Logging parameters and model configuration
You can define a namespace for storing any parameters or hyperparameters. Assign the values one by one, or define a dictionary of values.
run["parameters/epoch_nr"] = 5
run["parameters/batch_size"] = 32
run["parameters/dense"] = 512
run["parameters/optimizer"] = "sgd"
run["parameters/metrics"] = ["accuracy", "mae"]
run["parameters/activation"] = "ReLU"
params = {
"epoch_nr": 5,
"batch_size": 32,
"dense": 512,
"optimizer": "sgd",
"metrics": ["accuracy", "binary_accuracy"],
"activation": "ReLU",
}
run["parameters"] = params
import argparse
argparser = argparse.ArgumentParser()
argparser.add_argument("--lr", default=0.01)
argparser.add_argument("--batch", default=32)
argparser.add_argument("--activation", default="ReLU")
args = argparser.parse_args()
run["parameters"] = args
Or, using Namespace()
:
from argparse import Namespace
args = Namespace(
lr=0.01,
batch=32,
activation="ReLU",
)
args = argparser.parse_args()
run["parameters"] = args
In each of the above, the parameters are stored in a namespace called "parameters"
. Inside that namespace, a field is created for each parameter.
You'll find your logged parameters in the All metadata section of the run.
See in Neptune 
Tip
You can also display parameters in the runs table and custom dashboards.