https://github.com/yjlolo/pytorch-deep-markov-model

PyTorch re-implementation of [Structured Inference Networks for Nonlinear State Space Models, AAAI 17]
https://github.com/yjlolo/pytorch-deep-markov-model

aaai markov-model pytorch-implementation reimplementation sequential-data variational-autoencoders variational-inference

Last synced: about 2 months ago
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PyTorch re-implementation of [Structured Inference Networks for Nonlinear State Space Models, AAAI 17]

• Host: GitHub
• URL: https://github.com/yjlolo/pytorch-deep-markov-model
• Owner: yjlolo
• License: mit
• Created: 2020-08-19T11:00:47.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-03-06T15:55:18.000Z (over 4 years ago)
• Last Synced: 2025-04-14T04:09:10.486Z (about 2 months ago)
• Topics: aaai, markov-model, pytorch-implementation, reimplementation, sequential-data, variational-autoencoders, variational-inference
• Language: Python
• Homepage:
• Size: 223 KB
• Stars: 24
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pytorch-deep-markov-model

PyTorch re-implementatoin of the Deep Markov Model (https://arxiv.org/abs/1609.09869)

```

@inproceedings{10.5555/3298483.3298543,

    author = {Krishnan, Rahul G. and Shalit, Uri and Sontag, David},

    title = {Structured Inference Networks for Nonlinear State Space Models},

    year = {2017},

    publisher = {AAAI Press},

    booktitle = {Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence},

    pages = {2101–2109},

    numpages = {9},

    location = {San Francisco, California, USA},

    series = {AAAI'17}

}

```

**Note:** 

1. The calculated metrics in `model/metrics.py` do not match those reported in the paper, which might be (more likely) due to differences in parameter settings and metric calculations.

2. The current implementatoin only supports JSB polyphonic music dataset.

## Under-development

Refer to the branch `factorial-dmm` for a model described as [Factorial DMM](https://groups.csail.mit.edu/sls/publications/2019/SameerKhurana_ICASSP-2019.pdf).

The other branch `refractor` is trying to improve readability with increased options of models (DOCUMENT NOT UPDATED YET!).

## Usage

Training the model with the default `config.json`:

    

    python train.py -c config.json

add `-i` flag to specifically name the experiment that is to be saved under `saved/`.

## `config.json`

This file specifies parameters and configurations.

Below explains some key parameters.

**A careful fine-tuning of the parameters seems necessary to match the reported performances.**

```javascript

{

    "arch": {

        "type": "DeepMarkovModel",

        "args": {

            "input_dim": 88,

            "z_dim": 100,

            "emission_dim": 100,

            "transition_dim": 200,

            "rnn_dim": 600,

            "rnn_type": "lstm",

            "rnn_layers": 1,

            "rnn_bidirection": false,   // condition z_t on both directions of inputs,

	    				// manually turn off `reverse_rnn_input` if True

					// (this is minor and could be quickly fixed)

            "use_embedding": true,      // use extra linear layer before RNN

            "orthogonal_init": true,    // orthogonal initialization for RNN

	    "gated_transition": true,       // use linear/non-linear gated transition

            "train_init": false,        // make z0 trainble

            "mean_field": false,        // use mean-field posterior q(z_t | x)

            "reverse_rnn_input": true,  // condition z_t on future inputs

            "sample": true              // sample during reparameterization

        }

    },

    "optimizer": {

        "type": "Adam",

        "args":{

            "lr": 0.0008,               // default value from the author's source code

            "weight_decay": 0.0,        // debugging stage indicates that 1.0 prevents training

            "amsgrad": true,

            "betas": [0.9, 0.999]

        }

    },

    "trainer": {

        "epochs": 3000,

        "overfit_single_batch": false,  // overfit one single batch for debug

        "save_dir": "saved/",

        "save_period": 500,

        "verbosity": 2,

        

        "monitor": "min val_loss",

        "early_stop": 100,

        "tensorboard": true,

        "min_anneal_factor": 0.0,

        "anneal_update": 5000

    }

}

```

## References

0. Project template brought from the [pytorch-template](https://github.com/victoresque/pytorch-template)

1. The original [source code](https://github.com/clinicalml/structuredinference/tree/master/expt-polyphonic-fast) in Theano

2. PyTorch implementation in [Pyro](https://github.com/pyro-ppl/pyro/tree/dev/examples/dmm) framework

3. Another PyTorch implementation by [@guxd](https://github.com/guxd/deepHMM)

## To-Do

- [ ] fine-tune to match the reported performances in the paper

- [ ] correct (if any) errors in metric calculation, `model/metric.py`

- [ ] optimize important sampling