b2txt25/model_training_nnn/rnn_args.yaml

model:
  n_input_features: 512 # number of input features in the neural data. (2 features per electrode, 256 electrodes)
  n_units: 768 # number of units per GRU layer
  rnn_dropout: 0.4 # dropout rate for the GRU layers
  rnn_trainable: true # whether the GRU layers are trainable
  n_layers: 5 # number of GRU layers
  patch_size: 14 # size of the input patches (14 time steps)
  patch_stride: 4 # stride for the input patches (4 time steps)

  input_network:
    n_input_layers: 1 # number of input layers per network (one network for each day)
    input_layer_sizes:
    - 512 # size of the input layer (number of input features)
    input_trainable: true # whether the input layer is trainable
    input_layer_dropout: 0.2 # dropout rate for the input layer

gpu_number: '1' # GPU number to use for training, formatted as a string (e.g., '0', '1', etc.)
mode: train
use_amp: true # whether to use automatic mixed precision (AMP) for training

# TPU and distributed training settings
use_tpu: false # whether to use TPU for training (set to true for TPU)
num_tpu_cores: 8 # number of TPU cores to use (typically 8 for v3-8 or v4-8)
gradient_accumulation_steps: 1 # number of gradient accumulation steps for distributed training
dataloader_num_workers: 0 # set to 0 for TPU to avoid multiprocessing issues

output_dir: trained_models/baseline_rnn # directory to save the trained model and logs
checkpoint_dir: trained_models/baseline_rnn/checkpoint # directory to save checkpoints during training
init_from_checkpoint: false # whether to initialize the model from a checkpoint
init_checkpoint_path: None # path to the checkpoint to initialize the model from, if any
save_best_checkpoint: true # whether to save the best checkpoint based on validation metrics
save_all_val_steps: false # whether to save checkpoints at all validation steps
save_final_model: false # whether to save the final model after training
save_val_metrics: true # whether to save validation metrics during training
early_stopping: false # whether to use early stopping based on validation metrics
early_stopping_val_steps: 20 # number of validation steps to wait before stopping training if no improvement is seen

num_training_batches: 120000 # number of training batches to run
lr_scheduler_type: cosine # type of learning rate scheduler to use
lr_max: 0.005 # maximum learning rate for the main model
lr_min: 0.0001 # minimum learning rate for the main model
lr_decay_steps: 120000 # number of steps for the learning rate decay
lr_warmup_steps: 1000 # number of warmup steps for the learning rate scheduler
lr_max_day: 0.005 # maximum learning rate for the day specific input layers
lr_min_day: 0.0001 # minimum learning rate for the day specific input layers
lr_decay_steps_day: 120000 # number of steps for the learning rate decay for the day specific input layers
lr_warmup_steps_day: 1000 # number of warmup steps for the learning rate scheduler for the day specific input layers

beta0: 0.9 # beta0 parameter for the Adam optimizer
beta1: 0.999 # beta1 parameter for the Adam optimizer
epsilon: 0.1 # epsilon parameter for the Adam optimizer
weight_decay: 0.001 # weight decay for the main model
weight_decay_day: 0 # weight decay for the day specific input layers
seed: 10 # random seed for reproducibility
grad_norm_clip_value: 10 # gradient norm clipping value

batches_per_train_log: 200 # number of batches per training log
batches_per_val_step: 2000 # number of batches per validation step

batches_per_save: 0 # number of batches per save
log_individual_day_val_PER: true # whether to log individual day validation performance
log_val_skip_logs: false # whether to skip logging validation metrics
save_val_logits: true # whether to save validation logits
save_val_data: false # whether to save validation data

dataset:
  data_transforms:
    white_noise_std: 1.0 # standard deviation of the white noise added to the data
    constant_offset_std: 0.2 # standard deviation of the constant offset added to the data
    random_walk_std: 0.0 # standard deviation of the random walk added to the data
    random_walk_axis: -1 # axis along which the random walk is applied
    static_gain_std: 0.0 # standard deviation of the static gain applied to the data
    random_cut: 3 # number of time steps to randomly cut from the beginning of each batch of trials
    smooth_kernel_size: 100 # size of the smoothing kernel applied to the data
    smooth_data: true # whether to smooth the data
    smooth_kernel_std: 2 # standard deviation of the smoothing kernel applied to the data

  neural_dim: 512 # dimensionality of the neural data
  batch_size: 64 # batch size for training
  n_classes: 41 # number of classes (phonemes) in the dataset
  max_seq_elements: 500 # maximum number of sequence elements (phonemes) for any trial
  days_per_batch: 4 # number of randomly-selected days to include in each batch
  seed: 1 # random seed for reproducibility
  num_dataloader_workers: 4 # number of workers for the data loader
  loader_shuffle: false # whether to shuffle the data loader
  must_include_days: null # specific days to include in the dataset
  test_percentage: 0.1 # percentage of data to use for testing
  feature_subset: null # specific features to include in the dataset

  dataset_dir: ../data/hdf5_data_final # directory containing the dataset
  bad_trials_dict: null # dictionary of bad trials to exclude from the dataset
  sessions: # list of sessions to include in the dataset
  - t15.2023.08.11
  - t15.2023.08.13
  - t15.2023.08.18
  - t15.2023.08.20
  - t15.2023.08.25
  - t15.2023.08.27
  - t15.2023.09.01
  - t15.2023.09.03
  - t15.2023.09.24
  - t15.2023.09.29
  - t15.2023.10.01
  - t15.2023.10.06
  - t15.2023.10.08
  - t15.2023.10.13
  - t15.2023.10.15
  - t15.2023.10.20
  - t15.2023.10.22
  - t15.2023.11.03
  - t15.2023.11.04
  - t15.2023.11.17
  - t15.2023.11.19
  - t15.2023.11.26
  - t15.2023.12.03
  - t15.2023.12.08
  - t15.2023.12.10
  - t15.2023.12.17
  - t15.2023.12.29
  - t15.2024.02.25
  - t15.2024.03.03
  - t15.2024.03.08
  - t15.2024.03.15
  - t15.2024.03.17
  - t15.2024.04.25
  - t15.2024.04.28
  - t15.2024.05.10
  - t15.2024.06.14
  - t15.2024.07.19
  - t15.2024.07.21
  - t15.2024.07.28
  - t15.2025.01.10
  - t15.2025.01.12
  - t15.2025.03.14
  - t15.2025.03.16
  - t15.2025.03.30
  - t15.2025.04.13
  dataset_probability_val: # probability of including a trial in the validation set (0 or 1)
  - 0 # no val or test data from this day
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 0 # no val or test data from this day
  - 1
  - 1
  - 1
  - 0 # no val or test data from this day
  - 0 # no val or test data from this day
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1
  - 1