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cfg_sample.yaml
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22 lines (19 loc) · 1.02 KB
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num_epochs: 100 # Number of Epochs to train
batch_size: 64 # Batch Size
route_iters: 3 # Routing Iterations for capsules module
use_cuda: True # If using CUDA support
loss:
with_reconstruction: True # Use reconstruction loss of not
rc_loss_weight: 0.0005 # Weight balance for classification margin loss and reconstruction mse loss
optim:
lr: 0.001 # Adam optimizer initial learning rate
step_decay: 0.5 # Step decay for MultiStepLRScheduler
milestone: [30, 70] # Milestone for MultiStepLRScheduler
dataloader:
num_workers: 4 # Data loader multiprocess workers
pin_memory: True # Using CUDA pin mmemory
checkpoint:
model_path: "capsnet.pt" # Model state path to save, would be join we log_dir below
log_dir: "checkpoint" # Logger directory, including model state path and tensorboard event file
use_checkpoint: True # Using previous check point in log_dir for resuming training
use_best: False # Start training for best model in model state file