Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 275982,7 "name": "jpj",8 "username": "jpj",9 "avatar_template": "/letter_avatar_proxy/v4/letter/j/ed655f/{size}.png",10 "created_at": "2021-04-08T13:03:38.342Z",11 "cooked": "<pre><code class=\"lang-auto\"> # layers\n self.hidden_layer_1 = torch.nn.Linear(80,100)\n self.hidden_layer_2 = torch.nn.Linear(100,100)\n self.output = torch.nn.Linear(100,15)\n</code></pre>\n<pre><code class=\"lang-auto\"> def forward(self, input):\n H1 = self.hidden_layer_1(input)\n H1 = self.ReLU(H1)\n H2 = self.hidden_layer_2(H1)\n H2 = self.ReLU(H2)\n final_inputs = self.output(H2)\n # not applying activation on final_inputs because CrossEntropyLoss does that\n return final_inputs\n</code></pre>\n<p>I have a feed forward neural network with 80 input features, two hidden layers with 100 nodes each and 15 outputs(one for each class).</p>\n<p>I save weights from a restricted boltzmann machine on tensor flow in a <em>.pkl</em> file.</p>\n<p>How do I initialise the above code with these weights?</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 2,15 "updated_at": "2021-04-08T13:11:49.396Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 127,20 "reads": 7,21 "readers_count": 6,22 "score": 636.4,23 "yours": false,24 "topic_id": 117511,25 "topic_slug": "how-to-initialise-weights-for-the-first-layer",26 "display_username": "jpj",27 "primary_group_name": null,28 "flair_name": null,29 "flair_url": null,30 "flair_bg_color": null,31 "flair_color": null,32 "flair_group_id": null,33 "badges_granted": [],34 "version": 2,35 "can_edit": false,36 "can_delete": false,37 "can_recover": false,38 "can_see_hidden_post": false,39 "can_wiki": false,40 "read": true,41 "user_title": null,42 "bookmarked": false,43 "actions_summary": [],44 "moderator": false,45 "admin": false,46 "staff": false,47 "user_id": 42234,48 "hidden": false,49 "trust_level": 1,50 "deleted_at": null,51 "user_deleted": false,52 "edit_reason": null,53 "can_view_edit_history": true,54 "wiki": false,55 "post_url": "/t/how-to-initialise-weights-for-the-first-layer/117511/1",56 "can_accept_answer": false,57 "can_unaccept_answer": false,58 "accepted_answer": false,59 "topic_accepted_answer": null,60 "can_vote": false61 },62 {63 "id": 276127,64 "name": "",65 "username": "ptrblck",66 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",67 "created_at": "2021-04-09T06:54:34.150Z",68 "cooked": "<p>Assuming you’ve stored the TF weights as numpy arrays, you could take a look at <a href=\"https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb\">this notebook</a>, which <a class=\"mention\" href=\"/u/tom\">@tom</a> and I created to port the StyleGAN parameters to our PyTorch implementation.</p>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 2,72 "updated_at": "2021-04-09T06:54:34.150Z",73 "reply_count": 0,74 "reply_to_post_number": null,75 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1,509 "primary_group_name": null,510 "flair_name": null,511 "flair_url": null,512 "flair_color": null,513 "flair_bg_color": null,514 "flair_group_id": null,515 "trust_level": 1516 }517 ],518 "created_by": {519 "id": 42234,520 "username": "jpj",521 "name": "jpj",522 "avatar_template": "/letter_avatar_proxy/v4/letter/j/ed655f/{size}.png"523 },524 "last_poster": {525 "id": 3534,526 "username": "ptrblck",527 "name": "",528 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png"529 },530 "links": [531 {532 "url": "https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb",533 "title": "lernapparat/pytorch_style_gan.ipynb at master · lernapparat/lernapparat · GitHub",534 "internal": false,535 "attachment": false,536 "reflection": false,537 "clicks": 15,538 "user_id": 3534,539 "domain": "github.com",540 "root_domain": "github.com"541 }542 ]543 },544 "bookmarks": []545 },546 {547 "post_stream": {548 "posts": [549 {550 "id": 276058,551 "name": "Moinudin Gallotta",552 "username": "moinudin",553 "avatar_template": "/user_avatar/discuss.pytorch.org/moinudin/{size}/36842_2.png",554 "created_at": "2021-04-08T21:39:36.817Z",555 "cooked": "<p>I have seen tutorials use <code>x.to(device)</code>, which I understand sends <code>x</code> to the computation device (cpu/gpu) and I have seen others use <code>Variable(x)</code>, which I understand is for <code>autograd</code>, without calling <code>.to(device)</code>. Does <code>x.to(device)</code> implicitly create an autograd variable? Or can you use <code>Variable(x).to(device)</code>?</p>",556 "post_number": 1,557 "post_type": 1,558 "posts_count": 2,559 "updated_at": "2021-04-08T21:39:36.817Z",560 "reply_count": 0,561 "reply_to_post_number": null,562 "quote_count": 0,563 "incoming_link_count": 1325,564 "reads": 19,565 "readers_count": 18,566 "score": 6623.8,567 "yours": false,568 "topic_id": 117551,569 "topic_slug": "x-to-device-vs-variable-x",570 "display_username": "Moinudin Gallotta",571 "primary_group_name": null,572 "flair_name": null,573 "flair_url": null,574 "flair_bg_color": null,575 "flair_color": null,576 "flair_group_id": null,577 "badges_granted": [],578 "version": 1,579 "can_edit": false,580 "can_delete": false,581 "can_recover": false,582 "can_see_hidden_post": false,583 "can_wiki": false,584 "read": true,585 "user_title": null,586 "bookmarked": false,587 "actions_summary": [],588 "moderator": false,589 "admin": false,590 "staff": false,591 "user_id": 44024,592 "hidden": false,593 "trust_level": 1,594 "deleted_at": null,595 "user_deleted": false,596 "edit_reason": null,597 "can_view_edit_history": true,598 "wiki": false,599 "post_url": "/t/x-to-device-vs-variable-x/117551/1",600 "can_accept_answer": false,601 "can_unaccept_answer": false,602 "accepted_answer": false,603 "topic_accepted_answer": null,604 "can_vote": false605 },606 {607 "id": 276115,608 "name": "",609 "username": "ptrblck",610 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",611 "created_at": "2021-04-09T05:32:16.580Z",612 "cooked": "<p><code>Variable</code>s are deprecated since PyTorch <code>0.4</code> and you should use tensors now.<br>\nAutograd is able to track operations of tensors, if they require gradients, so there is no need to use the tensor vs. <code>Variable</code> split anymore.</p>",613 "post_number": 2,614 "post_type": 1,615 "posts_count": 2,616 "updated_at": "2021-04-09T05:32:16.580Z",617 "reply_count": 0,618 "reply_to_post_number": null,619 "quote_count": 0,620 "incoming_link_count": 12,621 "reads": 13,622 "readers_count": 12,623 "score": 77.6,624 "yours": false,625 "topic_id": 117551,626 "topic_slug": "x-to-device-vs-variable-x",627 "display_username": "",628 "primary_group_name": null,629 "flair_name": null,630 "flair_url": null,631 "flair_bg_color": null,632 "flair_color": null,633 "flair_group_id": null,634 "badges_granted": [],635 "version": 1,636 "can_edit": false,637 "can_delete": false,638 "can_recover": false,639 "can_see_hidden_post": false,640 "can_wiki": false,641 "read": true,642 "user_title": "",643 "bookmarked": false,644 "actions_summary": [645 {646 "id": 2,647 "count": 1648 }649 ],650 "moderator": true,651 "admin": true,652 "staff": true,653 "user_id": 3534,654 "hidden": false,655 "trust_level": 2,656 "deleted_at": null,657 "user_deleted": false,658 "edit_reason": null,659 "can_view_edit_history": true,660 "wiki": false,661 "post_url": "/t/x-to-device-vs-variable-x/117551/2",662 "can_accept_answer": false,663 "can_unaccept_answer": false,664 "accepted_answer": false,665 "topic_accepted_answer": null666 }667 ],668 "stream": [669 276058,670 276115671 ]672 },673 "timeline_lookup": [674 [675 1,676 1661677 ]678 ],679 "suggested_topics": [680 {681 "fancy_title": "RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [], which is output 0 of PermuteBackward, is at version 3; expected version 0 instead",682 "id": 217071,683 "title": "RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [], which is output 0 of PermuteBackward, is at version 3; expected version 0 instead",684 "slug": "runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation-torch-cuda-floattensor-which-is-output-0-of-permutebackward-is-at-version-3-expected-version-0-instead",685 "posts_count": 3,686 "reply_count": 1,687 "highest_post_number": 3,688 "image_url": null,689 "created_at": "2025-02-24T03:04:56.489Z",690 "last_posted_at": "2025-02-27T16:03:51.563Z",691 "bumped": true,692 "bumped_at": "2025-02-27T16:03:51.563Z",693 "archetype": "regular",694 "unseen": false,695 "pinned": false,696 "unpinned": null,697 "visible": true,698 "closed": false,699 "archived": false,700 "bookmarked": null,701 "liked": null,702 "tags_descriptions": {},703 "like_count": 1,704 "views": 47,705 "category_id": 1,706 "featured_link": null,707 "has_accepted_answer": true,708 "posters": [709 {710 "extras": "latest",711 "description": "Original Poster, Most Recent Poster",712 "user": {713 "id": 82881,714 "username": "Zhouker",715 "name": "",716 "avatar_template": "/letter_avatar_proxy/v4/letter/z/87869e/{size}.png",717 "trust_level": 1718 }719 },720 {721 "extras": null,722 "description": "Frequent Poster, Accepted Answer",723 "user": {724 "id": 18088,725 "username": "KFrank",726 "name": "K. 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Where W1 and W2 are let’s say two randomly generated weights. I want to freeze W1, W2 and only update a, and b through the training. I’m wondering how can I implement this in Pytorch?</p>\n<p>Thanks a lot,</p>",1085 "post_number": 1,1086 "post_type": 1,1087 "posts_count": 2,1088 "updated_at": "2021-04-08T21:22:00.704Z",1089 "reply_count": 1,1090 "reply_to_post_number": null,1091 "quote_count": 0,1092 "incoming_link_count": 7,1093 "reads": 8,1094 "readers_count": 7,1095 "score": 41.6,1096 "yours": false,1097 "topic_id": 117549,1098 "topic_slug": "calculating-gradients-w-r-t-another-definied-parameter",1099 "display_username": "mahdi morafah",1100 "primary_group_name": null,1101 "flair_name": null,1102 "flair_url": null,1103 "flair_bg_color": null,1104 "flair_color": null,1105 "flair_group_id": null,1106 "badges_granted": [],1107 "version": 1,1108 "can_edit": false,1109 "can_delete": false,1110 "can_recover": false,1111 "can_see_hidden_post": false,1112 "can_wiki": false,1113 "read": true,1114 "user_title": null,1115 "bookmarked": false,1116 "actions_summary": [],1117 "moderator": false,1118 "admin": false,1119 "staff": false,1120 "user_id": 35725,1121 "hidden": false,1122 "trust_level": 1,1123 "deleted_at": null,1124 "user_deleted": false,1125 "edit_reason": null,1126 "can_view_edit_history": true,1127 "wiki": false,1128 "post_url": "/t/calculating-gradients-w-r-t-another-definied-parameter/117549/1",1129 "can_accept_answer": false,1130 "can_unaccept_answer": false,1131 "accepted_answer": false,1132 "topic_accepted_answer": null,1133 "can_vote": false1134 },1135 {1136 "id": 276098,1137 "name": "K. Frank",1138 "username": "KFrank",1139 "avatar_template": "/letter_avatar_proxy/v4/letter/k/ecb155/{size}.png",1140 "created_at": "2021-04-09T03:43:20.725Z",1141 "cooked": "<p>Hi Mahdi!</p>\n<aside class=\"quote no-group\" data-username=\"mahdi_morafah\" data-post=\"1\" data-topic=\"117549\" data-full=\"true\">\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/mahdi_morafah/48/27971_2.png\" class=\"avatar\"> mahdi_morafah:</div>\n<blockquote>\n<p>I want to freeze W1, W2 and only update a, and b through the training.</p>\n</blockquote>\n</aside>\n<p>Let me assume that <code>W1</code>, <code>W2</code>, <code>a</code>, and <code>b</code> are some sort of pytorch<br>\ntensors (and that your “.” represents some sort of multiplication).</p>\n<p>Simply set</p>\n<pre data-code-wrap=\"python\"><code class=\"lang-python\">W1.requires_grad = False\nW2.requires_grad = False\na.requires_grad = True\nb.requires_grad = True\n</code></pre>\n<p>When you backpropagate, pytorch will only calculate gradients<br>\nfor <code>a</code> and <code>b</code>, treating <code>W1</code> and <code>W2</code> as fixed (not trained) parameters.</p>\n<p>Best.</p>\n<p>K. Frank</p>",1142 "post_number": 2,1143 "post_type": 1,1144 "posts_count": 2,1145 "updated_at": "2021-04-09T03:43:20.725Z",1146 "reply_count": 0,1147 "reply_to_post_number": null,1148 "quote_count": 1,1149 "incoming_link_count": 0,1150 "reads": 5,1151 "readers_count": 4,1152 "score": 1.0,1153 "yours": false,1154 "topic_id": 117549,1155 "topic_slug": "calculating-gradients-w-r-t-another-definied-parameter",1156 "display_username": "K. 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