trained models on omniglot

DataLoader/__pycache__/dataLoaders.cpython-37.pyc

DataLoader/__pycache__/prepare_omniglot.cpython-37.pyc

__pycache__/config.cpython-37.pyc

distributions/__pycache__/__init__.cpython-37.pyc

distributions/__pycache__/bernoulli.cpython-37.pyc

distributions/__pycache__/distribution.cpython-37.pyc

distributions/__pycache__/round.cpython-37.pyc

distributions/__pycache__/utils.cpython-37.pyc

experiments/omniglot_binary_classif.py

...iglot_classif/MNIST_Stochastic_ST_maxpooling_mixt_acc.png

...glot_classif/MNIST_Stochastic_ST_maxpooling_mixt_loss.png

...cc_loss/Omniglot_classif/MNIST_Stochastic_ST_mixt_acc.png

...c_loss/Omniglot_classif/MNIST_Stochastic_ST_mixt_loss.png

...ss/Omniglot_classif/Omniglot_classif_NonBinaryNet_acc.png

...s/Omniglot_classif/Omniglot_classif_NonBinaryNet_loss.png

..._classif/Omniglot_classif_NonBinaryNet_maxpooling_acc.png

...classif/Omniglot_classif_NonBinaryNet_maxpooling_loss.png

.../Omniglot_classif_Stochastic_ST_first_conv_binary_acc.png

...Omniglot_classif_Stochastic_ST_first_conv_binary_loss.png

...lassif_Stochastic_ST_first_conv_binary_maxpooling_acc.png