Tefla is built on top of Tensorflow. It provides higher level access to tensorflow's features. Inerface, Easy to build complex models.
Tefla features:
. Support for data-sets, data-augmentation
. easy to define complex deep models
. single and multi GPU training
. various prediction fnctions including ensembling of models
. different metrics for performance measurement\
. custom losses
. learning rate schedules, polynomial, step, validation_loss based
Tefla requires Tensorflow(version >=r1.0)
pip install scipy
pip install git+https://github.com/lucasb-eyer/pydensecrf.git
For the latest stable version:
pip install tefla
for current version installation:
pip install git+https://github.com/n3011/tefla.git
For Developer / TO Work with source and modifying source code:
git clone https://github.com/n3011/tefla.git
cd tefla
export PYTHONPATH=.
Many examples available with recent deep learning research integration
- VGG19 Trained on ImageNet Note: (Model is converted from original Caffe version
- Inception_Resnet_v2 trained on ImageNet Note: model is converted from original tf version, trained by Google brain
Recent deep convolutional models are easy to implement using TEFLA
- Its as easy as
>>>from tefla.core.layers import conv2d
>>>convolved = conv2d(input, 48, False, None)
|-- 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
1. [Train v2](https://github.com/n3011/tefla/blob/master/tefla/trainv2.py)
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 gives a overview about Tefla usages
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)
Welcome to the first release of Tefla, if you find any bug, please report it in the GitHub issues section.
Improvements and requests for new features are more than welcome! Do not hesitate to twist and tweak Tefla, and send pull-requests.
Note: This project BASE is jointly developed with Artelus team: www.github.com/litan/tefla. Both projects are evolving independently, with a cross-pollination of ideas.