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https://github.com/openagi/tefla
Tensorflow based deep-learning library.
https://github.com/openagi/tefla
ai deep-learning gan generative-adversarial-network lstm ml nlp tensorflow-framework
Last synced: about 1 month ago
JSON representation
Tensorflow based deep-learning library.
- Host: GitHub
- URL: https://github.com/openagi/tefla
- Owner: openAGI
- License: other
- Created: 2016-11-14T09:56:19.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T21:55:00.000Z (over 1 year ago)
- Last Synced: 2024-10-14T21:40:54.206Z (about 1 month ago)
- Topics: ai, deep-learning, gan, generative-adversarial-network, lstm, ml, nlp, tensorflow-framework
- Language: Python
- Homepage: https://openAGI.github.io/tefla/
- Size: 3.54 MB
- Stars: 55
- Watchers: 6
- Forks: 10
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
----------------
[](https://openagi.github.io/tefla/)
[](https://travis-ci.org/openAGI/tefla)
[](https://badge.fury.io/py/tefla)
[](https://github.com/openagi/tefla/blob/master/LICENSE)
Tefla is built on top of Tensorflow for fast prototyping of deep learning algorithms. It provides high level access to the features of tensorflow. An interface to easily build complex models.
## Why Tefla?
Tefla has several advantages over Keras or any other library. You may always ask why Tensorflow instead of PyTorch, Caffe, MXNet, and others? Its an effort from an Indian AI company and AI developers to make AI accessible to all.
1. Tefla allows you to design complex models at ease.
2. Tefla guarantees scalability, portability and reproducibility.
3. Tefla's models repo provides state-of-the-art deep learning methods implementation for free.
4. Tefla makes the deep model deployment very easy; it has inbuilt API to serve the trained model.
5. Tefla comes with API for segmentation learning, semi-supervised learning, and generative model training.
6. Tefla has faster input pipeline.
7. Tefla enables faster model training with Multi-GPU support.
8. Tefla also comes with language modeling tools, encoder, decoder framework support.
9. Tefla also supports reinforcement learning modeling and training.
Tefla features:
. Supports custom optimizers
. Supports data-sets, data-augmentation, and others
. Supports text datasets
. Easy to define complex deep models
. Single and multi GPU training
. Various prediction functions including ensembling of models
. Different metrics for performance measurement
. Custom losses
. Learning rate schedules, polynomial, step, validation_loss based
. Semantic segmentation learning
. Semi-supervised learning
## Installation
### Prerequisite to install Tefla
Before you install Tefla you need to install Tensorflow version r1.8.0 or later.
```Shell
pip install tensorflow-gpu
or
pip install tensorflow
```
### Install Tefla
The latest release of Tefla is version 1.9.0.
- To install the latest stable version:
```python
pip install tefla
```
- To install the current version:
```python
pip install git+https://github.com/openagi/tefla.git
```
- To develop or work with source and modifying source code:
```Shell
git clone https://github.com/openagi/tefla.git
cd tefla
pip install -r requirements.txt
export PYTHONPATH=.
```
## Documentation
For more information about installing, configuring, and managing see, [Tefla Docs](https://openAGI.github.io/tefla/)
## Tefla Models
Recent deep convolutional models are easy to implement using Tefla. For more information about the latest state-of-the-art models that are implemented using tefla, see [Recent Models](https://github.com/openagi/models)
## Getting Started with just three easy steps
1. Import the layers
```python
>>>from tefla.core.layers import conv2d
>>>convolved = conv2d(input, 48, False, None)
```
2. Create the data directory
- Data Directory structure for using normal images
```Shell
|-- Data_Dir
| |-- training_image_size (eg. training_256, for 256 image size)
| |-- validation_image_size (eg. validation_256, for 256 image size)
| |-- training_labels.csv
| |-- validation_labels.csv
```
- TFRecords support available using tefla/dataset class and [Train v2](https://github.com/openagi/tefla/blob/master/tefla/trainv2.py)
3. Run the training
```Shell
python tefla/train.py --model models/alexnet.py --training_cnf models/multiclass_cnf.py --data_dir /path/to/data/dir (as per instructions 2.a)
```
## MNIST example that gives a overview about how to use Tefla
```python
image_size =(32, 32)
crop_size = (28, 28)
def model(is_training, reuse):
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(batch_norm=True, activation=prelu, **common_args)
fc_args = make_args(activation=prelu, **common_args)
logit_args = make_args(activation=None, **common_args)
x = input((None, height, width, 1), **common_args)
x = conv2d(x, 32, name='conv1_1', **conv_args)
x = conv2d(x, 32, name='conv1_2', **conv_args)
x = max_pool(x, name='pool1', **common_args)
x = dropout(x, drop_p=0.25, name='dropout1', **common_args)
x = fully_connected(x, n_output=128, name='fc1', **fc_args)
x = dropout(x, drop_p=0.5, name='dropout2', **common_args)
logits = fully_connected(x, n_output=10, name="logits", **logit_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
training_cnf = {
'classification': True,
'validation_scores': [('validation accuracy', util.accuracy_wrapper), ('validation kappa', util.kappa_wrapper)],
'num_epochs': 50,
'lr_policy': StepDecayPolicy(
schedule={
0: 0.01,
30: 0.001,
}
)
}
util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO)
trainer = SupervisedTrainer(model, training_cnf, classification=training_cnf['classification'])
trainer.fit(data_set, weights_from=None, start_epoch=1, verbose=1, summary_every=10)
```
## Contributions
Welcome to the fourth release of Tefla, if you find any bug, please report the issue on GitHub. Improvements and requests for new features are more than welcome. Do not hesitate to twist and tweak Tefla, and send pull-requests.
## License
[MIT License](https://openAGI.github.io/tefla/license/)