AdaMax

AdaMax optimizer.

Parameters

• lr

  Default → 0.1

• beta_1

  Default → 0.9

• beta_2

  Default → 0.999

• eps

  Default → 1e-08

Attributes

• m (collections.defaultdict)

• v (collections.defaultdict)

Examples

from river import datasets
from river import evaluate
from river import linear_model
from river import metrics
from river import optim
from river import preprocessing

dataset = datasets.Phishing()
optimizer = optim.AdaMax()
model = (
    preprocessing.StandardScaler() |
    linear_model.LogisticRegression(optimizer)
)
metric = metrics.F1()

evaluate.progressive_val_score(dataset, model, metric)

F1: 87.61%

Methods

look_ahead

Updates a weight vector before a prediction is made.

Parameters: w (dict): A dictionary of weight parameters. The weights are modified in-place. Returns: The updated weights.

Parameters

• w — 'dict'

step

Updates a weight vector given a gradient.

Parameters

• w — 'dict | VectorLike'
• g — 'dict | VectorLike'

Returns

dict | VectorLike: The updated weights.

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1. Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. ↩

2. Ruder, S., 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. ↩