Train_loader = DataLoader((X, L), batchsize=batch_size, shuffle=true) # Normalize input data to have zero mean and unit standard deviation Wid = weight tensor that generates an activation aᵢ Wd = Weight tensors of layer d that generates an activation # convert the arguments passed during onnx.export callįunction RSO(X,L,C,model, batch_size, device)Ĭ = Number of rounds to update parameters Torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach()) Return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach(), Lstm_out, (hidden_a, hidden_b) = self.lstm(x, (h0, c0)) Specifically, a numpy equivalent for the following would be great: It would help us compare the numpy output to torch output for the same code, and give us some modular code/functions to use. I think it might be useful to include the numpy/scipy equivalent for both nn.LSTM and nn.linear. So, the question is, how can I 'translate' this RNN definition into a class that doesn't need pytorch, and how to use the state dict weights for it?Īlternatively, is there a 'light' version of pytorch, that I can use just to run the model and yield a result? EDIT However, can I have some implementation for the nn.LSTM and nn.Linear using something not involving pytorch? Also, how will I use the weights from the state dict into the new class? I think I can easily implement the sigmoid function using numpy. I can work with numpy array instead of tensors, and reshape instead of view, and I don't need a device setting.īased on the class definition above, what I can see here is that I only need the following components from torch to get an output from the forward function: I am aware of this question, but I'm willing to go as low level as possible. Torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device)) Return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device), #return torch.rand(self.num_layers, self.batch_size, self.hidden_size) Lstm_out, self.hidden = self.lstm(cur_ft_tensor, self.hidden) Output_scores = torch.sigmoid(output_space) #we'll need to check if we need this sigmoidĬur_ft_tensor=feature_list#.view()Ĭur_ft_tensor=cur_ft_tensor.view()

Output_space = self.hidden2out(lstm_out.view(len( feature_list), -1)) Lstm_out, _ = self.lstm( feature_list.view(len( feature_list), 1, -1)) Self.hidden2out = nn.Linear(hidden_size, output_size) Self.lstm = nn.LSTM(input_size, hidden_size,num_layers) Self.matching_in_out = matching_in_out #length of input vector matches the length of output vector Def _init_(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):

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