experiment_group: Sample Experiments experiments: - dataset: EmnistDataset dataset_args: sample_to_balance: true subsample_fraction: null transform: null target_transform: null seed: 4711 data_loader_args: splits: [train, val] shuffle: true num_workers: 8 cuda: true model: CharacterModel metrics: [accuracy] # network: MLP # network_args: # input_size: 784 # hidden_size: 512 # output_size: 80 # num_layers: 3 # dropout_rate: 0 # activation_fn: SELU network: ResidualNetwork network_args: in_channels: 1 num_classes: 80 depths: [2, 1] block_sizes: [96, 32] # network: LeNet # network_args: # output_size: 62 # activation_fn: GELU train_args: batch_size: 256 epochs: 32 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-03 betas: [0.9, 0.999] eps: 1.e-08 # weight_decay: 5.e-4 amsgrad: false # lr_scheduler: null lr_scheduler: OneCycleLR lr_scheduler_args: max_lr: 1.e-03 epochs: 32 anneal_strategy: linear callbacks: [Checkpoint, ProgressBar, EarlyStopping, WandbCallback, WandbImageLogger, OneCycleLR] callback_args: Checkpoint: monitor: val_accuracy ProgressBar: epochs: 32 log_batch_frequency: 100 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