Numpy Example

from sklearn.datasets import load_boston
import numpy as np
import wandb
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wandb.init()
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# Save hyperparameters
wandb.config.lr = 0.000001
wandb.config.epochs = 1
​
# Load Dataset
data, target = load_boston(return_X_y=True)
​
# Initialize model
weights = np.zeros(data.shape[1])
bias = 0
​
# Train Model
for _ in range(wandb.config.epochs):
    np.random.shuffle(data)
    for i in range(data.shape[0]):
        x = data[i, :]
        y = target[i]
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        err = y - np.dot(weights, x)
        if (err < 0):
            weights -= wandb.config.lr * x 
            bias -= wandb.config.lr
        else:
            weights += wandb.config.lr * x
            bias += wandb.config.lr
​
        # Log absolute error as "loss"
        wandb.log({"Loss": np.abs(err)})
​
# Save Model
np.save("weights", weights)
wandb.save("weights.npy")