[WIP] code

code for experimenting with jointly optimising embeddings.

Getting started

Start with cloning the repo:

git clone https://github.com/attaullah/JOE.git
cd JOE/

Environment setup

Required packages

tensorflow
scikit-learn
absl-py
scipy

and pip packages

tensorflow-addons
tensorflow-datasets

Data preparation

MNIST, Fashion-MNIST, SVHN, and CIFAR-10 datasets are loaded using TensorFlow datasets. package. By default, tensorflow_datasets package will save datasets at ~/ tensorflow_datasets/ directory.

For PlantVillage dataset [1] please follow instructions at plant-disease-dataset. The downloaded files are expected to be saved in the data/ directory.

Example usage

Training can be started using the train.py script. Details of self-explanatory command-line arguments can be seen by passing --helpfull to it.

 python joe.py --helpfull
 
       USAGE: joe.py [flags]
flags:
  --batch_size: size of mini-batch
    (default: '128')
    (an integer)
  --confidence_measure: <1-nn>: confidence measure for pseudo-label selection.
    (default: '1-nn')
  --dataset: <cifar10|svhn|plant32|plant64|plant96>: dataset name
    (default: 'cifar10')
  --epochs: initial training epochs
    (default: '200')
    (an integer)
  --epochs_per_m_iteration: number of epochs per meta-iterations
    (default: '200')
    (an integer)
  --gpu: gpu id
    (default: '0')
  --lbl: <knn|lda|rf|lr>: shallow classifiers for labelling for metric learning losses
    (default: 'knn')
  --lr: learning_rate
    (default: '0.001')
    (a number)
  --lt: <cross-entropy|triplet>: loss_type: cross-entropy, triplet,  arcface or contrastive.
    (default: 'cross-entropy')
  --margin: margin for triplet loss calculation
    (default: '1.0')
    (a number)
  --meta_iterations: number of meta_iterations
    (default: '25')
    (an integer)
  --network: <wrn-28-2|resnet18|vgg16|resnet34|resnet20|resnet50>: network architecture.
    (default: 'wrn-28-2')
  --opt: <adam|sgd|sgdw|rmsprop>: optimizer.
    (default: 'adam')
  --pre: prefix for log directory
    (default: '')
  --[no]self_training: apply self-training
    (default: 'false')
  --[no]semi: True: N-labelled training, False: All-labelled training
    (default: 'true')
  --verbose: verbose
    (default: '1')
    (an integer)
  --[no]weights: random or ImageNet pretrained weights