AMSGrad¶
AMSGrad optimizer.
Parameters¶
-
lr
Type → int | float | optim.base.Scheduler
Default →
0.1The learning rate.
-
beta_1
Default →
0.9 -
beta_2
Default →
0.999 -
eps
Default →
1e-08 -
correct_bias
Default →
True
Attributes¶
-
m (collections.defaultdict)
-
v (collections.defaultdict)
-
v_hat (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.AMSGrad()
model = (
preprocessing.StandardScaler() |
linear_model.LogisticRegression(optimizer)
)
metric = metrics.F1()
evaluate.progressive_val_score(dataset, model, metric)
F1: 86.60%
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.