ComfyUI Node: NNT Training Hyperparameters
Category
NNT Neural Network Toolkit/Models
Inputs
experiment_name STRING
batch_size INT
epochs INT
optimizer
- Adadelta
- Adagrad
- Adam
- AdamW
- SparseAdam
- Adamax
- ASGD
- NAdam
- RAdam
- RMSprop
- Rprop
- SGD
learning_rate FLOAT
weight_decay FLOAT
momentum FLOAT
loss_function
- L1Loss
- MSELoss
- CrossEntropyLoss
- CTCLoss
- NLLLoss
- PoissonNLLLoss
- GaussianNLLLoss
- KLDivLoss
- BCELoss
- BCEWithLogitsLoss
- MarginRankingLoss
- HingeEmbeddingLoss
- MultiLabelMarginLoss
- HuberLoss
- SmoothL1Loss
- SoftMarginLoss
- MultiLabelSoftMarginLoss
- CosineEmbeddingLoss
- MultiMarginLoss
- TripletMarginLoss
- TripletMarginWithDistanceLoss
reduction
- mean
- sum
- none
weight_enabled
- True
- False
class_weights STRING
margin FLOAT
use_lr_scheduler
- True
- False
scheduler_type
- StepLR
- ReduceLROnPlateau
- CosineAnnealingLR
scheduler_step_size INT
scheduler_gamma FLOAT
min_lr FLOAT
use_early_stopping
- True
- False
patience INT
min_delta FLOAT
Outputs
DICT
STRING
Extension: ComfyUI Neural Network Toolkit NNT
Neural Network Toolkit (NNT) for ComfyUI is an extensive set of custom ComfyUI nodes for designing, training, and fine-tuning neural networks. This toolkit allows defining models, layers, training workflows, transformers, and tensor operations in a visual manner using nodes.
Authored by inventorado
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