Hi all, I am recently trying to build a RNN model for some NLP task, during which I found that the RNN layer interface provided by pytorch (no matter what cell type, gru or lstm) doesn’t support masking the inputs. Masking is broadly used in NLP domain for the inputs within a single batch having different length (as inputs are generally bunch of natural language sentences), so just wondering ...
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Oct 11, 2017 · GitHub, code, software, git PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM Quasi-Recurrent Neural Network (QRNN) for PyTorch This repository contains a PyTorch implementation of Salesforce Research 's Quasi-Recurrent Neural Networks paper.
If a torch.nn.utils.rnn.PackedSequence has been given as the input, the output will also be a packed sequence. For the unpacked case, the directions can be separated using output.view (seq_len, batch, num_directions, hidden_size), with forward and backward being direction 0 and 1 respectively.
Nowadays nearly all of my code is written using Python, NumPy, and PyTorch. I moved to PyTorch from TensorFlow in 2017, and my experience has resembled Andrej Karpathy's:). I've made small open-source contributions (code, tests, and/or docs) to TensorFlow, PyTorch, Edward, Pyro, and other projects. Some of my projects can be found here: GitHub
JusperLee/Dual-Path-RNN-Pytorch ... Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. ...
Recurrent Neural Networks (RNNs) help us tackle sequential information like natural language. If we are going to predict properties of the current word, we better remember the words before it too. An RNN has such an internal state/memory which stores the summary of the sequence it has seen so far.