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Interpretable Models for NLP using PyTorch

anuvada 최신버젼 다운로드

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This repo is deprecated. Please find the updated package here.

https://github.com/EdGENetworks/anuvada

# Anuvada: Interpretable Models for NLP using PyTorch

One of the common criticisms of deep learning has been it's black box nature. To address this issue, researchers have
developed many ways to visualise and explain the inference. Some examples would be attention in the case of RNN's,
activation maps, guided back propagation and occlusion (in the case of CNN's). This library is an ongoing effort to
provide a high-level access to such models relying on PyTorch.

## Installing

Clone this repo and add it to your python library path.

* [PyTorch](http://pytorch.org)
* [NumPy](http://numpy.org/)

### Getting started

### Importing libraries

```python
import anuvada
import numpy as np
import torch
import pandas as pd
```

```python
from anuvada.models.classification_attention_rnn import AttentionClassifier
```

### Creating the dataset

```python
from anuvada.datasets.data_loader import CreateDataset
from anuvada.datasets.data_loader import LoadData
```

```python
data = CreateDataset()
```

```python
df = pd.read_csv('MovieSummaries/movie_summary_filtered.csv')
```

```python
# passing only the first 512 samples, I don't have a GPU!
y = list(df.Genre.values)[0:512]
x = list(df.summary.values)[0:512]
```

```python
x, y = data.create_dataset(x,y, folder_path='test', max_doc_tokens=500)
```

### Loading created dataset

```python
l = LoadData()
```

```python
x, y, token2id, label2id, lengths_mask = l.load_data_from_path('test')
```

### Change into torch vectors

```python
x = torch.from_numpy(x)
```

```python
y = torch.from_numpy(y)
```

### Create attention classifier

```python
acf = AttentionClassifier(vocab_size=len(token2id),embed_size=25,gru_hidden=25,n_classes=len(label2id))
```

```python
loss = acf.fit(x,y, lengths_mask ,epochs=5)
```

Epoch 1 / 5
    [========================================] 100%	loss: 3.9904loss: 3.9904

Epoch 2 / 5
    [========================================] 100%	loss: 3.9851loss: 3.9851

Epoch 3 / 5
    [========================================] 100%	loss: 3.9783loss: 3.9783

Epoch 4 / 5
    [========================================] 100%	loss: 3.9739loss: 3.9739

Epoch 5 / 5
    [========================================] 100%	loss: 3.9650loss: 3.9650

## To do list

- [x] Implement Attention with RNN
- [ ] Implement Attention Visualisation
- [x] Implement working Fit Module
- [x] Implement support for masking gradients in RNN (Working now!)
- [x] Implement a generic data set loader
- [ ] Implement CNN Classifier with feature map visualisation

## Acknowledgments

* https://github.com/henryre/pytorch-fitmodule

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