ixaxaar / Pytorch Sublstm
Licence: mit
subLSTMs for pytorch from Cortical microcircuits as gated-recurrent neural networks
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subtractive LSTM (subLSTM), for Pytorch
This is an implementation of subLSTM described in the paper Cortical microcircuits as gated-recurrent neural networks, Rui Ponte Costa et al.
Install
pip install pytorch-sublstm
Usage
Parameters:
Following are the constructor parameters:
| Argument | Default | Description |
|---|---|---|
| input_size | None |
Size of the input vectors |
| hidden_size | None |
Size of hidden units |
| num_layers | 1 |
Number of layers in the network |
| bias | True |
Bias |
| batch_first | False |
Whether data is fed batch first |
| dropout | 0 |
Dropout between layers in the network |
| bidirectional | False |
If the network is bidirectional |
Example usage:
nn Interface
import torch
from torch.autograd import Variable
from subLSTM.nn import SubLSTM
hidden_size = 20
input_size = 10
seq_len = 5
batch_size = 7
hidden = None
input = Variable(torch.randn(batch_size, seq_len, input_size))
rnn = SubLSTM(input_size, hidden_size, num_layers=2, bias=True, batch_first=True)
# forward pass
output, hidden = rnn(input, hidden)
Cell Interface
import torch
from torch.autograd import Variable
from subLSTM.nn import SubLSTMCell
hidden_size = 20
input_size = 10
seq_len = 5
batch_size = 7
hidden = None
hx = Variable(torch.randn(batch_size, hidden_size))
cx = Variable(torch.randn(batch_size, hidden_size))
input = Variable(torch.randn(batch_size, input_size))
cell = SubLSTMCell(input_size, hidden_size, bias=True)
(hx, cx) = cell(input, (hx, cx))
Tasks:
A language modeling task is included here. Refer to its README for more info.
Attributions:
A lot of the code is recycled from pytorch
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