# Configuration file for MNIST Classifier Training

# Model Configuration
model:
  type: 'cnn'  # Options: 'cnn', 'fc'
  dropout_rate: 0.3
  num_classes: 10

# Training Configuration
training:
  epochs: 20
  batch_size: 128
  initial_lr: 0.001
  optimizer: 'adamw'  # Options: 'adam', 'adamw', 'sgd'
  weight_decay: 0.0001
  scheduler: 'onecycle'  # Options: 'cosine', 'onecycle', 'step'
  warmup_epochs: 2
  early_stop_patience: 7
  gradient_clip_norm: 1.0

# Data Configuration
data:
  data_dir: './data'
  val_split: 0.1  # 10% of training data for validation
  num_workers: 4
  pin_memory: true
  
# Data Augmentation (for training only)
augmentation:
  rotation_degrees: 10
  translate: 0.1
  scale_range: [0.9, 1.1]
  random_erasing_prob: 0.1

# Hardware Configuration
hardware:
  use_gpu: true
  use_amp: false  # Automatic Mixed Precision (set to true for faster training on modern GPUs)
  
# Logging and Saving
logging:
  save_dir: './checkpoints'
  log_dir: './runs'
  save_freq: 5  # Save checkpoint every N epochs
  
# Reproducibility
seed: 42