sweep.py

import numpy as np
import wandb

from src.network import NeuralNetwork
from src.utils import load_data

# Set up your default hyperparameters
hyperparameter_defaults = dict(
    n_layers=1,
    n_neurons=128,
    eta=0.008,
    lmbda=0.2,
    alpha=0.1,
    epochs=20,
    batch_size=40,
    # size_training=5000,
    # size_validation=500,
)

# Pass your defaults to wandb.init
wandb.init(config=hyperparameter_defaults)
# Access all hyperparameter values through wandb.config
config = wandb.config

training, validation, test = load_data()
# architecture = [784, 128, 10]
architecture = [784] + [config['n_neurons']] * config['n_layers'] + [10]

nn = NeuralNetwork(
    architecture, eta=config['eta'], lmbda=config['lmbda'],
    alpha=config['alpha']
)
nn.train(
    np.random.permutation(training)[:5000],
    np.random.permutation(validation)[:500],
    epochs=config['epochs'], batch_size=config['batch_size']
)