Pytorch implementation of the Variational Recurrent Neural Network (VRNN).

Related tags

Deep Learning VariationalRecurrentNeuralNetwork
Overview

VariationalRecurrentNeuralNetwork

Pytorch implementation of the Variational RNN (VRNN), from A Recurrent Latent Variable Model for Sequential Data.

The paper is available here.

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Run:

To train: python train.py

To sample with saved model: python sample.py [saves/saved_state_dict_name.pth]

Some samples:

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Comments
  • Repackaging of states necessary?

    Repackaging of states necessary?

    Hi,

    in the VRNN class (in model.py), you cut the gradients behind h_{t-1} using the _repackage_state() function. I've been thinking about this question for a while now and would have said that the correct thing to do is to not cut the gradients because nothing in the paper indicates that one should.

    May I ask what your reasoning is? - I'm not very sure about mine.

    Thanks! Best, Max

    opened by maximilianigl 1
  • nll loss maybe got nan

    nll loss maybe got nan

    the code def _nll_bernoulli(self, theta, x): return - torch.sum(x*torch.log(theta + EPS) + (1-x)*torch.log(1-theta-EPS)) may got nan loss. i think it should be def _nll_bernoulli(self, theta, x): return - torch.sum(x*torch.log(theta + EPS) + (1-x)*torch.log(1-theta+EPS))

    opened by jvnext 0
  • The dim of the data

    The dim of the data

    When load the data from the dataloader, the dim is transposed: data = Variable(data.squeeze().transpose(0, 1)).to(device) So in model.py line 82

     for t in range(x.size(0)):

	phi_x_t = self.phi_x(x[t])

    the size of phi_x_t is ( batch_size, h_dim ), is that correct ?

    opened by RuifMaxx 0
  • i have some question

    i have some question

    Error message :

    Done! Traceback (most recent call last): File "train.py", line 112, in train(epoch) File "train.py", line 25, in train data = (data - data.min().data[0]) / (data.max().data[0] - data.min().data[0]) IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number

    so , I changed the code (as below)

    data.min().data[0] -> data.min()

    *.data[0] -> *.item() ( * Denotes all variables that use data [0].)

    Does this deviate from your intention in your code? @emited

    opened by ghost 1
  • clipping before optimizing step.

    clipping before optimizing step.

    @emited Please check and let me know.

    1. Added clipping before the optimizer step.
    2. Updated with .item() rather than .data[0]
    3. Input is normalized but the reconstruction is done for the unnormalized image so this is removed
    4. Loss get's NAN sometimes and so added 1e-5 as epsilon in the std2 denominator
    opened by srinadhu 0
  • loss got nan.

    loss got nan.

    Train Epoch: 3 [0/60000 (0%)] KLD Loss: 2.687659 NLL Loss: 73.599564 Train Epoch: 3 [2800/60000 (21%)] KLD Loss: 2.976363 NLL Loss: 78.757454 Train Epoch: 3 [5600/60000 (43%)] KLD Loss: 2.837864 NLL Loss: 78.958122 Train Epoch: 3 [8400/60000 (64%)] KLD Loss: nan NLL Loss: nan Train Epoch: 3 [11200/60000 (85%)] KLD Loss: nan NLL Loss: nan ====> Epoch: 3 Average loss: nan ====> Test set loss: KLD Loss = nan, NLL Loss = nan Train Epoch: 4 [0/60000 (0%)] KLD Loss: nan NLL Loss: nan

    opened by kunrenzhilu 5
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emmanuel
emmanuel
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