Output of classification layer

Ganga: <ul>
<li>
<p>Can last layer (classification layer) output can be negative?,bcz I am getting all values  as negative</p>
</li>
<li>
<p>My last  layer looks like (batchnorm-&gt;dropout-&gt;Linear)</p>
</li>
</ul>

ptrblck: <p>Yes, it can be negative, if the last layer output can be understood as logits, which are not bounded.</p>

Ganga: <p>Thanks <a class="mention" href="/u/ptrblck">@ptrblck</a> 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</p>
<p>My concern is does(softmax) is included in very last layer as I am getting non probability values</p>

ptrblck: <aside class="quote no-group" data-username="Ganga" data-post="3" data-topic="96353">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ganga/48/28549_2.png" class="avatar"> Ganga:</div>
<blockquote>
<p>I have confirmed that he is using softmax in cross entropy loss</p>
</blockquote>
</aside>
<p>This would be wrong, since <code>nn.CrossEntropyLoss</code> expects raw logits and applies <code>log_softmax</code> internally as described in the <a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss">docs</a>.</p>

Ganga: <p>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<br>
have a look into both outputs sample</p>
<ul>
<li>
<p><strong>output from the base code:</strong><br>
16.696354 8.354133 10.186245 11.498075 12.300736 10.866700 11.166842<br>
9.032269 10.319295 7.855459 9.150773 6.944067 11.078278 9.090258<br>
7.000237 9.369949 7.972084 10.183696 10.354758 9.057864 7.839106<br>
9.350568 8.339901 10.578559 8.151072 6.615736 10.325701 7.763713<br>
9.784429 13.697207 10.587792 7.794194 9.910049 10.466525 10.924786<br>
11.512972 12.069916 8.615462 11.006269 11.574995 9.244633 9.231817<br>
9.324425 12.565766 10.593280 8.469263 8.870036 11.072852 10.698648<br>
11.417478 12.453324 11.991568 8.204532 8.595074 9.544779 6.938322<br>
9.163651 8.716043 11.082959 9.219058 10.724289 11.477286 11.559748<br>
11.386333 10.270207 8.702656 10.115452 9.487432 8.977314 9.583576<br>
7.993706 10.435097 10.116531 10.186015 9.529931 11.199296 9.484911<br>
9.646361 7.954441 8.351682 8.918447 8.052635 9.389923 8.473695 8.584301<br>
9.649491 10.457809 9.019952 8.396974 11.244998 6.340092 9.839185<br>
6.674132 10.418262 7.842873 6.073377 8.398934 10.016559 7.708073<br>
9.683187 9.297018 7.753638 8.203460 13.838176 10.963732 10.340004<br>
11.344488 9.076180 7.599188 6.947826 7.193683 5.522867 10.630272<br>
9.558903 9.628851 6.170614 9.047945 7.087698 11.783425 12.145776<br>
11.271029 9.954466 11.373574 10.642700 11.845484 9.974299 11.123785<br>
6.554690 8.252899 11.271506 5.816365 9.478853 8.866497 8.868212</p>
</li>
<li>
<p><strong>output from the modified code</strong>:<br>
-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 -13.565306 -12.647863 -9.406588 -8.665648 -7.453368 -10.149251 -9.376029 -6.375273 -8.216988 -3.747476 -11.429441 -4.374193 -15.604350 -11.742348 -11.518512 -9.860221 -13.548150 -11.518255 -12.010603 -9.612392 -12.391186 -11.025039 -13.260274 -13.135287 -10.671009 -11.138130 -14.439317 -15.647364 -13.423687 -11.016035 -11.076318 -10.595095 -12.421420 -8.869572 -14.374072 -12.589520 -12.590418 -13.917626 -6.548331 -9.402328 -7.252759 -12.496483 -10.553160 -6.649272 -9.539070 -8.899148 -8.414417 -7.749596 -8.681293 -11.269684 -9.499310 -9.282102 -12.725037 -14.374966 -13.139431 -6.361098 -8.762877 -11.736611 -13.809862 -11.898018 -10.444094 -10.377052 -12.177114 -11.741683 -8.074329 -8.566818 -8.870081 -9.658258 -7.284324 -9.309556 -9.178699 -9.968216 -8.615371 -6.829469 -9.614569 -7.418700 -9.835848 -7.022779 -8.463126 -9.525493 -8.248149 -8.402008 -8.429467 -10.062819 -8.570545 -8.131669 -9.946007 -5.550721 -10.329622 -5.090342 -7.400333 -6.980797 -10.276329 -10.533189 -8.035837 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</li>
</ul>

ptrblck: <p>Logits are represented as floating point numbers in the range <code>[-Inf, Inf]</code>.<br>
If you want to get the class predictions, you can use <code>torch.argmax(output, dim=1)</code>.</p>

Ganga: <p>yeah I  am using <code>torch.max(output,1)</code> for getting class prediction,  I have evaluated for 5000 images , accuracy of prediction is same as what i was getting before(from base code)</p>
<p>but my concern is can output be negative? if it soo I can move further</p>

ptrblck: <p><code>outputs</code> can contain negative values, as explained before. The output of <code>torch.argmax</code> should not contain negative values.</p>

Ganga: <p>thanks <a class="mention" href="/u/ptrblck">@ptrblck</a>, yeah as <code>torch.max()</code>  gives the img class it cant be negative, I am not getting negative value for torch.max</p>