printed the output from the base code and the output from the one which I have altered the code both the output is in floating point
have a look into both outputs sample
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output from the base code:
16.696354 8.354133 10.186245 11.498075 12.300736 10.866700 11.166842
9.032269 10.319295 7.855459 9.150773 6.944067 11.078278 9.090258
7.000237 9.369949 7.972084 10.183696 10.354758 9.057864 7.839106
9.350568 8.339901 10.578559 8.151072 6.615736 10.325701 7.763713
9.784429 13.697207 10.587792 7.794194 9.910049 10.466525 10.924786
11.512972 12.069916 8.615462 11.006269 11.574995 9.244633 9.231817
9.324425 12.565766 10.593280 8.469263 8.870036 11.072852 10.698648
11.417478 12.453324 11.991568 8.204532 8.595074 9.544779 6.938322
9.163651 8.716043 11.082959 9.219058 10.724289 11.477286 11.559748
11.386333 10.270207 8.702656 10.115452 9.487432 8.977314 9.583576
7.993706 10.435097 10.116531 10.186015 9.529931 11.199296 9.484911
9.646361 7.954441 8.351682 8.918447 8.052635 9.389923 8.473695 8.584301
9.649491 10.457809 9.019952 8.396974 11.244998 6.340092 9.839185
6.674132 10.418262 7.842873 6.073377 8.398934 10.016559 7.708073
9.683187 9.297018 7.753638 8.203460 13.838176 10.963732 10.340004
11.344488 9.076180 7.599188 6.947826 7.193683 5.522867 10.630272
9.558903 9.628851 6.170614 9.047945 7.087698 11.783425 12.145776
11.271029 9.954466 11.373574 10.642700 11.845484 9.974299 11.123785
6.554690 8.252899 11.271506 5.816365 9.478853 8.866497 8.868212 -
output from the modified code:
-14.924747 -15.466200 -14.091967 -12.780253 -10.942934 -10.649412 -11.797517 -9.700449 -5.488928 -7.536321 -10.630082 -13.750564 -10.921607 -9.393652 -14.469529 -9.283759 -8.825542 -11.879877 -10.629101 -11.310738 -7.375803 -9.345245 -11.384661 -7.627495 -9.068569 -11.338547 -10.864298 -11.547069 -12.373827 -13.071493 -10.939083 -9.916439 -8.749317 -9.801317 -9.856300 -10.900458 -10.874573 -11.667508 -9.267649 -8.144867 -9.818483 -8.629480 -8.891129 -8.721351 -9.278702 -9.894162 -10.265675 -10.583538 -6.523682 -7.911346 -10.026297 -10.141512 -10.514209 -11.701536 -8.204363 -12.029481 -9.373199 -11.994194 -9.822119 -10.543645 -6.892629 -8.988424 -8.302072 -9.932587 -11.056336 -10.489428 -6.542980 -5.797212 -7.184329 -10.637536 -11.578483 -8.901155 -14.063570 -9.878801 -12.068350 -10.989915 -9.141111 -8.553189 -10.079253 -12.481957 -7.441250 -5.653161 -4.582238 -7.205393 -11.772191 -5.400904 -4.785841 -15.616782 -15.108768 -15.460314 -14.476255 -9.388381 -12.002260 -10.625971 -12.178844 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Logits are represented as floating point numbers in the range [-Inf, Inf].
If you want to get the class predictions, you can use torch.argmax(output, dim=1).
yeah I am using torch.max(output,1) for getting class prediction, I have evaluated for 5000 images , accuracy of prediction is same as what i was getting before(from base code)
but my concern is can output be negative? if it soo I can move further
outputs can contain negative values, as explained before. The output of torch.argmax should not contain negative values.
thanks @ptrblck, yeah as torch.max() gives the img class it cant be negative, I am not getting negative value for torch.max