Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 253881,7 "name": "Rahul Raj Devaraja",8 "username": "rahulrajdevaraja",9 "avatar_template": "/user_avatar/discuss.pytorch.org/rahulrajdevaraja/{size}/30917_2.png",10 "created_at": "2020-12-28T10:04:47.666Z",11 "cooked": "<p>I need a suggestion of using an existing bert model which is pre-trained for sentence classification.</p>\n<p>the existing model accepts text in form of: “ClC1=CC=CC(Cl)=C1C=O.ClC1=CC=CC(Cl)” which is a chemical reaction for example. Now to enhance the model where I can use continuous values such as time and temperature as features to it to retrain the model on my dataset.</p>\n<p>my idea is to use an ensemble approach, where I take the last layer output values of the existing bert model, create another model to just accept the continuous variables and concat them in an ensemble approach similar to this link: <a href=\"https://discuss.pytorch.org/t/combining-trained-models-in-pytorch/28383\" class=\"inline-onebox\">Combining Trained Models in PyTorch</a></p>\n<p>Any alternate approaches or suggestions? or links to use as a resource for this implementation</p>\n<p>Thanks<br>\nRahul Raj Devaraja</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 1,15 "updated_at": "2020-12-28T10:04:47.666Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 52,20 "reads": 4,21 "readers_count": 3,22 "score": 260.8,23 "yours": false,24 "topic_id": 107315,25 "topic_slug": 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"message_bus_last_id": 0,382 "participant_count": 1,383 "show_read_indicator": false,384 "thumbnails": null,385 "slow_mode_enabled_until": null,386 "can_vote": false,387 "vote_count": 0,388 "user_voted": false,389 "discourse_zendesk_plugin_zendesk_id": null,390 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",391 "details": {392 "can_edit": false,393 "notification_level": 1,394 "participants": [395 {396 "id": 40493,397 "username": "rahulrajdevaraja",398 "name": "Rahul Raj Devaraja",399 "avatar_template": "/user_avatar/discuss.pytorch.org/rahulrajdevaraja/{size}/30917_2.png",400 "post_count": 1,401 "primary_group_name": null,402 "flair_name": null,403 "flair_url": null,404 "flair_color": null,405 "flair_bg_color": null,406 "flair_group_id": null,407 "trust_level": 1408 }409 ],410 "created_by": {411 "id": 40493,412 "username": "rahulrajdevaraja",413 "name": "Rahul Raj Devaraja",414 "avatar_template": "/user_avatar/discuss.pytorch.org/rahulrajdevaraja/{size}/30917_2.png"415 },416 "last_poster": {417 "id": 40493,418 "username": "rahulrajdevaraja",419 "name": "Rahul Raj Devaraja",420 "avatar_template": "/user_avatar/discuss.pytorch.org/rahulrajdevaraja/{size}/30917_2.png"421 },422 "links": [423 {424 "url": "https://discuss.pytorch.org/t/combining-trained-models-in-pytorch/28383",425 "title": "Combining Trained Models in PyTorch",426 "internal": true,427 "attachment": false,428 "reflection": false,429 "clicks": 2,430 "user_id": 40493,431 "domain": "discuss.pytorch.org",432 "root_domain": "pytorch.org"433 }434 ]435 },436 "bookmarks": []437 },438 {439 "post_stream": {440 "posts": [441 {442 "id": 253009,443 "name": "",444 "username": "T33K3Y",445 "avatar_template": "/letter_avatar_proxy/v4/letter/t/da6949/{size}.png",446 "created_at": "2020-12-22T16:47:11.655Z",447 "cooked": "<p>Hello People,</p>\n<p>i’m trying to get Faster Rcnn to run according to the tutorial given in</p><aside class=\"onebox allowlistedgeneric\">\n <header class=\"source\">\n <a href=\"https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html#modifying-the-model-to-add-a-different-backbone\" target=\"_blank\" rel=\"noopener nofollow ugc\">pytorch.org</a>\n </header>\n <article class=\"onebox-body\">\n <img src=\"\" class=\"thumbnail\" width=\"\" height=\"\">\n\n<h3><a href=\"https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html#modifying-the-model-to-add-a-different-backbone\" target=\"_blank\" rel=\"noopener nofollow ugc\">TorchVision Object Detection Finetuning Tutorial — PyTorch Tutorials 1.7.1...</a></h3>\n\n\n\n </article>\n <div class=\"onebox-metadata\">\n \n \n </div>\n <div style=\"clear: both\"></div>\n</aside>\n<p>\n.<br>\nI get</p>\n<p>Traceback (most recent call last):<br>\nFile “/home/hmmmm/PycharmProjects/pythonProject3/identitymodul.py”, line 296, in <br>\nout = model(images)<br>\nFile “/home/hmmmm/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 722, in _call_impl<br>\nresult = self.forward(*input, **kwargs)<br>\nFile “/home/hmmmm/anaconda3/lib/python3.8/site-packages/torchvision/models/detection/generalized_rcnn.py”, line 99, in forward<br>\ndetections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)<br>\nFile “/home/hmmmm/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 722, in _call_impl<br>\nresult = self.forward(*input, **kwargs)<br>\nFile “/home/hmmmm/anaconda3/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py”, line 751, in forward<br>\nbox_features = self.box_roi_pool(features, proposals, image_shapes)<br>\nFile “/home/hmmmm/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 722, in _call_impl<br>\nresult = self.forward(*input, **kwargs)<br>\nFile “/home/hmmmm/anaconda3/lib/python3.8/site-packages/torchvision/ops/poolers.py”, line 198, in forward<br>\nself.setup_scales(x_filtered, image_shapes)<br>\nFile “/home/hmmmm/anaconda3/lib/python3.8/site-packages/torchvision/ops/poolers.py”, line 162, in setup_scales<br>\nlvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item()<br>\nIndexError: list index out of range</p>\n<p>as an error, so i tried to debug and found out that “x_filtered” is empty and this is caused by this for-loop</p>\n<pre><code> for k, v in x.items():\n if k in self.featmap_names:\n x_filtered.append(v)\n</code></pre>\n<p>so i have added some prints to better understand what was happening</p>\n<pre><code> for k, v in x.items():\n\n print('k')\n print(k)\n print(self.featmap_names)\n print(k in self.featmap_names)\n if k in self.featmap_names:\n x_filtered.append(v)\n print('testo')\n</code></pre>\n<p>and got the following output:</p>\n<p>k<br>\n0<br>\n[0]<br>\nFalse</p>\n<p>As I see it this False should be true, but i guess i am not seeing something.</p>\n<p>Would be thankful for any help.</p>",448 "post_number": 1,449 "post_type": 1,450 "posts_count": 2,451 "updated_at": "2020-12-22T16:47:11.655Z",452 "reply_count": 0,453 "reply_to_post_number": null,454 "quote_count": 0,455 "incoming_link_count": 51,456 "reads": 5,457 "readers_count": 4,458 "score": 256.0,459 "yours": false,460 "topic_id": 106873,461 "topic_slug": "if-k-0-and-self-featmap-names-0-shouldnt-k-in-self-featmap-names-be-true-getting-false",462 "display_username": "",463 "primary_group_name": null,464 "flair_name": null,465 "flair_url": null,466 "flair_bg_color": null,467 "flair_color": null,468 "flair_group_id": null,469 "badges_granted": [],470 "version": 1,471 "can_edit": false,472 "can_delete": false,473 "can_recover": false,474 "can_see_hidden_post": false,475 "can_wiki": false,476 "link_counts": [477 {478 "url": "https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html#modifying-the-model-to-add-a-different-backbone",479 "internal": false,480 "reflection": false,481 "title": "TorchVision Object Detection Finetuning Tutorial — PyTorch Tutorials 1.7.1 documentation",482 "clicks": 1483 }484 ],485 "read": true,486 "user_title": null,487 "bookmarked": false,488 "actions_summary": [],489 "moderator": false,490 "admin": false,491 "staff": false,492 "user_id": 40323,493 "hidden": false,494 "trust_level": 1,495 "deleted_at": null,496 "user_deleted": false,497 "edit_reason": null,498 "can_view_edit_history": true,499 "wiki": false,500 "post_url": "/t/if-k-0-and-self-featmap-names-0-shouldnt-k-in-self-featmap-names-be-true-getting-false/106873/1",501 "can_accept_answer": false,502 "can_unaccept_answer": false,503 "accepted_answer": false,504 "topic_accepted_answer": true,505 "can_vote": false506 },507 {508 "id": 253861,509 "name": "",510 "username": "T33K3Y",511 "avatar_template": "/letter_avatar_proxy/v4/letter/t/da6949/{size}.png",512 "created_at": "2020-12-28T07:56:41.074Z",513 "cooked": "<p>Got a solution here:</p>\n<aside class=\"quote quote-modified\" data-post=\"5\" data-topic=\"79834\">\n <div class=\"title\">\n <div class=\"quote-controls\"></div>\n <img loading=\"lazy\" alt=\"\" width=\"24\" height=\"24\" src=\"https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/jimmy_hall/48/13872_2.png\" class=\"avatar\">\n <a href=\"https://discuss.pytorch.org/t/index-error-using-custom-backbone-on-fasterrcnn/79834/5\">Index error using custom backbone on FasterRCNN</a> <a class=\"badge-category__wrapper \" href=\"/c/vision/5\"><span data-category-id=\"5\" data-drop-close=\"true\" class=\"badge-category \" title=\"Topics related to either pytorch/vision or vision research related topics\"><span class=\"badge-category__name\">vision</span></span></a>\n </div>\n <blockquote>\n I got the same error following the tutorial exactly, but the FasterRCNN documentation: \n<a href=\"https://github.com/pytorch/vision/blob/master/torchvision/models/detection/faster_rcnn.py\" rel=\"noopener nofollow ugc\">https://github.com/pytorch/vision/blob/master/torchvision/models/detection/faster_rcnn.py</a> \nsuggests that the featmap_names in the MultiScaleROIAlign should be a character, ‘0’, not an integer. I replaced \nroi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],\n output_size=7,\n sampling_ratio=2)\n\nwith \nroi…\n </blockquote>\n</aside>\n",514 "post_number": 2,515 "post_type": 1,516 "posts_count": 2,517 "updated_at": "2021-01-04T16:26:17.161Z",518 "reply_count": 0,519 "reply_to_post_number": null,520 "quote_count": 0,521 "incoming_link_count": 3,522 "reads": 5,523 "readers_count": 4,524 "score": 16.0,525 "yours": false,526 "topic_id": 106873,527 "topic_slug": "if-k-0-and-self-featmap-names-0-shouldnt-k-in-self-featmap-names-be-true-getting-false",528 "display_username": "",529 "primary_group_name": null,530 "flair_name": null,531 "flair_url": null,532 "flair_bg_color": null,533 "flair_color": null,534 "flair_group_id": null,535 "badges_granted": [],536 "version": 1,537 "can_edit": false,538 "can_delete": false,539 "can_recover": false,540 "can_see_hidden_post": false,541 "can_wiki": false,542 "link_counts": [543 {544 "url": 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"Oom error during process in 3d side project",781 "id": 220811,782 "title": "Oom error during process in 3d side project",783 "slug": "oom-error-during-process-in-3d-side-project",784 "posts_count": 1,785 "reply_count": 0,786 "highest_post_number": 1,787 "image_url": null,788 "created_at": "2025-06-14T08:01:18.169Z",789 "last_posted_at": "2025-06-14T08:01:18.214Z",790 "bumped": true,791 "bumped_at": "2025-06-14T08:02:01.738Z",792 "archetype": "regular",793 "unseen": false,794 "pinned": false,795 "unpinned": null,796 "visible": true,797 "closed": false,798 "archived": false,799 "bookmarked": null,800 "liked": null,801 "tags_descriptions": {},802 "like_count": 0,803 "views": 32,804 "category_id": 5,805 "featured_link": null,806 "has_accepted_answer": false,807 "posters": [808 {809 "extras": "latest single",810 "description": "Original Poster, Most Recent Poster",811 "user": {812 "id": 84588,813 "username": "reinforced",814 "name": "꼬리 웰시코기의",815 "avatar_template": 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Getting False",824 "id": 106873,825 "title": "If k = 0 and self.featmap_names=[0] shouldn't k in self.featmap_names be True? Getting False",826 "posts_count": 2,827 "created_at": "2020-12-22T16:47:11.577Z",828 "views": 592,829 "reply_count": 0,830 "like_count": 0,831 "last_posted_at": "2020-12-28T07:56:41.074Z",832 "visible": true,833 "closed": false,834 "archived": false,835 "has_summary": false,836 "archetype": "regular",837 "slug": "if-k-0-and-self-featmap-names-0-shouldnt-k-in-self-featmap-names-be-true-getting-false",838 "category_id": 5,839 "word_count": 355,840 "deleted_at": null,841 "user_id": 40323,842 "featured_link": null,843 "pinned_globally": false,844 "pinned_at": null,845 "pinned_until": null,846 "image_url": null,847 "slow_mode_seconds": 0,848 "draft": null,849 "draft_key": "topic_106873",850 "draft_sequence": null,851 "unpinned": null,852 "pinned": false,853 "current_post_number": 1,854 "highest_post_number": 2,855 "deleted_by": null,856 "actions_summary": [857 {858 "id": 4,859 "count": 0,860 "hidden": false,861 "can_act": false862 },863 {864 "id": 8,865 "count": 0,866 "hidden": false,867 "can_act": false868 },869 {870 "id": 10,871 "count": 0,872 "hidden": false,873 "can_act": false874 },875 {876 "id": 7,877 "count": 0,878 "hidden": false,879 "can_act": false880 }881 ],882 "chunk_size": 20,883 "bookmarked": false,884 "topic_timer": null,885 "message_bus_last_id": 0,886 "participant_count": 1,887 "show_read_indicator": false,888 "thumbnails": null,889 "slow_mode_enabled_until": null,890 "accepted_answer": {891 "post_number": 2,892 "username": "T33K3Y",893 "name": "",894 "excerpt": "Got a solution here:"895 },896 "can_vote": false,897 "vote_count": 0,898 "user_voted": false,899 "discourse_zendesk_plugin_zendesk_id": null,900 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",901 "details": {902 "can_edit": false,903 "notification_level": 1,904 "participants": [905 {906 "id": 40323,907 "username": "T33K3Y",908 "name": "",909 "avatar_template": "/letter_avatar_proxy/v4/letter/t/da6949/{size}.png",910 "post_count": 2,911 "primary_group_name": null,912 "flair_name": null,913 "flair_url": null,914 "flair_color": null,915 "flair_bg_color": null,916 "flair_group_id": null,917 "trust_level": 1918 }919 ],920 "created_by": {921 "id": 40323,922 "username": "T33K3Y",923 "name": "",924 "avatar_template": "/letter_avatar_proxy/v4/letter/t/da6949/{size}.png"925 },926 "last_poster": {927 "id": 40323,928 "username": "T33K3Y",929 "name": "",930 "avatar_template": "/letter_avatar_proxy/v4/letter/t/da6949/{size}.png"931 },932 "links": [933 {934 "url": "https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html#modifying-the-model-to-add-a-different-backbone",935 "title": "TorchVision Object Detection Finetuning Tutorial — PyTorch Tutorials 1.7.1 documentation",936 "internal": false,937 "attachment": false,938 "reflection": false,939 "clicks": 1,940 "user_id": 40323,941 "domain": "pytorch.org",942 "root_domain": "pytorch.org"943 }944 ]945 },946 "bookmarks": []947 },948 {949 "post_stream": {950 "posts": [951 {952 "id": 250565,953 "name": "Preetham R Patlolla",954 "username": "Preetham_R_Patlolla",955 "avatar_template": "/user_avatar/discuss.pytorch.org/preetham_r_patlolla/{size}/30809_2.png",956 "created_at": "2020-12-10T03:46:37.713Z",957 "cooked": "<p>I’ve been trying to develop an Autoencoder model for the task of knowledge representation where my input is a sequence of images. Loss of this model (both training and testing) doesn’t decrease. It is instead fluctuating and of course, my image reconstruction is very poor. I’ve tried the following:</p>\n<ol>\n<li>Changing the learning rate between 1 and 0.001</li>\n<li>Increasing and decreasing the batch size</li>\n<li>With and without dropout layers</li>\n</ol>\n<h1>Encoder-Decoder model</h1>\n<p>class Encoder_Decoder(nn.Module):</p>\n<pre><code>def __init__(self):\n super(Encoder_Decoder, self).__init__()\n \n #Encoder\n self.encoder = nn.Sequential(nn.Conv3d(in_channels=3, out_channels=16, kernel_size=(3, 3, 3), padding=(0,0,0)), \n nn.ReLU(), nn.BatchNorm3d(16), \n nn.MaxPool3d(kernel_size=(1,2,2)),\n nn.Conv3d(in_channels=16, out_channels=64, kernel_size=(3, 3, 3), padding=(0,1,1)), \n nn.ReLU(), nn.BatchNorm3d(64), \n nn.MaxPool3d(kernel_size=(2,2,2)),\n nn.Conv3d(in_channels=64, out_channels=256, kernel_size=(3, 3, 3), padding=(1,1,1)), \n nn.ReLU(), nn.BatchNorm3d(256), \n nn.MaxPool3d(kernel_size=(2,2,2)),\n nn.Conv3d(in_channels=256, out_channels=64, kernel_size=(3,3,3),padding=(1,1,1)), \n nn.ReLU(), nn.BatchNorm3d(64), \n nn.MaxPool3d(kernel_size=(2,2,2)),\n nn.Conv3d(in_channels=64, out_channels=16, kernel_size=(1,1,1), padding=(0,0,0)), \n nn.ReLU(), nn.BatchNorm3d(16), \n nn.MaxPool3d(kernel_size=(2,1,1)),\n nn.Conv3d(in_channels=16, out_channels=4, kernel_size=(1,1,1), padding=(0,0,0)), \n nn.ReLU(), nn.BatchNorm3d(4), \n nn.MaxPool3d(kernel_size=(1,1,1)))\n \n \n #Decoder\n self.decoder = nn.Sequential(nn.ConvTranspose3d(in_channels=4, out_channels=16, kernel_size=3), \n nn.ReLU(), \n nn.Upsample(scale_factor=(2,2,2)),\n nn.ConvTranspose3d(in_channels=16, out_channels=64, kernel_size=3), \n nn.ReLU(), \n nn.Upsample(scale_factor=(2,2,2)),\n nn.ConvTranspose3d(in_channels=64, out_channels=256, kernel_size=3), \n nn.ReLU(), \n nn.Upsample(scale_factor=(2,2,2)),\n nn.ConvTranspose3d(in_channels=256, out_channels=64, kernel_size=3), \n nn.ReLU(), \n nn.Upsample(scale_factor=(2,2,2)),\n nn.ConvTranspose3d(in_channels=64, out_channels=16, kernel_size=1), \n nn.ReLU(), \n nn.Upsample(size=(22,28,28)),\n nn.ConvTranspose3d(in_channels=16, out_channels=3, kernel_size=1), \n nn.ReLU(), \n nn.Upsample(size=(22,28,28)))\n \ndef forward(self,x):\n\n # x has the shapw (16,22,3,28,28)\n\n \n x1 = self.encoder(x)\n # x1 has the shape (16,4,1,1,1)\n\n \n x2 = self.decoder(x1)\n # x2 has the shape (16,22,3,28,28)\n\n return x1, x2\n</code></pre>\n<p>Train:</p>\n<p>def train(model, trainloader, criterion, optimizer, epoch):</p>\n<pre><code>model.train()\n\n \n\nfor batch_idx, inputs in enumerate(trainloader):\n \n\n inputs = inputs.float()\n\n \n\n if torch.cuda.is_available():\n\n inputs = inputs.to(\"cuda\")\n\n optimizer.zero_grad()\n\n encoded_vectors,outputs = model(inputs)\n\n loss = criterion(outputs,inputs)\n\n loss.backward()\n\n optimizer.step()\n\n if batch_idx % 50 == 0:\n\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n\n epoch, batch_idx * len(inputs), len(trainloader.dataset),\n\n 100. * batch_idx / len(trainloader), loss.item()))\n</code></pre>\n<p>test:</p>\n<p>def test(model, criterion1, testloader):</p>\n<pre><code>model.eval()\n\ntest_loss = 0\n\nfor batch_idx, inputs in enumerate(testloader):\n\n if torch.cuda.is_available():\n\n inputs = inputs.cuda()\n\n encoded_vectors,outputs = model(inputs)\n\n loss = criterion(outputs,inputs)\n\n test_loss += loss.item() * inputs.shape[0]\n\ntest_loss /= len(testloader.dataset)\n\nprint('\\nTest set: Average loss: {:.4f}\\n'.format(test_loss))\n\nif abs(test_loss) <= 0.005:\n\n return True\n\nelse:\n\n return False\n</code></pre>\n<p>def main():</p>\n<pre><code>model = Encoder_Decoder()\n\n\n\nmodel = model.to('cuda')\n\ncriterion = torch.nn.MSELoss()\n\noptimizer = optim.AdamW(model.parameters(), lr=0.005, eps=1e-3, amsgrad=False)\n\nfor epoch in range(20):\n\n train(model, train_loader, criterion, optimizer, epoch)\n\n test(model, criterion, test_loader)\n \n\nreturn model\n</code></pre>\n<p>if <strong>name</strong> == “<strong>main</strong>”:</p>\n<pre><code>model = main()</code></pre>",958 "post_number": 1,959 "post_type": 1,960 "posts_count": 6,961 "updated_at": "2020-12-10T05:53:16.941Z",962 "reply_count": 0,963 "reply_to_post_number": null,964 "quote_count": 0,965 "incoming_link_count": 369,966 "reads": 20,967 "readers_count": 19,968 "score": 1849.0,969 "yours": false,970 "topic_id": 105721,971 "topic_slug": "loss-doesnt-decrease",972 "display_username": "Preetham R Patlolla",973 "primary_group_name": null,974 "flair_name": null,975 "flair_url": null,976 "flair_bg_color": null,977 "flair_color": null,978 "flair_group_id": null,979 "badges_granted": [],980 "version": 4,981 "can_edit": false,982 "can_delete": false,983 "can_recover": false,984 "can_see_hidden_post": false,985 "can_wiki": false,986 "read": true,987 "user_title": null,988 "bookmarked": false,989 "actions_summary": [],990 "moderator": false,991 "admin": false,992 "staff": false,993 "user_id": 38694,994 "hidden": false,995 "trust_level": 2,996 "deleted_at": null,997 "user_deleted": false,998 "edit_reason": null,999 "can_view_edit_history": true,1000 "wiki": false,1001 "post_url": "/t/loss-doesnt-decrease/105721/1",1002 "can_accept_answer": false,1003 "can_unaccept_answer": false,1004 "accepted_answer": false,1005 "topic_accepted_answer": true,1006 "can_vote": false1007 },1008 {1009 "id": 250576,1010 "name": "Preetham R Patlolla",1011 "username": "Preetham_R_Patlolla",1012 "avatar_template": "/user_avatar/discuss.pytorch.org/preetham_r_patlolla/{size}/30809_2.png",1013 "created_at": "2020-12-10T05:38:41.108Z",1014 "cooked": "<p>I have also tried experimenting different loss functions and different dimensions for the latent space (encoding vector) but nothing worked out and stuck at this part for more than 2 weeks. <a class=\"mention\" href=\"/u/ptrblck\">@ptrblck</a> Kindly address this. Many thanks in advance.</p>",1015 "post_number": 2,1016 "post_type": 1,1017 "posts_count": 6,1018 "updated_at": "2020-12-10T05:39:27.701Z",1019 "reply_count": 1,1020 "reply_to_post_number": null,1021 "quote_count": 0,1022 "incoming_link_count": 2,1023 "reads": 17,1024 "readers_count": 16,1025 "score": 18.4,1026 "yours": false,1027 "topic_id": 105721,1028 "topic_slug": "loss-doesnt-decrease",1029 "display_username": "Preetham R Patlolla",1030 "primary_group_name": null,1031 "flair_name": null,1032 "flair_url": null,1033 "flair_bg_color": null,1034 "flair_color": null,1035 "flair_group_id": null,1036 "badges_granted": [],1037 "version": 1,1038 "can_edit": false,1039 "can_delete": false,1040 "can_recover": false,1041 "can_see_hidden_post": false,1042 "can_wiki": false,1043 "read": true,1044 "user_title": null,1045 "bookmarked": false,1046 "actions_summary": [],1047 "moderator": false,1048 "admin": false,1049 "staff": false,1050 "user_id": 38694,1051 "hidden": false,1052 "trust_level": 2,1053 "deleted_at": null,1054 "user_deleted": false,1055 "edit_reason": null,1056 "can_view_edit_history": true,1057 "wiki": false,1058 "post_url": "/t/loss-doesnt-decrease/105721/2",1059 "can_accept_answer": false,1060 "can_unaccept_answer": false,1061 "accepted_answer": false,1062 "topic_accepted_answer": true1063 },1064 {1065 "id": 250830,1066 "name": "Preetham R Patlolla",1067 "username": "Preetham_R_Patlolla",1068 "avatar_template": "/user_avatar/discuss.pytorch.org/preetham_r_patlolla/{size}/30809_2.png",1069 "created_at": "2020-12-11T07:55:08.791Z",1070 "cooked": "<p>This is how my loss looks like:</p>\n<p>Train Epoch: 0 [0/2000 (0%)]\tLoss: 0.890425<br>\nTrain Epoch: 0 [400/2000 (20%)]\tLoss: 0.739331<br>\nTrain Epoch: 0 [800/2000 (40%)]\tLoss: 0.740495<br>\nTrain Epoch: 0 [1200/2000 (60%)]\tLoss: 0.737210<br>\nTrain Epoch: 0 [1600/2000 (80%)]\tLoss: 0.742648</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 1 [0/2000 (0%)]\tLoss: 0.738974<br>\nTrain Epoch: 1 [400/2000 (20%)]\tLoss: 0.739232<br>\nTrain Epoch: 1 [800/2000 (40%)]\tLoss: 0.737139<br>\nTrain Epoch: 1 [1200/2000 (60%)]\tLoss: 0.736564<br>\nTrain Epoch: 1 [1600/2000 (80%)]\tLoss: 0.737216</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 2 [0/2000 (0%)]\tLoss: 0.737539<br>\nTrain Epoch: 2 [400/2000 (20%)]\tLoss: 0.737013<br>\nTrain Epoch: 2 [800/2000 (40%)]\tLoss: 0.745166<br>\nTrain Epoch: 2 [1200/2000 (60%)]\tLoss: 0.740684<br>\nTrain Epoch: 2 [1600/2000 (80%)]\tLoss: 0.735286</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 3 [0/2000 (0%)]\tLoss: 0.738531<br>\nTrain Epoch: 3 [400/2000 (20%)]\tLoss: 0.741940<br>\nTrain Epoch: 3 [800/2000 (40%)]\tLoss: 0.737881<br>\nTrain Epoch: 3 [1200/2000 (60%)]\tLoss: 0.737666<br>\nTrain Epoch: 3 [1600/2000 (80%)]\tLoss: 0.739561</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 4 [0/2000 (0%)]\tLoss: 0.741185<br>\nTrain Epoch: 4 [400/2000 (20%)]\tLoss: 0.736491<br>\nTrain Epoch: 4 [800/2000 (40%)]\tLoss: 0.741242<br>\nTrain Epoch: 4 [1200/2000 (60%)]\tLoss: 0.738887<br>\nTrain Epoch: 4 [1600/2000 (80%)]\tLoss: 0.738182</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 5 [0/2000 (0%)]\tLoss: 0.738633<br>\nTrain Epoch: 5 [400/2000 (20%)]\tLoss: 0.741287<br>\nTrain Epoch: 5 [800/2000 (40%)]\tLoss: 0.744609<br>\nTrain Epoch: 5 [1200/2000 (60%)]\tLoss: 0.738787<br>\nTrain Epoch: 5 [1600/2000 (80%)]\tLoss: 0.742097</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 6 [0/2000 (0%)]\tLoss: 0.737597<br>\nTrain Epoch: 6 [400/2000 (20%)]\tLoss: 0.738081<br>\nTrain Epoch: 6 [800/2000 (40%)]\tLoss: 0.734609<br>\nTrain Epoch: 6 [1200/2000 (60%)]\tLoss: 0.738837<br>\nTrain Epoch: 6 [1600/2000 (80%)]\tLoss: 0.739030</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 7 [0/2000 (0%)]\tLoss: 0.742665<br>\nTrain Epoch: 7 [400/2000 (20%)]\tLoss: 0.737820<br>\nTrain Epoch: 7 [800/2000 (40%)]\tLoss: 0.740105<br>\nTrain Epoch: 7 [1200/2000 (60%)]\tLoss: 0.734893<br>\nTrain Epoch: 7 [1600/2000 (80%)]\tLoss: 0.740252</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 8 [0/2000 (0%)]\tLoss: 0.740616<br>\nTrain Epoch: 8 [400/2000 (20%)]\tLoss: 0.741398<br>\nTrain Epoch: 8 [800/2000 (40%)]\tLoss: 0.740078<br>\nTrain Epoch: 8 [1200/2000 (60%)]\tLoss: 0.739548<br>\nTrain Epoch: 8 [1600/2000 (80%)]\tLoss: 0.739944</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 9 [0/2000 (0%)]\tLoss: 0.739905<br>\nTrain Epoch: 9 [400/2000 (20%)]\tLoss: 0.738651<br>\nTrain Epoch: 9 [800/2000 (40%)]\tLoss: 0.737514<br>\nTrain Epoch: 9 [1200/2000 (60%)]\tLoss: 0.734963<br>\nTrain Epoch: 9 [1600/2000 (80%)]\tLoss: 0.735477</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 10 [0/2000 (0%)]\tLoss: 0.736167<br>\nTrain Epoch: 10 [400/2000 (20%)]\tLoss: 0.738089<br>\nTrain Epoch: 10 [800/2000 (40%)]\tLoss: 0.733951<br>\nTrain Epoch: 10 [1200/2000 (60%)]\tLoss: 0.738294<br>\nTrain Epoch: 10 [1600/2000 (80%)]\tLoss: 0.739406</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 11 [0/2000 (0%)]\tLoss: 0.739151<br>\nTrain Epoch: 11 [400/2000 (20%)]\tLoss: 0.737162<br>\nTrain Epoch: 11 [800/2000 (40%)]\tLoss: 0.737700<br>\nTrain Epoch: 11 [1200/2000 (60%)]\tLoss: 0.738951<br>\nTrain Epoch: 11 [1600/2000 (80%)]\tLoss: 0.736479</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 12 [0/2000 (0%)]\tLoss: 0.738607<br>\nTrain Epoch: 12 [400/2000 (20%)]\tLoss: 0.742146<br>\nTrain Epoch: 12 [800/2000 (40%)]\tLoss: 0.740505<br>\nTrain Epoch: 12 [1200/2000 (60%)]\tLoss: 0.735908<br>\nTrain Epoch: 12 [1600/2000 (80%)]\tLoss: 0.742282</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 13 [0/2000 (0%)]\tLoss: 0.736525<br>\nTrain Epoch: 13 [400/2000 (20%)]\tLoss: 0.736685<br>\nTrain Epoch: 13 [800/2000 (40%)]\tLoss: 0.734824<br>\nTrain Epoch: 13 [1200/2000 (60%)]\tLoss: 0.740992<br>\nTrain Epoch: 13 [1600/2000 (80%)]\tLoss: 0.738559</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 14 [0/2000 (0%)]\tLoss: 0.735638<br>\nTrain Epoch: 14 [400/2000 (20%)]\tLoss: 0.737805<br>\nTrain Epoch: 14 [800/2000 (40%)]\tLoss: 0.741408<br>\nTrain Epoch: 14 [1200/2000 (60%)]\tLoss: 0.731682<br>\nTrain Epoch: 14 [1600/2000 (80%)]\tLoss: 0.738875</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 15 [0/2000 (0%)]\tLoss: 0.740533<br>\nTrain Epoch: 15 [400/2000 (20%)]\tLoss: 0.737641<br>\nTrain Epoch: 15 [800/2000 (40%)]\tLoss: 0.738011<br>\nTrain Epoch: 15 [1200/2000 (60%)]\tLoss: 0.741101<br>\nTrain Epoch: 15 [1600/2000 (80%)]\tLoss: 0.739203</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 16 [0/2000 (0%)]\tLoss: 0.741356<br>\nTrain Epoch: 16 [400/2000 (20%)]\tLoss: 0.739178<br>\nTrain Epoch: 16 [800/2000 (40%)]\tLoss: 0.737916<br>\nTrain Epoch: 16 [1200/2000 (60%)]\tLoss: 0.743919<br>\nTrain Epoch: 16 [1600/2000 (80%)]\tLoss: 0.736833</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 17 [0/2000 (0%)]\tLoss: 0.739630<br>\nTrain Epoch: 17 [400/2000 (20%)]\tLoss: 0.739462<br>\nTrain Epoch: 17 [800/2000 (40%)]\tLoss: 0.741527<br>\nTrain Epoch: 17 [1200/2000 (60%)]\tLoss: 0.733570<br>\nTrain Epoch: 17 [1600/2000 (80%)]\tLoss: 0.741055</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 18 [0/2000 (0%)]\tLoss: 0.739831<br>\nTrain Epoch: 18 [400/2000 (20%)]\tLoss: 0.740010<br>\nTrain Epoch: 18 [800/2000 (40%)]\tLoss: 0.736455<br>\nTrain Epoch: 18 [1200/2000 (60%)]\tLoss: 0.737576<br>\nTrain Epoch: 18 [1600/2000 (80%)]\tLoss: 0.736869</p>\n<p>Test set: Average loss: 0.7384</p>\n<p>Train Epoch: 19 [0/2000 (0%)]\tLoss: 0.739427<br>\nTrain Epoch: 19 [400/2000 (20%)]\tLoss: 0.740225<br>\nTrain Epoch: 19 [800/2000 (40%)]\tLoss: 0.743299<br>\nTrain Epoch: 19 [1200/2000 (60%)]\tLoss: 0.737947<br>\nTrain Epoch: 19 [1600/2000 (80%)]\tLoss: 0.735534</p>\n<p>Test set: Average loss: 0.738</p>",1071 "post_number": 3,1072 "post_type": 1,1073 "posts_count": 6,1074 "updated_at": "2020-12-11T07:55:08.791Z",1075 "reply_count": 0,1076 "reply_to_post_number": 2,1077 "quote_count": 0,1078 "incoming_link_count": 5,1079 "reads": 14,1080 "readers_count": 13,1081 "score": 27.8,1082 "yours": false,1083 "topic_id": 105721,1084 "topic_slug": "loss-doesnt-decrease",1085 "display_username": "Preetham R Patlolla",1086 "primary_group_name": null,1087 "flair_name": null,1088 "flair_url": null,1089 "flair_bg_color": null,1090 "flair_color": null,1091 "flair_group_id": null,1092 "badges_granted": [],1093 "version": 1,1094 "can_edit": false,1095 "can_delete": false,1096 "can_recover": false,1097 "can_see_hidden_post": false,1098 "can_wiki": false,1099 "read": true,1100 "user_title": null,1101 "reply_to_user": {1102 "id": 38694,1103 "username": "Preetham_R_Patlolla",1104 "name": "Preetham R Patlolla",1105 "avatar_template": "/user_avatar/discuss.pytorch.org/preetham_r_patlolla/{size}/30809_2.png"1106 },1107 "bookmarked": false,1108 "actions_summary": [],1109 "moderator": false,1110 "admin": false,1111 "staff": false,1112 "user_id": 38694,1113 "hidden": false,1114 "trust_level": 2,1115 "deleted_at": null,1116 "user_deleted": false,1117 "edit_reason": null,1118 "can_view_edit_history": true,1119 "wiki": false,1120 "post_url": "/t/loss-doesnt-decrease/105721/3",1121 "can_accept_answer": false,1122 "can_unaccept_answer": false,1123 "accepted_answer": false,1124 "topic_accepted_answer": true1125 },1126 {1127 "id": 250833,1128 "name": "Preetham R Patlolla",1129 "username": "Preetham_R_Patlolla",1130 "avatar_template": "/user_avatar/discuss.pytorch.org/preetham_r_patlolla/{size}/30809_2.png",1131 "created_at": "2020-12-11T07:56:43.611Z",1132 "cooked": "<p>Am I doing some obvious blunder? or Is it possible that my data has no patterns or valid features to learn?</p>",1133 "post_number": 4,1134 "post_type": 1,1135 "posts_count": 6,1136 "updated_at": "2020-12-11T07:56:43.611Z",1137 "reply_count": 1,1138 "reply_to_post_number": null,1139 "quote_count": 0,1140 "incoming_link_count": 2,1141 "reads": 15,1142 "readers_count": 14,1143 "score": 18.0,1144 "yours": false,1145 "topic_id": 105721,1146 "topic_slug": "loss-doesnt-decrease",1147 "display_username": "Preetham R Patlolla",1148 "primary_group_name": null,1149 "flair_name": null,1150 "flair_url": null,1151 "flair_bg_color": null,1152 "flair_color": null,1153 "flair_group_id": null,1154 "badges_granted": [],1155 "version": 1,1156 "can_edit": false,1157 "can_delete": false,1158 "can_recover": false,1159 "can_see_hidden_post": false,1160 "can_wiki": false,1161 "read": true,1162 "user_title": null,1163 "bookmarked": false,1164 "actions_summary": [],1165 "moderator": false,1166 "admin": false,1167 "staff": false,1168 "user_id": 38694,1169 "hidden": false,1170 "trust_level": 2,1171 "deleted_at": null,1172 "user_deleted": false,1173 "edit_reason": null,1174 "can_view_edit_history": true,1175 "wiki": false,1176 "post_url": "/t/loss-doesnt-decrease/105721/4",1177 "can_accept_answer": false,1178 "can_unaccept_answer": false,1179 "accepted_answer": false,1180 "topic_accepted_answer": true1181 },1182 {1183 "id": 250877,1184 "name": "",1185 "username": "ptrblck",1186 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",1187 "created_at": "2020-12-11T09:55:33.751Z",1188 "cooked": "<p>You could start by overfitting a small dataset (e.g. just 10 samples) by playing around with the hyperparameters (optimizer, learning rate etc.).<br>\nIf that doesn’t help, I would suggest to simplify the model.<br>\nOnce your can come up with a model and hyperparameters, which overfit this small dataset, you could scale up the use case again by using more data.</p>",1189 "post_number": 5,1190 "post_type": 1,1191 "posts_count": 6,1192 "updated_at": "2020-12-28T05:08:03.530Z",1193 "reply_count": 1,1194 "reply_to_post_number": 4,1195 "quote_count": 0,1196 "incoming_link_count": 4,1197 "reads": 15,1198 "readers_count": 14,1199 "score": 43.0,1200 "yours": false,