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experiment_group: Sample Experiments
experiments:
- dataloader: EmnistDataLoaders
data_loader_args:
splits: [train, val]
sample_to_balance: true
subsample_fraction: null
transform: null
target_transform: null
batch_size: 256
shuffle: true
num_workers: 8
cuda: true
seed: 4711
model: CharacterModel
metrics: [accuracy]
# network: MLP
# network_args:
# input_size: 784
# output_size: 62
# num_layers: 3
network: LeNet
network_args:
input_size: [28, 28]
output_size: 62
train_args:
batch_size: 256
epochs: 16
criterion: CrossEntropyLoss
criterion_args:
weight: null
ignore_index: -100
reduction: mean
# optimizer: RMSprop
# optimizer_args:
# lr: 1.e-3
# alpha: 0.9
# eps: 1.e-7
# momentum: 0
# weight_decay: 0
# centered: false
optimizer: AdamW
optimizer_args:
lr: 1.e-2
betas: [0.9, 0.999]
eps: 1.e-08
weight_decay: 0
amsgrad: false
# lr_scheduler: null
lr_scheduler: OneCycleLR
lr_scheduler_args:
max_lr: 1.e-3
epochs: 16
callbacks: [Checkpoint, EarlyStopping, WandbCallback, WandbImageLogger, OneCycleLR]
callback_args:
Checkpoint:
monitor: val_accuracy
EarlyStopping:
monitor: val_loss
min_delta: 0.0
patience: 3
mode: min
WandbCallback:
log_batch_frequency: 10
WandbImageLogger:
num_examples: 4
OneCycleLR:
null
verbosity: 1 # 0, 1, 2
resume_experiment: null
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