mnist_plotly_tensorboard.yaml

exp:
  dir: logs/mnist/views_plotly_tensorboard
  resume: # last, best_[...], or empty (from scratch)
dataset:
  import: mnist.datasets.factory
  name: mnist
  dir: data/mnist
  train_split: train
  eval_split: val
  nb_threads: 4
  batch_size: 64
model:
  name: simple
  network:
    import: mnist.models.networks.factory
    name: net
  criterion:
    name: nll
  metric:
    name: accuracy
    topk: [1,5]
optimizer:
  name: sgd
  lr: 0.01
  momentum: 0.5
engine:
  name: default
  debug: False
  nb_epochs: 10
  print_freq: 10
  saving_criteria:
  - loss:min          # save when new_best < best
  - accuracy_top1:max # save when new_best > best
  - accuracy_top5:max # save when new_best > best
misc:
  cuda: True
  seed: 1337
views:
  names:
  - plotly
  - tensorboard
  items_plotly:
  - logs:train_epoch.loss+logs:eval_epoch.loss
  - logs:train_epoch.accuracy_top1+logs:eval_epoch.accuracy_top1
  items_tensorboard:
  - logs:train_batch.loss+logs:eval_batch.loss
  - logs:train_batch.accuracy_top1+logs:eval_batch.accuracy_top1