10000 Use in-place tensor operators where possible. · dotnet/TorchSharpExamples@4c7dbb0 · GitHub
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Use in-place tensor operators where possible.
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‎tutorials/CSharp/synthetic_data.ipynb

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"languageId": "polyglot-notebook"
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"<div><div></div><div></div><div><strong>Installed Packages</strong><ul><li><span>TorchSharp-cpu, 0.100.3</span></li></ul></div></div>"
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]
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},
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"output_type": "display_data"
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],
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"source": [
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"#r \"nuget: TorchSharp-cpu\"\n",
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"\n",
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" public override torch.Tensor forward(torch.Tensor input)\n",
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" {\n",
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" using var _ = torch.NewDisposeScope();\n",
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" var z = torch.tanh(hid1.call(input));\n",
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" z = torch.sigmoid(oupt.call(z));\n",
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" var z = hid1.call(input).tanh_();\n",
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" z = oupt.call(z).sigmoid_();\n",
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" return z.MoveToOuterDisposeScope();\n",
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" }\n",
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"}"
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" public override torch.Tensor forward(torch.Tensor input)\n",
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" {\n",
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" using var _ = torch.NewDisposeScope();\n",
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" var z = torch.nn.functional.relu(hid1.call(input));\n",
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" z = torch.sigmoid(oupt.call(z));\n",
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" var z = hid1.call(input).relu_();\n",
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" z = oupt.call(z).sigmoid_();\n",
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" return z.MoveToOuterDisposeScope();\n",
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"}"
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"language": "csharp"
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"cell_type": "markdown",
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"source": [
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"A standard training loop. It ends with evaluating the trained model on the training set."
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"We need an optimizer."
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]
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"var learning_rate = 0.01f;\n",
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"\n",
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"var optimizer = torch.optim.SGD(model.parameters(), learning_rate);"
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]
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},
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"attachments": {},
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"A standard training loop. It ends with evaluating the trained model on the training set."
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]
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" initial loss = 0.023704259\n",
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" final loss = 0.023490703\n"
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]
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}
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],
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"source": [
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"Console.WriteLine(\" initial loss = \" + loss.forward(model.forward(X_train), y_train).item<float>().ToString());\n",
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"\n",
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"var optimizer = torch.optim.SGD(model.parameters(), learning_rate);\n",
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"\n",
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"for (int i = 0; i < 10000; i++) {\n",
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" // Compute the loss\n",
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" using var output = loss.forward(model.forward(X_train), y_train);\n",
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"loss.forward(model.forward(X_test), y_test).item<float>()"
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