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RMSProp

RMSProp optimizer.

Parameters

  • lr (mutable)

    Type → int | float | optim.base.Scheduler

    Default → 0.1

  • rho

    Type → float

    Default → 0.9

  • eps

    Type → float

    Default → 1e-08

Attributes

  • learning_rate

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.RMSProp()
model = (
    preprocessing.StandardScaler() |
    linear_model.LogisticRegression(optimizer)
)
metric = metrics.F1()

evaluate.progressive_val_score(dataset, model, metric)

F1: 87.24%

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 — DictLike | VectorLike

step

Updates a weight vector given a gradient.

Parameters

  • w — DictLike | VectorLike
  • g — DictLike | VectorLike

Returns

DictLike | VectorLike: The updated weights.

References