What's the difference between "hidden" and "output" in PyTorch LSTM?

deep-learning pytorch lstm recurrent-neural-network tensor

I'm having trouble understanding the documentation for PyTorch's LSTM module (and also RNN and GRU, which are similar). Regarding the outputs, it says:

Outputs: output, (h_n, c_n) 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. If a torch.nn.utils.rnn.PackedSequence has been given as the input, the output will also be a packed sequence. h_n (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t=seq_len c_n (num_layers * num_directions, batch, hidden_size): tensor containing the cell state for t=seq_len

It seems that the variables output and h_n both give the values of the hidden state. Does h_n just redundantly provide the last time step that's already included in output, or is there something more to it than that?

nnnmmm

I made a diagram. The names follow the PyTorch docs, although I renamed num_layers to w.

output comprises all the hidden states in the last layer ("last" depth-wise, not time-wise). (h_n, c_n) comprises the hidden states after the last timestep, t = n, so you could potentially feed them into another LSTM.

https://i.stack.imgur.com/SjnTl.png

The batch dimension is not included.

Great, thanks, that makes a lot of sense and is really helpful. So that means, for example, that there is no way to get the hidden values for all layers at a time step other than the last one?

Right, unless you have individual LSTMs with num_layers = 1 that take the previous net's output as input.

@nnnmmm So, each (blue) box is an LSTM/RNN/GRU unit, right? And h_i and c_i are the hidden and cell states resp and w is the depth of our network, right?

@kmario23: yes, each blue box is an LSTM unit. As I understand, vanilla RNN and GRU don't have cell states, just hidden states, so they would look a little different. You're right about h_i, c_i and w.

This is way clearer than Pytorch's official doc. They should include this pic. So many thanks for this. Amazing. Now I understand exactly what the output means.

prosti

It really depends on a model you use and how you will interpret the model. Output may be:

a single LSTM cell hidden state

several LSTM cell hidden states

all the hidden states outputs

Output, is almost never interpreted directly. If the input is encoded there should be a softmax layer to decode the results.

Note: In language modeling hidden states are used to define the probability of the next word, p(wt+1|w1,...,wt) =softmax(Wht+b).

Jibin Mathew

The output state is the tensor of all the hidden state from each time step in the RNN(LSTM), and the hidden state returned by the RNN(LSTM) is the last hidden state from the last time step from the input sequence. You could check this by collecting all of the hidden states from each step and comparing that to the output state,(provided you are not using pack_padded_sequence).

Karthik Ragunath A

In Pytorch, the output parameter gives the output of each individual LSTM cell in the last layer of the LSTM stack, while hidden state and cell state give the output of each hidden cell and cell state in the LSTM stack in every layer.

import torch.nn as nn
torch.manual_seed(1)
inputs = [torch.randn(1, 3) for _ in range(5)] # indicates that there are 5 sequences to be given as inputs and (1,3) indicates that there is 1 layer with 3 cells
hidden = (torch.randn(1, 1, 3),
          torch.randn(1, 1, 3)) #initializing h and c values to be of dimensions (1, 1, 3) which indicates there is (1 * 1) - num_layers * num_directions, with batch size of 1 and projection size of 3. 
                                #Since there is only 1 batch in input, h and c can also have only one batch of data for initialization and the number of cells in both input and output should also match.
 
lstm = nn.LSTM(3, 3) #implying both input and output are 3 dimensional data
for i in inputs:
    out, hidden = lstm(i.view(1, 1, -1), hidden)
    print('out:', out)
    print('hidden:', hidden)

Output

out: tensor([[[-0.1124, -0.0653,  0.2808]]], grad_fn=<StackBackward>)
hidden: (tensor([[[-0.1124, -0.0653,  0.2808]]], grad_fn=<StackBackward>), tensor([[[-0.2883, -0.2846,  2.0720]]], grad_fn=<StackBackward>))
out: tensor([[[ 0.1675, -0.0376,  0.4402]]], grad_fn=<StackBackward>)
hidden: (tensor([[[ 0.1675, -0.0376,  0.4402]]], grad_fn=<StackBackward>), tensor([[[ 0.4394, -0.1226,  1.5611]]], grad_fn=<StackBackward>))
out: tensor([[[0.3699, 0.0150, 0.1429]]], grad_fn=<StackBackward>)
hidden: (tensor([[[0.3699, 0.0150, 0.1429]]], grad_fn=<StackBackward>), tensor([[[0.8432, 0.0618, 0.9413]]], grad_fn=<StackBackward>))
out: tensor([[[0.1795, 0.0296, 0.2957]]], grad_fn=<StackBackward>)
hidden: (tensor([[[0.1795, 0.0296, 0.2957]]], grad_fn=<StackBackward>), tensor([[[0.4541, 0.1121, 0.9320]]], grad_fn=<StackBackward>))
out: tensor([[[0.1365, 0.0596, 0.3931]]], grad_fn=<StackBackward>)
hidden: (tensor([[[0.1365, 0.0596, 0.3931]]], grad_fn=<StackBackward>), tensor([[[0.3430, 0.1948, 1.0255]]], grad_fn=<StackBackward>))

Multi-Layered LSTM

import torch.nn as nn
torch.manual_seed(1)
num_layers = 2
inputs = [torch.randn(1, 3) for _ in range(5)] 
hidden = (torch.randn(2, 1, 3),
          torch.randn(2, 1, 3))
lstm = nn.LSTM(input_size=3, hidden_size=3, num_layers=2)
for i in inputs:
    # Step through the sequence one element at a time.
    # after each step, hidden contains the hidden state.
    out, hidden = lstm(i.view(1, 1, -1), hidden)
    print('out:', out)
    print('hidden:', hidden)

Output

out: tensor([[[-0.0819,  0.1214, -0.2586]]], grad_fn=<StackBackward>)
hidden: (tensor([[[-0.2625,  0.4415, -0.4917]],

        [[-0.0819,  0.1214, -0.2586]]], grad_fn=<StackBackward>), tensor([[[-2.5740,  0.7832, -0.9211]],

        [[-0.2803,  0.5175, -0.5330]]], grad_fn=<StackBackward>))
out: tensor([[[-0.1298,  0.2797, -0.0882]]], grad_fn=<StackBackward>)
hidden: (tensor([[[-0.3818,  0.3306, -0.3020]],

        [[-0.1298,  0.2797, -0.0882]]], grad_fn=<StackBackward>), tensor([[[-2.3980,  0.6347, -0.6592]],

        [[-0.3643,  0.9301, -0.1326]]], grad_fn=<StackBackward>))
out: tensor([[[-0.1630,  0.3187,  0.0728]]], grad_fn=<StackBackward>)
hidden: (tensor([[[-0.5612,  0.3134, -0.0782]],

        [[-0.1630,  0.3187,  0.0728]]], grad_fn=<StackBackward>), tensor([[[-1.7555,  0.6882, -0.3575]],

        [[-0.4571,  1.2094,  0.1061]]], grad_fn=<StackBackward>))
out: tensor([[[-0.1723,  0.3274,  0.1546]]], grad_fn=<StackBackward>)
hidden: (tensor([[[-0.5112,  0.1597, -0.0901]],

        [[-0.1723,  0.3274,  0.1546]]], grad_fn=<StackBackward>), tensor([[[-1.4417,  0.5892, -0.2489]],

        [[-0.4940,  1.3620,  0.2255]]], grad_fn=<StackBackward>))
out: tensor([[[-0.1847,  0.2968,  0.1333]]], grad_fn=<StackBackward>)
hidden: (tensor([[[-0.3256,  0.3217, -0.1899]],

        [[-0.1847,  0.2968,  0.1333]]], grad_fn=<StackBackward>), tensor([[[-1.7925,  0.6096, -0.4432]],

        [[-0.5147,  1.4031,  0.2014]]], grad_fn=<StackBackward>))

Bi-Directional Multi-Layered LSTM

import torch.nn as nn
torch.manual_seed(1)
num_layers = 2
is_bidirectional = True
inputs = [torch.randn(1, 3) for _ in range(5)] 
hidden = (torch.randn(4, 1, 3),
          torch.randn(4, 1, 3)) #4 -> (2 * 2) -> num_layers * num_directions
lstm = nn.LSTM(input_size=3, hidden_size=3, num_layers=2, bidirectional=is_bidirectional)

for i in inputs:
    # Step through the sequence one element at a time.
    # after each step, hidden contains the hidden state.
    out, hidden = lstm(i.view(1, 1, -1), hidden)
    print('out:', out)
    print('hidden:', hidden)
    # output dim -> (seq_len, batch, num_directions * hidden_size) -> (5, 1, 2*3)
    # hidden dim -> (num_layers * num_directions, batch, hidden_size) -> (2 * 2, 1, 3)
    # cell state dim -> (num_layers * num_directions, batch, hidden_size) -> (2 * 2, 1, 3)

Output

out: tensor([[[-0.4620,  0.1115, -0.1087,  0.1646,  0.0173, -0.2196]]],
       grad_fn=<CatBackward>)
hidden: (tensor([[[ 0.5187,  0.2656, -0.2543]],

        [[ 0.4175,  0.0539,  0.0633]],

        [[-0.4620,  0.1115, -0.1087]],

        [[ 0.1646,  0.0173, -0.2196]]], grad_fn=<StackBackward>), tensor([[[ 1.1546,  0.4012, -0.4119]],

        [[ 0.7999,  0.2632,  0.2587]],

        [[-1.4196,  0.2075, -0.3148]],

        [[ 0.6605,  0.0243, -0.5783]]], grad_fn=<StackBackward>))
out: tensor([[[-0.1860,  0.1359, -0.2719,  0.0815,  0.0061, -0.0980]]],
       grad_fn=<CatBackward>)
hidden: (tensor([[[ 0.2945,  0.0842, -0.1580]],

        [[ 0.2766, -0.1873,  0.2416]],

        [[-0.1860,  0.1359, -0.2719]],

        [[ 0.0815,  0.0061, -0.0980]]], grad_fn=<StackBackward>), tensor([[[ 0.5453,  0.1281, -0.2497]],

        [[ 0.9706, -0.3592,  0.4834]],

        [[-0.3706,  0.2681, -0.6189]],

        [[ 0.2029,  0.0121, -0.3028]]], grad_fn=<StackBackward>))
out: tensor([[[ 0.1095,  0.1520, -0.3238,  0.0283,  0.0387, -0.0820]]],
       grad_fn=<CatBackward>)
hidden: (tensor([[[ 0.1427,  0.0859, -0.2926]],

        [[ 0.1536, -0.2343,  0.0727]],

        [[ 0.1095,  0.1520, -0.3238]],

        [[ 0.0283,  0.0387, -0.0820]]], grad_fn=<StackBackward>), tensor([[[ 0.2386,  0.1646, -0.4102]],

        [[ 0.2636, -0.4828,  0.1889]],

        [[ 0.1967,  0.2848, -0.7155]],

        [[ 0.0735,  0.0702, -0.2859]]], grad_fn=<StackBackward>))
out: tensor([[[ 0.2346,  0.1576, -0.4006, -0.0053,  0.0256, -0.0653]]],
       grad_fn=<CatBackward>)
hidden: (tensor([[[ 0.1706,  0.0147, -0.0341]],

        [[ 0.1835, -0.3951,  0.2506]],

        [[ 0.2346,  0.1576, -0.4006]],

        [[-0.0053,  0.0256, -0.0653]]], grad_fn=<StackBackward>), tensor([[[ 0.3422,  0.0269, -0.0475]],

        [[ 0.4235, -0.9144,  0.5655]],

        [[ 0.4589,  0.2807, -0.8332]],

        [[-0.0133,  0.0507, -0.1996]]], grad_fn=<StackBackward>))
out: tensor([[[ 0.2774,  0.1639, -0.4460, -0.0228,  0.0086, -0.0369]]],
       grad_fn=<CatBackward>)
hidden: (tensor([[[ 0.2147, -0.0191,  0.0677]],

        [[ 0.2516, -0.4591,  0.3327]],

        [[ 0.2774,  0.1639, -0.4460]],

        [[-0.0228,  0.0086, -0.0369]]], grad_fn=<StackBackward>), tensor([[[ 0.4414, -0.0299,  0.0889]],

        [[ 0.6360, -1.2360,  0.7229]],

        [[ 0.5692,  0.2843, -0.9375]],

        [[-0.0569,  0.0177, -0.1039]]], grad_fn=<StackBackward>))

Pulkit Bansal

I just verified some of this using code, and its indeed correct that if it's a depth 1 LSTM, then h_n is the same as the last value of the "output". (this will not be true for > 1 depth LSTM though as explained above by @nnnmmm)

So, basically the "output" we get after applying LSTM is not the same as o_t as defined in the documentation, rather it is h_t.

import torch
import torch.nn as nn

torch.manual_seed(0)
model = nn.LSTM( input_size = 1, hidden_size = 50, num_layers  = 1 )
x = torch.rand( 50, 1, 1)
output, (hn, cn) = model(x)

Now one can check that output[-1] and hn both have the same value as follows

tensor([[ 0.1140, -0.0600, -0.0540,  0.1492, -0.0339, -0.0150, -0.0486,  0.0188,
          0.0504,  0.0595, -0.0176, -0.0035,  0.0384, -0.0274,  0.1076,  0.0843,
         -0.0443,  0.0218, -0.0093,  0.0002,  0.1335,  0.0926,  0.0101, -0.1300,
         -0.1141,  0.0072, -0.0142,  0.0018,  0.0071,  0.0247,  0.0262,  0.0109,
          0.0374,  0.0366,  0.0017,  0.0466,  0.0063,  0.0295,  0.0536,  0.0339,
          0.0528, -0.0305,  0.0243, -0.0324,  0.0045, -0.1108, -0.0041, -0.1043,
         -0.0141, -0.1222]], grad_fn=<SelectBackward>)

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