Experiments with Adam

This repo contains the scripts used to perform the experiments in this blog post. If you're using this code or our results, please cite it appropriately.

You will find

• a script to train cifar10 to >94% accuracy in 30 epochs without Test Time Augmentation or 18 with.
• a script to finetune a pretrained resnet50 on the Standford cars dataset to 90% accuracy in 60 epochs.
• a script to train an AWD LSTM (or QRNN) to the same perplexity as the Saleforce team that created them but in just 90 epochs.

Requirements

• the fastai library is necessary to run all the models. If you didn't pip-install it, don't forget to have a simlink pointing to it in the directory where you clone this repo.
• pytorch 0.4.0 is necessary to have the implementation of amsgrad inside Adam.
• additonaly, the QRNNs model requires the cupy library.

To install

Run the script get_data.sh that will download and organize the data needed for the models

Experiments

Cifar10 dataset

• this should train cifar10 to 94.25% accuracy (on average):

  python train_cifar10.py 3e-3 --wd=0.1 --wd_loss=False

• this should train cifar10 to 94% accuracy (on average) but faster:

  python train_cifar10.py 3e-3 --wd=0.1 --wd_loss=False --cyc_len=18 --tta=True

Stanford cars dataset

• this should get 90% accuracy (on average) without TTA, 91% with:

  python fit_stanford_cars.py '(1e-2,3e-3)' --wd=1e-3 --tta=True

Language models

• this should train an AWD LSTM to 68.7/65.5 perplexity without cache pointer, 52.9/50.9 with

  python train_rnn.py 5e-3 --wd=1.2e-6 --alpha=3 --beta=1.5

• this should train an AWD QRNN to 69.6/66.7 perplexity without cache pointer, 53.6/51.7 with

  python train_rnn.py 5e-3 --wd=1e-6 --qrnn=True