A Re-implementation of Fixed-update Initialization (https://arxiv.org/abs/1901.09321). (requires Pytorch 1.0)

Cite as:

Hongyi Zhang, Yann N. Dauphin, Tengyu Ma. Fixup Initialization: Residual Learning Without Normalization. 7th International Conference on Learning Representations (ICLR 2019).

The default arguments will train a ResNet-110 (https://arxiv.org/abs/1512.03385) with Fixup + Mixup (https://arxiv.org/abs/1710.09412).

Example:

The following script will train a ResNet-32 model (https://arxiv.org/abs/1512.03385) on GPU 0 with Fixup and no Mixup (alpha=0), with weight decay 5e-4 and (the default) learning rate 0.1 and batch size 128.

CUDA_VISIBLE_DEVICES=0 python cifar_train.py -a fixup_resnet32 --sess benchmark_a0d5e4lr01 --seed 11111 --alpha 0. --decay 5e-4

ImageNet models with training scripts are now available. (Thanks @tjingrant for help!)

Top-1 accuracy for ResNet-50 at Epoch 100 with Mixup (alpha=0.7) is around 76.0%.

Transformer model with Fixup (instead of layer normalization) is available. To run the experiments, you will need to download and install the fairseq library (the provided code was tested on an earlier version: https://github.com/pytorch/fairseq/tree/5d00e8eea2644611f397d05c6c8f15083388b8b4). You can then copy the files into corresponding folders.

An example script run.sh is provided to run the IWSLT experiments described in the paper. For more information, please refer to the instructions in fairseq repo (https://github.com/pytorch/fairseq/tree/5d00e8eea2644611f397d05c6c8f15083388b8b4/examples/translation).