{"id":2102,"date":"2025-03-30T10:49:22","date_gmt":"2025-03-30T07:49:22","guid":{"rendered":"http:\/\/csnotes.ru\/?p=2102"},"modified":"2025-03-31T10:08:56","modified_gmt":"2025-03-31T07:08:56","slug":"%d0%b1%d0%b0%d0%b7%d0%be%d0%b2%d1%8b%d0%b9-%d0%ba%d0%be%d0%b4-pytorch-%d0%b4%d0%bb%d1%8f-%d1%81%d0%be%d0%b7%d0%b4%d0%b0%d0%bd%d0%b8%d1%8f-%d0%bd%d0%b5%d0%b9%d1%80%d0%be%d1%81%d0%b5%d1%82%d0%b8","status":"publish","type":"post","link":"https:\/\/csnotes.ru\/?p=2102","title":{"rendered":"\u041f\u043e\u043b\u043d\u043e\u0441\u0432\u044f\u0437\u043d\u0430\u044f \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u044c \u043d\u0430 Pytorch &#8211; \u0431\u0430\u0437\u043e\u0432\u0430\u044f \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430 \u043a\u043e\u0434\u0430 \u043d\u0430 \u043f\u0440\u043e\u0441\u0442\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">\u041d\u0438\u0436\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0448\u0430\u0431\u043b\u043e\u043d \u043a\u043e\u0434\u0430 \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0412 \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0435\u043d\u043e \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0434\u0432\u0443\u0445 \u043f\u043e\u043b\u043d\u043e\u0441\u0432\u044f\u0437\u043d\u044b\u0445 \u0441\u043b\u043e\u0435\u0432.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import optim\nfrom random import randint\n\n\n# \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u043a\u043b\u0430\u0441\u0441\u0430 \u0434\u043b\u044f \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u0438\u0437 \u0434\u0432\u0443\u0445 \u0441\u043b\u043e\u0435\u0432\nclass NetGirl(nn.Module):\n    def __init__(self, input_dim, num_hidden, output_dim):\n        super().__init__()\n        self.layer1 = nn.Linear(input_dim, num_hidden)\n        self.layer2 = nn.Linear(num_hidden, output_dim)\n\n    def forward(self, x):\n        x = self.layer1(x) # \u041f\u0440\u043e\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u0432\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0447\u0435\u0440\u0435\u0437 \u043f\u0435\u0440\u0432\u044b\u0439 \u0441\u043b\u043e\u0439\n        x = F.tanh(x)      # \u041f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u043a\u0430\u043b. \u043f\u0440\u043e\u0438\u0437\u0432\u0435\u0434\u0435\u043d\u0438\u0435 + \u0431\u0430\u0439\u0435\u0441 \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u0447\u0435\u0440\u0435\u0437 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0430\u043a\u0442\u0438\u0432\u0430\u0446\u0438\u0438\n        x = self.layer2(x) # \u041f\u0440\u043e\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043f\u0435\u0440\u0432\u043e\u0433\u043e \u0441\u043b\u043e\u044f \u0447\u0435\u0440\u0435\u0437 \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u043b\u043e\u0439\n        x = F.tanh(x)      # \u041f\u0440\u043e\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u0441\u043a\u0430\u043b. \u043f\u0440\u043e\u0438\u0437\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0432\u0442\u043e\u0440\u043e\u0433\u043e \u0441\u043b\u043e\u044f \u0447\u0435\u0440\u0435\u0437 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0430\u043a\u0442\u0438\u0432\u0430\u0446\u0438\u0438\n        return x\n\n\n# \u0414\u0430\u043d\u043d\u044b\u0435 \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f\nx_train = torch.FloatTensor(&#91;(-1, -1, -1), (-1, -1, 1), (-1, 1, -1), (-1, 1, 1),\n                             (1, -1, -1,), (1, -1, 1), (1, 1, -1), (1, 1, 1)])\ny_train = torch.FloatTensor(&#91;-1, 1, -1, 1, -1, 1, -1, -1])\ntotal = len(y_train)\n\n\n# \u0421\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u043c\u043e\u0434\u0435\u043b\u044c \u0438 \u0437\u0430\u0434\u0430\u0434\u0438\u043c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0430 \u043d\u0435\u0439\u0440\u043e\u043d\u043e\u0432 \u0434\u043b\u044f \u0434\u0432\u0443\u0445 \u0441\u043b\u043e\u0435\u0432\nmodel = NetGirl(3, 3, 1)\n\n# \u041e\u0431\u0443\u0447\u0430\u0435\u043c \u043c\u043e\u0434\u0435\u043b\u044c\n# \u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043c \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0442\u043e\u0440 \u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043f\u043e\u0442\u0435\u0440\u044c\noptimizer = optim.RMSprop(params=model.parameters(), lr=0.01)\nloss_func = torch.nn.MSELoss()\n\n# \u041f\u0435\u0440\u0435\u0432\u043e\u0434\u0438\u043c \u043c\u043e\u0434\u0435\u043b\u044c \u0432 \u0440\u0435\u0436\u0438\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f\nmodel.train()\n\n# \u0426\u0438\u043a\u043b \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f\nfor _ in range(1000):\n    k = randint(0, total-1)\n    y = model(x_train&#91;k])\n    y = y.squeeze()    # \u0414\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0432\u043e\u0439 \u043e\u0441\u0438 \u043a y (\u0441\u0432\u044f\u0437\u0430\u043d\u043e \u0441 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u043e\u0439 \u0431\u0430\u0442\u0447\u0430\u043c\u0438)\n    loss = loss_func(y, y_train&#91;k])\n\n    # \u041e\u0434\u0438\u043d \u0448\u0430\u0433 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 \u0441\u0442\u043e\u0445\u0430\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043d\u043e\u0433\u043e \u0441\u043f\u0443\u0441\u043a\u0430\n    optimizer.zero_grad()    # \u043e\u0431\u043d\u0443\u043b\u0435\u043d\u0438\u0435 \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043e\u0432 \u043f\u0435\u0440\u0435\u0434 \u043e\u0447\u0435\u0440\u0435\u0434\u043d\u044b\u043c \u0448\u0430\u0433\u043e\u043c\n    loss.backward()\n    optimizer.step()\n\n# \u041f\u0435\u0440\u0435\u043e\u0434 \u043c\u043e\u0434\u0435\u043b\u0438 \u0432 \u0440\u0435\u0436\u0438\u043c \u044d\u043a\u0441\u043f\u043b\u0443\u0430\u0442\u0430\u0446\u0438\u0438\nmodel.eval()\n\n# \u041c\u043e\u0436\u043d\u043e \u043e\u0442\u043a\u043b\u044e\u0447\u0430\u0442\u044c \u0438 \u0432\u043a\u043b\u044e\u0447\u0430\u0442\u044c \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u044b\u0435 \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u044b \u0432\u0440\u0443\u0447\u043d\u0443\u044e\n# model.requires_grad_(False)\n# model.requires_grad_(True)\n\n# \u041e\u0446\u0435\u043d\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u0438\nfor x, d in zip(x_train, y_train):\n    with torch.no_grad():    # \u041f\u0440\u0438 \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u043e\u0442\u043a\u043b\u044e\u0447\u0430\u0442\u044c \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u0438 \u043f\u0430\u043c\u044f\u0442\u0438\n        y = model(x)\n        print(f\"\u0412\u044b\u0445\u043e\u0434\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u041d\u0421: {y.data} => {d}\")<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041d\u0438\u0436\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0448\u0430\u0431\u043b\u043e\u043d \u043a\u043e\u0434\u0430 \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438. \u0412 \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0435\u043d\u043e \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0434\u0432\u0443\u0445 \u043f\u043e\u043b\u043d\u043e\u0441\u0432\u044f\u0437\u043d\u044b\u0445 \u0441\u043b\u043e\u0435\u0432.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[30,17,50,40],"class_list":["post-2102","post","type-post","status-publish","format-standard","hentry","category-dl","tag-pytorch","tag-nn","tag-50","tag-40"],"views":88,"_links":{"self":[{"href":"https:\/\/csnotes.ru\/index.php?rest_route=\/wp\/v2\/posts\/2102","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/csnotes.ru\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/csnotes.ru\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/csnotes.ru\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/csnotes.ru\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2102"}],"version-history":[{"count":3,"href":"https:\/\/csnotes.ru\/index.php?rest_route=\/wp\/v2\/posts\/2102\/revisions"}],"predecessor-version":[{"id":2119,"href":"https:\/\/csnotes.ru\/index.php?rest_route=\/wp\/v2\/posts\/2102\/revisions\/2119"}],"wp:attachment":[{"href":"https:\/\/csnotes.ru\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2102"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/csnotes.ru\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2102"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/csnotes.ru\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2102"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}