How/What to initialize the hidden states in RNN sequence-to-sequence models?2019 Community Moderator ElectionWhy do we need to add START <s> + END </s> symbols when using Recurrent Neural Nets for Sequence-to-Sequence Models?How to train the same RNN over multiple series?What is the use of torch.no_grad in pytorch?What are the benefits and tradeoffs of a 1D conv vs a multi-input seq2seq LSTM model?On-the-fly seq2seq: starting translation before the input sequence endsCan Sequence to sequence models be used to convert code from one programming language to another?How many Hidden Layers and Neurons should I use in an RNN?What is the advantage of using RNN with fixed timestep length over Neural Network?What should the size of the decoder output be in a sequence to sequence modelInitialising states in a multilayer sequence to sequence model
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How/What to initialize the hidden states in RNN sequence-to-sequence models?
2019 Community Moderator ElectionWhy do we need to add START <s> + END </s> symbols when using Recurrent Neural Nets for Sequence-to-Sequence Models?How to train the same RNN over multiple series?What is the use of torch.no_grad in pytorch?What are the benefits and tradeoffs of a 1D conv vs a multi-input seq2seq LSTM model?On-the-fly seq2seq: starting translation before the input sequence endsCan Sequence to sequence models be used to convert code from one programming language to another?How many Hidden Layers and Neurons should I use in an RNN?What is the advantage of using RNN with fixed timestep length over Neural Network?What should the size of the decoder output be in a sequence to sequence modelInitialising states in a multilayer sequence to sequence model
$begingroup$
In an RNN sequence-to-sequence model, the encode input hidden states and the output's hidden states needs to be initialized before training.
What values should we initialize them with? How should we initialize them?
From the PyTorch tutorial, it simply initializes zeros to the hidden states.
Is initializing zero the usual way of initializing hidden states in RNN seq2seq networks?
How about glorot initialization?
For a single-layer vanilla RNN wouldn't the fan-in and fan-out be equals to $(1 + 1)$ which gives a variance of $1$ and the gaussian distribution with $mean=0$ gives us a uniform distribution of $0$s.
for-each input-hidden weight
variance = 2.0 / (fan-in +fan-out)
stddev = sqrt(variance)
weight = gaussian(mean=0.0, stddev)
end-for
For single layer encoder-decoder architecture with attention, if we use glorot, we'll get a very very small variance when initializing the decoder hidden state since the fan-in would include the attention which is mapped to all possible vocabulary from the encoder output. So we result in a gaussian mean of ~= 0 too since stdev is really really small.
What other initialization methods are there, esp. for the use on RNN seq2seq models?
pytorch recurrent-neural-net sequence-to-sequence glorot-initialization
$endgroup$
add a comment |
$begingroup$
In an RNN sequence-to-sequence model, the encode input hidden states and the output's hidden states needs to be initialized before training.
What values should we initialize them with? How should we initialize them?
From the PyTorch tutorial, it simply initializes zeros to the hidden states.
Is initializing zero the usual way of initializing hidden states in RNN seq2seq networks?
How about glorot initialization?
For a single-layer vanilla RNN wouldn't the fan-in and fan-out be equals to $(1 + 1)$ which gives a variance of $1$ and the gaussian distribution with $mean=0$ gives us a uniform distribution of $0$s.
for-each input-hidden weight
variance = 2.0 / (fan-in +fan-out)
stddev = sqrt(variance)
weight = gaussian(mean=0.0, stddev)
end-for
For single layer encoder-decoder architecture with attention, if we use glorot, we'll get a very very small variance when initializing the decoder hidden state since the fan-in would include the attention which is mapped to all possible vocabulary from the encoder output. So we result in a gaussian mean of ~= 0 too since stdev is really really small.
What other initialization methods are there, esp. for the use on RNN seq2seq models?
pytorch recurrent-neural-net sequence-to-sequence glorot-initialization
$endgroup$
add a comment |
$begingroup$
In an RNN sequence-to-sequence model, the encode input hidden states and the output's hidden states needs to be initialized before training.
What values should we initialize them with? How should we initialize them?
From the PyTorch tutorial, it simply initializes zeros to the hidden states.
Is initializing zero the usual way of initializing hidden states in RNN seq2seq networks?
How about glorot initialization?
For a single-layer vanilla RNN wouldn't the fan-in and fan-out be equals to $(1 + 1)$ which gives a variance of $1$ and the gaussian distribution with $mean=0$ gives us a uniform distribution of $0$s.
for-each input-hidden weight
variance = 2.0 / (fan-in +fan-out)
stddev = sqrt(variance)
weight = gaussian(mean=0.0, stddev)
end-for
For single layer encoder-decoder architecture with attention, if we use glorot, we'll get a very very small variance when initializing the decoder hidden state since the fan-in would include the attention which is mapped to all possible vocabulary from the encoder output. So we result in a gaussian mean of ~= 0 too since stdev is really really small.
What other initialization methods are there, esp. for the use on RNN seq2seq models?
pytorch recurrent-neural-net sequence-to-sequence glorot-initialization
$endgroup$
In an RNN sequence-to-sequence model, the encode input hidden states and the output's hidden states needs to be initialized before training.
What values should we initialize them with? How should we initialize them?
From the PyTorch tutorial, it simply initializes zeros to the hidden states.
Is initializing zero the usual way of initializing hidden states in RNN seq2seq networks?
How about glorot initialization?
For a single-layer vanilla RNN wouldn't the fan-in and fan-out be equals to $(1 + 1)$ which gives a variance of $1$ and the gaussian distribution with $mean=0$ gives us a uniform distribution of $0$s.
for-each input-hidden weight
variance = 2.0 / (fan-in +fan-out)
stddev = sqrt(variance)
weight = gaussian(mean=0.0, stddev)
end-for
For single layer encoder-decoder architecture with attention, if we use glorot, we'll get a very very small variance when initializing the decoder hidden state since the fan-in would include the attention which is mapped to all possible vocabulary from the encoder output. So we result in a gaussian mean of ~= 0 too since stdev is really really small.
What other initialization methods are there, esp. for the use on RNN seq2seq models?
pytorch recurrent-neural-net sequence-to-sequence glorot-initialization
pytorch recurrent-neural-net sequence-to-sequence glorot-initialization
edited Jan 30 '18 at 6:46
alvas
asked Jan 30 '18 at 6:30
alvasalvas
79631229
79631229
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$begingroup$
It is important to clear up the difference between hidden state initialization and weight initialization. Glotrot (Xavier), Kaiming etc. are all initialization methods for the weights of neural networks.
Since your question is asking about hidden state initialization:
Hidden states on the other hand can be initialized in a variety of ways, initializing to zero is indeed common. Other methods include sampling from Gaussian or other distributions. In relation to RNN's this defines what a RNN starts with as its 'memory'. Two common approaches seem to be either a noisy initialization (from some sort of distribution or a random number generator), or a learned initialization.
To synthesize the link above; initializing hidden states with zeros can lead to the network learning to adapt from a zero hidden state, rather than minimizing the loss for a long sequence (it follows that this is more of a problem for short sequences). If there are enough sequences it can make sense to have the initial state be a trained variable that is a function of the error during back propagation.
$endgroup$
add a comment |
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1 Answer
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1 Answer
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$begingroup$
It is important to clear up the difference between hidden state initialization and weight initialization. Glotrot (Xavier), Kaiming etc. are all initialization methods for the weights of neural networks.
Since your question is asking about hidden state initialization:
Hidden states on the other hand can be initialized in a variety of ways, initializing to zero is indeed common. Other methods include sampling from Gaussian or other distributions. In relation to RNN's this defines what a RNN starts with as its 'memory'. Two common approaches seem to be either a noisy initialization (from some sort of distribution or a random number generator), or a learned initialization.
To synthesize the link above; initializing hidden states with zeros can lead to the network learning to adapt from a zero hidden state, rather than minimizing the loss for a long sequence (it follows that this is more of a problem for short sequences). If there are enough sequences it can make sense to have the initial state be a trained variable that is a function of the error during back propagation.
$endgroup$
add a comment |
$begingroup$
It is important to clear up the difference between hidden state initialization and weight initialization. Glotrot (Xavier), Kaiming etc. are all initialization methods for the weights of neural networks.
Since your question is asking about hidden state initialization:
Hidden states on the other hand can be initialized in a variety of ways, initializing to zero is indeed common. Other methods include sampling from Gaussian or other distributions. In relation to RNN's this defines what a RNN starts with as its 'memory'. Two common approaches seem to be either a noisy initialization (from some sort of distribution or a random number generator), or a learned initialization.
To synthesize the link above; initializing hidden states with zeros can lead to the network learning to adapt from a zero hidden state, rather than minimizing the loss for a long sequence (it follows that this is more of a problem for short sequences). If there are enough sequences it can make sense to have the initial state be a trained variable that is a function of the error during back propagation.
$endgroup$
add a comment |
$begingroup$
It is important to clear up the difference between hidden state initialization and weight initialization. Glotrot (Xavier), Kaiming etc. are all initialization methods for the weights of neural networks.
Since your question is asking about hidden state initialization:
Hidden states on the other hand can be initialized in a variety of ways, initializing to zero is indeed common. Other methods include sampling from Gaussian or other distributions. In relation to RNN's this defines what a RNN starts with as its 'memory'. Two common approaches seem to be either a noisy initialization (from some sort of distribution or a random number generator), or a learned initialization.
To synthesize the link above; initializing hidden states with zeros can lead to the network learning to adapt from a zero hidden state, rather than minimizing the loss for a long sequence (it follows that this is more of a problem for short sequences). If there are enough sequences it can make sense to have the initial state be a trained variable that is a function of the error during back propagation.
$endgroup$
It is important to clear up the difference between hidden state initialization and weight initialization. Glotrot (Xavier), Kaiming etc. are all initialization methods for the weights of neural networks.
Since your question is asking about hidden state initialization:
Hidden states on the other hand can be initialized in a variety of ways, initializing to zero is indeed common. Other methods include sampling from Gaussian or other distributions. In relation to RNN's this defines what a RNN starts with as its 'memory'. Two common approaches seem to be either a noisy initialization (from some sort of distribution or a random number generator), or a learned initialization.
To synthesize the link above; initializing hidden states with zeros can lead to the network learning to adapt from a zero hidden state, rather than minimizing the loss for a long sequence (it follows that this is more of a problem for short sequences). If there are enough sequences it can make sense to have the initial state be a trained variable that is a function of the error during back propagation.
answered Mar 25 at 17:02
Mati KMati K
156
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