akankshakshurai/cuda-error-resolution-analysis huggingface dataset:
Why am i having zero losses while training the custom model in yolov8 .?

username: Nishant_Wankhade
cooked: <p>I am getting this kind of things … and not able to get any results after training the yolov8 custom model …<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/8/b/8b11d54933a71156598903b1198a4b0c6f4574ee.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/8b11d54933a71156598903b1198a4b0c6f4574ee" title="20230525_173617"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/8/b/8b11d54933a71156598903b1198a4b0c6f4574ee_2_666x500.jpeg" alt="20230525_173617" data-base62-sha1="jQgzH84zyyNPmGI9Jh5R7umt8IK" width="666" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/8/b/8b11d54933a71156598903b1198a4b0c6f4574ee_2_666x500.jpeg, https://discuss.pytorch.org/uploads/default/optimized/3X/8/b/8b11d54933a71156598903b1198a4b0c6f4574ee_2_999x750.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/8/b/8b11d54933a71156598903b1198a4b0c6f4574ee_2_1332x1000.jpeg 2x" data-dominant-color="606669"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">20230525_173617</span><span class="informations">1920×1440 271 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>
Batching for RNN

username: noahw31
cooked: <p>Hi everyone,<br>
I have time series measurement data and want to model the behavior of the device using RNNs. I want to train using LSTM and GRU. I am not sure how to feed my data to the training loop and therefore am asking for some support on that side.</p>
<p>My data is as mentioned time-series measurement data. It is complex valued and i want to feed my RNN following versions of the data: Real part, Imaginary part and absolute value. And of each of those, i want to have the current version and delayed version with memory depth m. I stack everything on top of each other. After generating the data tensor it is of shape</p>
<pre><code class="lang-auto">(300000, 3 * (m+1))
</code></pre>
<p>Now I create a dataloader from that by <code>DataLoader(dataset, batch_size=batch_size, shuffle=False)</code><br>
The <code>batch_size</code> i chose to be 1000. During training, i therefore pass a tensor of <code>(1000, 3*(m+1))</code> to the LSTM, which was previously configured to have <code>input_size=3*(m+1), hidden_size=h</code>. From my understanding this is equivalent to splitting my measurement data into sequences of length 1000 and train the coefficients on that, before passing the next 1000 samples.<br>
From <a href="https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html" class="inline-onebox" rel="noopener nofollow ugc">LSTM — PyTorch 2.0 documentation</a> however i see that the desired input for the LSTM is <code>(N, L, H)</code> which in my case would be <code>(1000, ??, 3*(m+1))</code>, but i am unsure what<br>
i should do with the L=sequence length. As you might see i am confused on what to do and how to handle my data so any help is appreciated!</p>
<p>Feel free to ask for more details, and also point me to other resources where my question might be answered. Thanks in advance</p>