params.toml

# Path to the CSV data file
data_path = 

# Keep one row every skip_frequency row
skip_frequency = 

# Maximum gap (in seconds) between two data points to be considered 
# from different sessions
max_gap_between_sessions = 

# Minimum number of data points
min_num_points_per_session =

# Minimum session duration (in seconds)
min_session_duration =

# For each session in the dataset, part of it is used as an input
# while the other is used as a label (target). The following parameters
# define where does this split happen.
# If `progressive_bound` is set to `true`, n different training datasets are 
# returned for n different breakpoints defined in `label_bounds`
# If it is set to false, a random point is chosen between `label_lower_bound`
# and `label_upper_bound`.
# The validation dataset is always defined using the latter two parameters,
# regardless of the `progressive_bound` flag.
progressive_bound = 
label_lower_bound =
label_upper_bound =
label_bounds =

# Proportion of the dataset to include in the train split
train_split =

# Arrays of column names in the dataframe. Context is features that are fixed 
# in time (OS for eg)
features =
context =

# Batch size
batch_size =
# Number of times that the training dataset is repeated
repeat =
# Debugging flag.
debug =

# Number of workers for parallel data preprocessing
n_data_workers =

# Number of elements to prefetch
prefetch =

# Number of LSTM layers to stack
lstm_layers =

# Number of features in the time embedding
t2v_k =

# If true, model outputs target value with corresponding std 
use_std =

# Number of epochs
n_epochs = 

# Initial learning rate
learning_rate = 

# Max patience (in epochs)
max_patience = 

# Logging directory
log_dir =