How to Use Tefla: Simple Deep Learning Wrapper for Tensorflow

Published Apr 17, 2017Last updated Apr 19, 2017

I have been using this framework for about 4 months now and I find it very intriguing. What I personally love about this framework is how easy it is to use. Just three to four commands and you're all set!

So let's cut to the chase and start coding!!
If you want more instructions, you can visit https://github.com/litan/tefla.

Installing Tefla

First, you have to install it by downloading the source code and following the instructions here.

Pre-Process the Data

This step is somewhat optional — you can decide how you want to process your data. Different problems need different preprocessing techniques. And for that, Tefla provides some very good cropping methods.
For more information, click here.

Keep these two things in mind:

For a dominant object cropping, you can use
convert.convert(fname,target_size):

This takes two parameters fname, which is your full file path, and the shape of target image that you want. Keep in mind, in neural networks, it is necessary to resize your image so that it matches your input dimensions.
For all object cropping (crop_img()):

Sometimes, you might want to remove the redundant background and keep all the objects in the image — this is where this function comes in.
You can find the instruction here.

Essentially, both methods take the same input.
You can explore and play with it and then decide which function suits your data better.

Directory Structure

|-- Data_Dir

|   |-- training_image_size (eg. training_256, for 256 image size) 

|   |-- validation_image_size (eg. validation_256, for 256 image size) 

|   |-- test_image_size (eg. test_256, for 256 image size) 

|   |-- training_labels.csv(eg. data -> filename,label, header -> image,label)

|   |-- validation_labels.csv(eg. data -> filename,label, header -> image,label)

|   |-- test_labels.csv(eg. data -> filename,label, header -> image,label)

Structuring your data is important when you're using Tefla — this framework is based more on file operations, unlike Keras, which is based more on array operations.

To process faster when you're using Tefla, buy SSD. If you're using Keras, you should have a good amount of RAM. With that said, this is only relevant if you have a large amount of data. For small amounts of data (tens of gb's), Tefla and Keras are quite similiar.

To lower memory usage, you can use batch generators in Keras. However, they can get tricky sometimes. If you hate making batch generators, then Tefla is perfect for you

Building a Model

Let's get to the fun part!

In tefla.experiments, you can find prebuild models — that is exactly what you need for this step.

Open the file here abd find a model that can give you up to 99.74% accuracy on mnist data set.

Twist this model and beat the record of 99.79% on mnist dataset.
Cheers!!!

Training a Model

Just one command and you could be done with training!

Type:

python -m tefla.train --help

for help and type:

python -m tefla.train --model path/to/your/model --training_cnf path/to/train_cnf.py/for/that/model --data_dir /path/to/data/dir

Here, the train_cnf.py file is a config file, which is required for Tefla to do magics like:

data augmentation
making summary for tensorboard
adding reqularization
setting batch size,
and selecting optimizers

This is the most important file, which you have to consider while training your model. Tweaking this file can shoot accuracy of your model or drop if you did something wrong.

Retrain your model from any epoch by passing start_epoch and resume_lr parameters in the train command. It is that simple!!

Predict by Using Trained Model

python -m tefla.predict --help

Pass test_image_size directory in predict_dir argument.
Make sure model and training_cnf remains the same as that of training.
weights_from will pass the path of the epoch weights, with which you want to start prediction.

Note: the weights will be stored in the weights folder, wich you can find in the root directory.

And that is it — you are done with prediction.

Test Your Model

python -m tefla.metrics --help

Pass test_labels.csv --actual_labels_file arg.
Pass data_dir/predictions/prediction_class.csv in --predict_labels_file arg.
Press enter and you should see a confusion matrix with various other parameters and score.