Understanding a simple LSTM pytorch
import torch,ipdb
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable

rnn = nn.LSTM(input_size=10, hidden_size=20, num_layers=2)
input = Variable(torch.randn(5, 3, 10))
h0 = Variable(torch.randn(2, 3, 20))
c0 = Variable(torch.randn(2, 3, 20))
output, hn = rnn(input, (h0, c0))


This is the LSTM example from the docs. I don't know understand the following things:


What is output-size and why is it not specified anywhere?
Why does the input have 3 dimensions. What does 5 and 3 represent?
What are 2 and 3 in h0 and c0, what do those represent?


Edit:

import torch,ipdb
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F

num_layers=3
num_hyperparams=4
batch = 1
hidden_size = 20
rnn = nn.LSTM(input_size=num_hyperparams, hidden_size=hidden_size, num_layers=num_layers)

input = Variable(torch.randn(1, batch, num_hyperparams)) # (seq_len, batch, input_size)
h0 = Variable(torch.randn(num_layers, batch, hidden_size)) # (num_layers, batch, hidden_size)
c0 = Variable(torch.randn(num_layers, batch, hidden_size))
output, hn = rnn(input, (h0, c0))
affine1 = nn.Linear(hidden_size, num_hyperparams)

ipdb.set_trace()
print output.size()
print h0.size()



  *** RuntimeError: matrices expected, got 3D, 2D tensors at


The output for the LSTM is the output for all the hidden nodes on the final layer.
hidden_size - the number of LSTM blocks per layer.
input_size - the number of input features per time-step.
num_layers - the number of hidden layers.
In total there are hidden_size * num_layers LSTM blocks.

The input dimensions are (seq_len, batch, input_size).
seq_len - the number of time steps in each input stream.
batch - the size of each batch of input sequences.

The hidden and cell dimensions are: (num_layers, batch, hidden_size)


  output (seq_len, batch, hidden_size * num_directions): tensor containing the output features (h_t) from the last layer of the RNN, for each t.


So there will be hidden_size * num_directions outputs. You didn't initialise the RNN to be bidirectional so num_directions is 1. So output_size = hidden_size.

Edit: You can change the number of outputs by using a linear layer:

out_rnn, hn = rnn(input, (h0, c0))
lin = nn.Linear(hidden_size, output_size)
v1 = nn.View(seq_len*batch, hidden_size)
v2 = nn.View(seq_len, batch, output_size)
output = v2(lin(v1(out_rnn)))


Note: for this answer I assumed that we're only talking about non-bidirectional LSTMs.

Source: PyTorch docs.
