Welcome to TensorFlowNet!

**TensorFlowNet** is a Machine Learning framework that is built on top of TensorFlow and it uses TensorFlow’s Eager framework for fast research and experimentation. Visualization is done using TensorBoard.

TensorFlowNet is easy to be customized by creating the necessary classes:

  1. Data Loading: a dataset class is required to load the data.
  2. Model Design: a tf.keras.Model class that represents the network model.
  3. Loss Method: an appropriate class for the loss, for example CrossEntropyLoss or MSELoss.
  4. Evaluation Metric: a class to measure the accuracy of the results.


TensorFlowNet consists of the following packages:


This is for loading and transforming datasets.


Network models are kept in this package. It already includes ResNet, PreActResNet, Stacked Hourglass and SphereFace.


There are number of different choices available for Classification or Regression. New loss methods can be put here.


There are number of different choices available for Classification or Regression. New accuracy metrics can be put here.


As of now, the following plugins are available:

  1. ProgressBar:


    • main
    • dataloader
    • checkpoints
    • model
    • train
    • test


First, you need to download TensorFlowNet by calling the following command:

git clone –recursive https://github.com/human-analysis/tensorflownet.git

Since TensorFlowNet relies on several Python packages, you need to make sure that the requirements exist by executing the following command in the tensorflownet directory:

pip install -r requirements.txt


  • If you do not have TensorFlow or it does not meet the requirements, please follow the instruction on the TensorFlow website.

Congratulations!!! You are now ready to use TensorFlowNet!


TensorFlowNet comes with a classification example in which a ResNet model is trained for the CIFAR10 dataset.

python main.py


TensorFlowNet loads its parameters at the beginning via a config file and/or the command line.

Config file

When TensorFlowNet is being run, it will automatically load all parameters from args.txt by default, if it exists. In order to load a custom config file, the following parameter can be used: python main.py –config custom_args.txt



log_type = traditional\ save_results = No\ \ # dataset options\ dataroot = ./data\ dataset_train = CIFAR10\ dataset_test = CIFAR10\ batch_size = 64

Command line

Parameters can also be set in the command line when invoking main.py. These parameters will precede the existing parameters in the configuration file.

python main.py –log-type progressbar