11.05.2017 · I am new to Pytorch and RNN, and don not know how to initialize the trainable parameters of nn.RNN, nn.LSTM, nn.GRU. I would appreciate it if some one could show some example or advice!!! Thanks
LSTM. class torch.nn.LSTM(*args, **kwargs) [source] Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. For each element in the input sequence, each layer computes the following function: i t = σ ( W i i x t + b i i + W h i h t − 1 + b h i) f t = σ ( W i f x t + b i f + W h f h t − 1 + b h f) g t = tanh ( W i ...
In this competition were speed is essential you can not afford to keep determinism by using the regular implementation of GRU and LSTM. PyTorch to the rescue!¶.
06.08.2020 · Hi, I am a kind of Newb in pytorch 🙂 What I’m trying to do is a time series prediction model. After many trials and errors, I found the Keras code I wanted and tried to apply it to the pytorch. The main point of the Keras model is set to stateful = True, so I also used the hidden state and cell state values of the previous mini-batch without initializing the values of the hidden state …
01.03.2021 · Hi, I have started working on Video classification with CNN+LSTM lately and would like some advice. I have 2 folders that should be treated as class and many video files in them. I want to make a well-organised dataloader just like torchvision ImageFolder function, which will take in the videos from the folder and associate it with labels. I have tried manually creating a function that …
17.01.2018 · My initialization is showed as following: [QQ图片20180117105948] But I want to initialize the weights with Xavier not randn. Does someone know how to do it?
04.08.2017 · I have a nn.Module that contains an LSTM whose number of layers is passed in the initialization. I would like to do Xavier initialization of its weights and setting the bias of the forget gate to 1, to promote learning of long-term dependencies. My problem is how to iterate over all the parameters in order to initialize them. Doing something like for name, param in …
Custom weight initialization in PyTorch, You can define a method to ... Initializing parameters of a multi-layer LSTM, Module that contains an LSTM whose ...
21.03.2018 · I recently implemented the VGG16 architecture in Pytorch and trained it on the CIFAR-10 dataset, and I found that just by switching to xavier_uniform initialization for the weights (with biases initialized to 0), rather than using the default initialization, my validation accuracy after 30 epochs of RMSprop increased from 82% to 86%.
Whether He, Xavier, or Lecun intialization is better or any other initializations depends on the overall model's architecture (RNN/LSTM/CNN/FNN etc.), activation functions (ReLU, Sigmoid, Tanh etc.) and more. For example, more advanced initializations we will cover subsequently is orthogonal initialization that works better for RNN/LSTM.