This is a port of chainer-siamese and a fork of pytorch-siamese. It implements additional training datasets, as well as few-shot prediction code for the siamese network.
This installation requires cuda
to be installed.
$ poetry install
or
$ python3 -m venv env
$ source env/bin/activate
$ pip install -r requirements.txt
$ env/bin/python train_mnist.py --epoch 10
$ env/bin/python train_omniglot.py --epoch 10
$ env/bin/python train_omniglot_by_alphabet.py --epoch 10
This dumps for every epoch the current state
and creates a result.png
. Model
state is saved in a new directory unique to each run.