Vanishing gradient problem for recent stochastic recurrent neural networks
Multi tool use
$begingroup$
Recently, I've found some papers about generative recurrent models. All have attached sub-networks like prior/encoder/decoder/etc. to well-known LSTM cell for composing an aggregation of new-type RNN cell.
I am just curious about whether the gradient vanishing/exploding happens or not to those new RNN cell. Isn't there any problem about that kind of combination?
References:
It seems like they all have similar pattern as mentioned above.
A Recurrent Latent Variable Model for Sequential Data
Learning Stochastic Recurrent Networks
Z-Forcing: Training Stochastic Recurrent Networks
Pseudocode
The pseudocode for recurrent architecture is below:
def new_rnncell_call(x, htm1):
#prior_net/posterior_net/decoder_net is single layer or mlp each
q_prior = prior_net(htm1) # prior step
q = posterior_net([htm1, x]) # inference step
z = sample_from(q) # reparameterization trick
target_dist = decoder_net(z) # generation step
ht = innerLSTM([z, x], htm1) # recurrent step
return [q_prior, q, target_dist], ht
What concerns me are those naked weights outside of well-known LSTM (or GRU etc.) cell during processing bptt without any gating logic for activations as the weights inside LSTM. For me, this looks not similar to stacked-rnn layers or additional dense layers just to outputs.
Doesn't that have any gradient vanishing/exploding problem?
python deep-learning gradient-descent recurrent-neural-net
asked 5 mins ago
Sehee ParkSehee Park
1
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
Recently, I've found some papers about generative recurrent models. All have attached sub-networks like prior/encoder/decoder/etc. to well-known LSTM cell for composing an aggregation of new-type RNN cell.
I am just curious about whether the gradient vanishing/exploding happens or not to those new RNN cell. Isn't there any problem about that kind of combination?
References:
It seems like they all have similar pattern as mentioned above.
A Recurrent Latent Variable Model for Sequential Data
Learning Stochastic Recurrent Networks
Z-Forcing: Training Stochastic Recurrent Networks
Pseudocode
The pseudocode for recurrent architecture is below:
def new_rnncell_call(x, htm1):
#prior_net/posterior_net/decoder_net is single layer or mlp each
q_prior = prior_net(htm1) # prior step
q = posterior_net([htm1, x]) # inference step
z = sample_from(q) # reparameterization trick
target_dist = decoder_net(z) # generation step
ht = innerLSTM([z, x], htm1) # recurrent step
return [q_prior, q, target_dist], ht
What concerns me are those naked weights outside of well-known LSTM (or GRU etc.) cell during processing bptt without any gating logic for activations as the weights inside LSTM. For me, this looks not similar to stacked-rnn layers or additional dense layers just to outputs.
Doesn't that have any gradient vanishing/exploding problem?
python deep-learning gradient-descent recurrent-neural-net
asked 5 mins ago
Sehee ParkSehee Park
1
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Recently, I've found some papers about generative recurrent models. All have attached sub-networks like prior/encoder/decoder/etc. to well-known LSTM cell for composing an aggregation of new-type RNN cell.
I am just curious about whether the gradient vanishing/exploding happens or not to those new RNN cell. Isn't there any problem about that kind of combination?
References:
It seems like they all have similar pattern as mentioned above.
A Recurrent Latent Variable Model for Sequential Data
Learning Stochastic Recurrent Networks
Z-Forcing: Training Stochastic Recurrent Networks
Pseudocode
The pseudocode for recurrent architecture is below:
def new_rnncell_call(x, htm1):
#prior_net/posterior_net/decoder_net is single layer or mlp each
q_prior = prior_net(htm1) # prior step
q = posterior_net([htm1, x]) # inference step
z = sample_from(q) # reparameterization trick
target_dist = decoder_net(z) # generation step
ht = innerLSTM([z, x], htm1) # recurrent step
return [q_prior, q, target_dist], ht
What concerns me are those naked weights outside of well-known LSTM (or GRU etc.) cell during processing bptt without any gating logic for activations as the weights inside LSTM. For me, this looks not similar to stacked-rnn layers or additional dense layers just to outputs.
Doesn't that have any gradient vanishing/exploding problem?
python deep-learning gradient-descent recurrent-neural-net
asked 5 mins ago
Sehee ParkSehee Park
1
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Recently, I've found some papers about generative recurrent models. All have attached sub-networks like prior/encoder/decoder/etc. to well-known LSTM cell for composing an aggregation of new-type RNN cell.
I am just curious about whether the gradient vanishing/exploding happens or not to those new RNN cell. Isn't there any problem about that kind of combination?
References:
It seems like they all have similar pattern as mentioned above.
A Recurrent Latent Variable Model for Sequential Data
Learning Stochastic Recurrent Networks
Z-Forcing: Training Stochastic Recurrent Networks
Pseudocode
The pseudocode for recurrent architecture is below:
def new_rnncell_call(x, htm1):
#prior_net/posterior_net/decoder_net is single layer or mlp each
q_prior = prior_net(htm1) # prior step
q = posterior_net([htm1, x]) # inference step
z = sample_from(q) # reparameterization trick
target_dist = decoder_net(z) # generation step
ht = innerLSTM([z, x], htm1) # recurrent step
return [q_prior, q, target_dist], ht
What concerns me are those naked weights outside of well-known LSTM (or GRU etc.) cell during processing bptt without any gating logic for activations as the weights inside LSTM. For me, this looks not similar to stacked-rnn layers or additional dense layers just to outputs.
Doesn't that have any gradient vanishing/exploding problem?
python deep-learning gradient-descent recurrent-neural-net
python deep-learning gradient-descent recurrent-neural-net
asked 5 mins ago
Sehee ParkSehee Park
1
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
Sehee ParkSehee Park
1
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
Sehee ParkSehee Park
1
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
Sehee ParkSehee Park
1
asked 5 mins ago
Sehee ParkSehee Park
1
1
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sehee Park is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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