mnist_fp.yaml

# Validation set evaluation metrics:
# Top-1 Accuracy: 99.4%
# Top-5 Accuracy: 100.0%
seed: null
environment:
    platform: local
    cuda:
        cudnn_deterministic: true
        cudnn_benchmark: false
data:
    dataset_path: data/mnist/
    download: true
    train_batch_size: 64
    test_batch_size: 5000
    workers: 4
model:
    architecture: lenet5
    loss: nll_loss                              # select from {'cross_entropy', 'nll_loss', 'kl_div'}, see get_loss_fn() docs for details.
    arch_config:
        moving_average_mode: 'off'              # select from {'off', 'eval_only', 'train_and_eval'}, see ActivationQuantizer docs for details.
        moving_average_momentum: 0.99
        x_quant: fp                             # select from {'fp', 'ls-1', 'ls-T', 'ls-2', 'gf-1', 'gf-2', 'gf-3' (any `gf-k`)}, see QuantConv2d docs for details.
        w_quant: fp                             # select from {'fp', 'ls-1', 'ls-T', 'ls-2', 'gf-1', 'gf-2', 'gf-3' (any `gf-k`)}, see QuantConv2d docs for details.
        clamp:
            kind: identity                      # select from {'identity', 'symmetric'}, see QuantConv2d docs for details.
        conv1_filters: 20
        conv2_filters: 50
        output_classes: 10
optimization:
    epochs: 10
    optimizer:
        algorithm: adadelta                     # select from {'sgd', 'adam', 'adadelta'}, see get_optimizer() docs for details.
        lr: 1.0
    lr_scheduler:
        scheduler: step_lr                      # select from {'step_lr', 'multi_step_lr', 'linear_lr', 'lambda_lr'}, see get_lr_scheduler() docs for details.
        step_size: 1
        gamma: 0.7
log:
    level: INFO
    interval: 10
    tensorboard: true
    tensorboard_root: runs/
    root_experiments_dir: experiments/
    save_model_freq: 2