Train longer, generalize better - Big batch training

This is a code repository used to generate the results appearing in "Train longer, generalize better: closing the generalization gap in large batch training of neural networks" By Elad Hoffer, Itay Hubara and Daniel Soudry.

It is based off convNet.pytorch with some helpful options such as:

Training on several datasets
Complete logging of trained experiment
Graph visualization of the training/validation loss and accuracy
Definition of preprocessing and optimization regime for each model

Dependencies

pytorch
torchvision to load the datasets, perform image transforms
pandas for logging to csv
bokeh for training visualization

Data

Configure your dataset path at data.py.
To get the ILSVRC data, you should register on their site for access: http://www.image-net.org/

Experiment examples

python main_normal.py --dataset cifar10 --model resnet --save cifar10_resnet44_bs2048_lr_fix --epochs 100 --b 2048 --lr_bb_fix;
python main_normal.py --dataset cifar10 --model resnet --save cifar10_resnet44_bs2048_regime_adaptation --epochs 100 --b 2048 --lr_bb_fix --regime_bb_fix;
python main_gbn.py --dataset cifar10 --model resnet --save cifar10_resnet44_bs2048_ghost_bn256 --epochs 100 --b 2048 --lr_bb_fix --mini-batch-size 256;
python main_normal.py --dataset cifar100 --model resnet --save cifar100_wresnet16_4_bs1024_regime_adaptation --epochs 100 --b 1024 --lr_bb_fix --regime_bb_fix;
python main_gbn.py --model mnist_f1 --dataset mnist --save mnist_baseline_bs4096_gbn --epochs 50 --b 4096 --lr_bb_fix --no-regime_bb_fix --mini-batch-size 128;

See run_experiments.sh for more examples
Model configuration

Network model is defined by writing a .py file in models folder, and selecting it using the model flag. Model function must be registered in models/__init__.py The model function must return a trainable network. It can also specify additional training options such optimization regime (either a dictionary or a function), and input transform modifications.

e.g for a model definition:

class Model(nn.Module):

    def __init__(self, num_classes=1000):
        super(Model, self).__init__()
        self.model = nn.Sequential(...)

        self.regime = {
            0: {'optimizer': 'SGD', 'lr': 1e-2,
                'weight_decay': 5e-4, 'momentum': 0.9},
            15: {'lr': 1e-3, 'weight_decay': 0}
        }

        self.input_transform = {
            'train': transforms.Compose([...]),
            'eval': transforms.Compose([...])
        }
    def forward(self, inputs):
        return self.model(inputs)

 def model(**kwargs):
        return Model()

Train longer, generalize better - Big batch training

Dependencies

Data

Experiment examples

Model configuration