competition update
This commit is contained in:
nckcard
@@ -1,81 +1,89 @@
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model:
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n_input_features: 512
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n_units: 768
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rnn_dropout: 0.4
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rnn_trainable: true
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n_layers: 5
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bidirectional: false
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patch_size: 14
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patch_stride: 4
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n_input_features: 512 # number of input features in the neural data. (2 features per electrode, 256 electrodes)
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n_units: 768 # number of units per GRU layer
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rnn_dropout: 0.4 # dropout rate for the GRU layers
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rnn_trainable: true # whether the GRU layers are trainable
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n_layers: 5 # number of GRU layers
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patch_size: 14 # size of the input patches (14 time steps)
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patch_stride: 4 # stride for the input patches (4 time steps)
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input_network:
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n_input_layers: 1
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n_input_layers: 1 # number of input layers per network (one network for each day)
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input_layer_sizes:
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- 512
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input_trainable: true
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input_layer_dropout: 0.2
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gpu_number: '1'
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distributed_training: false
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- 512 # size of the input layer (number of input features)
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input_trainable: true # whether the input layer is trainable
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input_layer_dropout: 0.2 # dropout rate for the input layer
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gpu_number: '1' # GPU number to use for training, formatted as a string (e.g., '0', '1', etc.)
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mode: train
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use_amp: true
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output_dir: /media/lm-pc/8tb_nvme/b2txt25/rnn_v2_jitter
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init_from_checkpoint: false
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checkpoint_dir: /media/lm-pc/8tb_nvme/b2txt25/rnn_v2_jitter/checkpoint
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init_checkpoint_path: None
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save_best_checkpoint: true
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save_all_val_steps: false
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save_final_model: false
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save_val_metrics: true
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early_stopping: false
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early_stopping_val_steps: 20
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num_training_batches: 120000
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lr_scheduler_type: cosine
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lr_max: 0.005
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lr_min: 0.0001
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lr_decay_steps: 120000
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lr_warmup_steps: 1000
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lr_max_day: 0.005
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lr_min_day: 0.0001
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lr_decay_steps_day: 120000
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lr_warmup_steps_day: 1000
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beta0: 0.9
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beta1: 0.999
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epsilon: 0.1
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weight_decay: 0.001
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weight_decay_day: 0
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seed: 10
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grad_norm_clip_value: 10
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batches_per_train_log: 200
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batches_per_val_step: 2000
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batches_per_save: 0
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log_individual_day_val_PER: true
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log_val_skip_logs: false
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save_val_logits: true
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save_val_data: false
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use_amp: true # whether to use automatic mixed precision (AMP) for training
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output_dir: trained_models/baseline_rnn # directory to save the trained model and logs
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checkpoint_dir: trained_models/baseline_rnn/checkpoint # directory to save checkpoints during training
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init_from_checkpoint: false # whether to initialize the model from a checkpoint
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init_checkpoint_path: None # path to the checkpoint to initialize the model from, if any
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save_best_checkpoint: true # whether to save the best checkpoint based on validation metrics
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save_all_val_steps: false # whether to save checkpoints at all validation steps
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save_final_model: false # whether to save the final model after training
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save_val_metrics: true # whether to save validation metrics during training
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early_stopping: false # whether to use early stopping based on validation metrics
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early_stopping_val_steps: 20 # number of validation steps to wait before stopping training if no improvement is seen
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num_training_batches: 120000 # number of training batches to run
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lr_scheduler_type: cosine # type of learning rate scheduler to use
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lr_max: 0.005 # maximum learning rate for the main model
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lr_min: 0.0001 # minimum learning rate for the main model
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lr_decay_steps: 120000 # number of steps for the learning rate decay
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lr_warmup_steps: 1000 # number of warmup steps for the learning rate scheduler
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lr_max_day: 0.005 # maximum learning rate for the day specific input layers
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lr_min_day: 0.0001 # minimum learning rate for the day specific input layers
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lr_decay_steps_day: 120000 # number of steps for the learning rate decay for the day specific input layers
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lr_warmup_steps_day: 1000 # number of warmup steps for the learning rate scheduler for the day specific input layers
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beta0: 0.9 # beta0 parameter for the Adam optimizer
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beta1: 0.999 # beta1 parameter for the Adam optimizer
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epsilon: 0.1 # epsilon parameter for the Adam optimizer
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weight_decay: 0.001 # weight decay for the main model
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weight_decay_day: 0 # weight decay for the day specific input layers
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seed: 10 # random seed for reproducibility
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grad_norm_clip_value: 10 # gradient norm clipping value
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batches_per_train_log: 200 # number of batches per training log
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batches_per_val_step: 2000 # number of batches per validation step
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batches_per_save: 0 # number of batches per save
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log_individual_day_val_PER: true # whether to log individual day validation performance
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log_val_skip_logs: false # whether to skip logging validation metrics
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save_val_logits: true # whether to save validation logits
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save_val_data: false # whether to save validation data
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dataset:
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data_transforms:
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white_noise_std: 1.0
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constant_offset_std: 0.2
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random_walk_std: 0.0
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random_walk_axis: -1
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static_gain_std: 0.0
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random_cut: 3 #0
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smooth_kernel_size: 100
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smooth_data: true
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smooth_kernel_std: 2
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neural_dim: 512
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batch_size: 64
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n_classes: 41
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max_seq_elements: 500
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days_per_batch: 4
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seed: 1
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num_dataloader_workers: 4
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loader_shuffle: false
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must_include_days: null
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test_percentage: 0.1
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feature_subset: null
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dataset_dir: /media/lm-pc/8tb_nvme/b2txt25/hdf5_data
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bad_trials_dict: null
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sessions:
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white_noise_std: 1.0 # standard deviation of the white noise added to the data
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constant_offset_std: 0.2 # standard deviation of the constant offset added to the data
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random_walk_std: 0.0 # standard deviation of the random walk added to the data
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random_walk_axis: -1 # axis along which the random walk is applied
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static_gain_std: 0.0 # standard deviation of the static gain applied to the data
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random_cut: 3 # number of time steps to randomly cut from the beginning of each batch of trials
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smooth_kernel_size: 100 # size of the smoothing kernel applied to the data
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smooth_data: true # whether to smooth the data
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smooth_kernel_std: 2 # standard deviation of the smoothing kernel applied to the data
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neural_dim: 512 # dimensionality of the neural data
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batch_size: 64 # batch size for training
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n_classes: 41 # number of classes (phonemes) in the dataset
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max_seq_elements: 500 # maximum number of sequence elements (phonemes) for any trial
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days_per_batch: 4 # number of randomly-selected days to include in each batch
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seed: 1 # random seed for reproducibility
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num_dataloader_workers: 4 # number of workers for the data loader
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loader_shuffle: false # whether to shuffle the data loader
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must_include_days: null # specific days to include in the dataset
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test_percentage: 0.1 # percentage of data to use for testing
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feature_subset: null # specific features to include in the dataset
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dataset_dir: ../data/t15_copyTask_neuralData # directory containing the dataset
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bad_trials_dict: null # dictionary of bad trials to exclude from the dataset
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sessions: # list of sessions to include in the dataset
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- t15.2023.08.11
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- t15.2023.08.13
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- t15.2023.08.18
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@@ -121,7 +129,7 @@ dataset:
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- t15.2025.03.16
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- t15.2025.03.30
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- t15.2025.04.13
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dataset_probability_val:
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dataset_probability_val: # probability of including a trial in the validation set (0 or 1)
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- 0
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- 1
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- 1
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