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run_train.py
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run_train.py
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import argparse
import os
from bioflax.train import train
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser = argparse.ArgumentParser(
description="Parse command-line arguments for model training.")
parser.add_argument(
"--jax_seed", type=int, default=0, help="Seed for JAX RNG. Type: int, Default: 0"
)
# Data
parser.add_argument(
"--dataset",
type=str,
default="mnist",
choices=["mnist", "sinreg", "teacher"],
help="Dataset for training. Choices: ['mnist', 'sinprop', 'teacher'], Type: str, Default: 'mnist'",
)
parser.add_argument(
"--batch_size",
type=int,
default=32,
help="Batch size during training. Type: int, Default: 32",
)
parser.add_argument(
"--train_set_size",
type=int,
default=50,
help="Size of the training set. Type: int, Default: 50",
)
parser.add_argument(
"--test_set_size", type=int, default=10, help="Size of the test set. Type: int, Default: 10"
)
parser.add_argument(
"--val_split",
type=float,
default=0.1,
help="Percentage of training samples for validation. Type: float, Default: 0.1",
)
parser.add_argument(
"--in_dim", type=int, default=1, help="Input vector dimension. Type: int, Default: 1"
)
parser.add_argument(
"--seq_len", type=int, default=1, help="Length of input sequence. Type: int, Default: 1"
)
parser.add_argument(
"--output_features",
type=int,
default=10,
help="Number of output features. Type: int, Default: 10",
)
parser.add_argument(
"--teacher_act",
type=str,
default="sigmoid",
help="Activation function of teacher network. Type: str, Default: 'sigmoid'",
)
# Network
parser.add_argument(
"--mode",
type=str,
default="fa",
choices=["bp", "fa", "kp", "dfa"],
help="Training mode. Choices: ['bp', 'fa', 'kp', 'dfa']. Type: str, Default: 'fa'",
)
parser.add_argument(
"--activations",
nargs="+",
type=str,
default=["relu", "relu"],
help="Activation functions for each layer. Type: str (list), Default: ['relu', 'relu']",
)
parser.add_argument(
"--hidden_layers",
nargs="+",
type=int,
default=[500, 500],
help="Neurons in each hidden layer. Type: int (list), Default: [500, 500]",
)
# Optimizer
parser.add_argument(
"--epochs", type=int, default=5, help="Number of training epochs. Type: int, Default: 5"
)
parser.add_argument(
"--lr", type=float, default=0.02, help="Learning rate. Type: float, Default: 0.02"
)
parser.add_argument(
"--momentum", type=float, default=0, help="Momentum value. Type: float, Default: 0"
)
parser.add_argument(
"--weight_decay",
type=float,
default=0,
help="Weight decay for learning. Type: float, Default: 0.",
)
parser.add_argument(
"--optimizer",
type=str,
default="sgd",
choices=["sgd", "adam"],
help="Optimizer. Choices: ['sgd', 'adam']. Type: str, Default: 'sgd'",
)
# Initializer
parser.add_argument(
"--initializer",
type=str,
choices=["lecun", "uniform", "variance_scaling"],
default="lecun",
)
parser.add_argument(
"--scale_w", type=float, default=1.0, help="Scaling factor for variance. Type: float, Default: 1.0"
)
parser.add_argument(
"--scale_b", type=float, default=1.0, help="Scaling factor for variance. Type: float, Default: 1.0"
)
# Logging
parser.add_argument(
"--wandb_project",
type=str,
default="test_project",
help="Weights & Biases project name. Type: str, Default: 'bioflax'",
)
parser.add_argument(
"--wandb_entity",
type=str,
default="bioflax",
help="Weights & Biases entity name. Type: str, Default: 'bioflax'",
)
parser.add_argument(
"--use_wandb",
type=str2bool,
default=True,
help="Enable/disable Weights & Biases for logging. Type: boolean, Default: True",
)
parser.add_argument(
"--plot",
type=str2bool,
default=False,
help="Enable/disable plotting. Type: boolean, Default: False",
)
parser.add_argument(
"--compute_alignments",
type=str2bool,
default=True,
help="Enable/disable alignment computations. Type: boolean, Default: True",
)
parser.add_argument(
"--n", type=int, default=5, help="Batches for alignment averaging. Type: int, Default: 5"
)
args = parser.parse_args()
train(args)