Row 3950

Row ID: 3950 | Dataset Entry | Axioma AXP Content Repository

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Hi everyone, I'm trying to implement a LSTM in PyTorch but I have some doubts that I haven't been able to resolve by searching online:

First of all I saw from the documentation that the size parameters are `input_size` and `hidden_size` but I cannot understand how to control the size when I have more layers. Let's say I have 3 layers:

\[`input_size`\] `lstm1` \[`hidden_size`\] --> `lstm2` \[what about this size?\] --> `lstm3` \[what about this size?\]

Secondly I tried to use `nn.Sequential` but it doesn't work I think because the LSTM outputs a tensor and a tuple containing the memory and it cannot be passed to another layer. I managed to do this and it works but I wanted to know if there was another method, possibly using `nn.Sequential` . Here is my code:

import torch import torch.nn as nn class Model(nn.Module): def init(self): super().init() self.model = nn.ModuleDict({ 'lstm': nn.LSTM(input_size=300, hidden_size=200, num_layers=2), 'hidden_linear': nn.Linear(in_features=8 * 10 * 200, out_features=50), 'relu': nn.ReLU(inplace=True), 'output_linear': nn.Linear(in_features=50, out_features=3)}) def forward(self, x): out, memory = self.model['lstm'](x) out = out.view(-1) out = self.model['hidden_linear'](out) out = self.model["relu"](out) out = self.model["output_linear"](out) out = nn.functional.softmax(out, dim=0) return out input_tensor = torch.randn(8, 10, 300) model = Model() output = model(input_tensor)

Thank you for your help

FieldValue
text Hi everyone, I'm trying to implement a LSTM in PyTorch but I have some doubts that I haven't been able to resolve by searching online: First of all I saw from the documentation that the size parameters are `input_size` and `hidden_size` but I cannot understand how to control the size when I have more layers. Let's say I have 3 layers: \[`input_size`\] `lstm1` \[`hidden_size`\] --> `lstm2` \[what about this size?\] --> `lstm3` \[what about this size?\] Secondly I tried to use `nn.Sequential` …
label r/pytorch
dataType post
communityName r/pytorch
datetime 2024-03-30
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{
  "text": "Hi everyone, I'm trying to implement a LSTM in PyTorch but I have some doubts that I haven't been able to resolve by searching online:\n\nFirst of all I saw from the documentation that the size parameters are `input_size` and `hidden_size` but I cannot understand how to control the size when I have more layers. Let's say I have 3 layers:\n\n\\[`input_size`\\]  `lstm1` \\[`hidden_size`\\] --> `lstm2` \\[what about this size?\\] --> `lstm3` \\[what about this size?\\]\n\nSecondly I tried to use `nn.Sequential` but it doesn't work I think because the LSTM outputs a tensor and a tuple containing the memory and it cannot be passed to another layer. I managed to do this and it works but I wanted to know if there was another method, possibly using `nn.Sequential` . Here is my code:\n\n    import torch\n    import torch.nn as nn\n    \n    \n    class Model(nn.Module):\n        def init(self):\n            super().init()\n            self.model = nn.ModuleDict({\n                'lstm': nn.LSTM(input_size=300, hidden_size=200, num_layers=2),\n                'hidden_linear': nn.Linear(in_features=8 * 10 * 200, out_features=50),\n                'relu': nn.ReLU(inplace=True),\n                'output_linear': nn.Linear(in_features=50, out_features=3)})\n    \n        def forward(self, x):\n            out, memory = self.model['lstm'](x)\n    \n            out = out.view(-1)\n    \n            out = self.model['hidden_linear'](out)\n    \n            out = self.model[\"relu\"](out)\n    \n            out = self.model[\"output_linear\"](out)\n    \n            out = nn.functional.softmax(out, dim=0)\n    \n            return out\n    \n    \n    input_tensor = torch.randn(8, 10, 300)\n    model = Model()\n    output = model(input_tensor)\n\nThank you for your help",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-03-30",
  "username_encoded": "Z0FBQUFBQm5LakwxUzNWYTI4d09vTS1NTVhoZ0pkQkZ5dWprUHBFa0dQc0xET0RNWEZSdnBPaTRKOGY0RUhUNkowT1FyRG5ybndiYWVaMXFBMkhtTENpeVdtaFBMR0VzYmRnUktId1dpamJpTHlWTi1ZaVR1ZUE9",
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}
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