Source code for zfit.minimizers.optimizers_tf
# Copyright (c) 2019 zfit
import tensorflow as tf
from .base_tf import WrapOptimizer
# class AdadeltaMinimizer(AdapterTFOptimizer, tf.train.AdadeltaOptimizer, ZfitMinimizer):
# def __init__(self):
# raise NotImplementedError("Currently a placeholder, has to be implemented (with WrapOptimizer")
#
#
# class AdagradMinimizer(AdapterTFOptimizer, tf.train.AdagradOptimizer, ZfitMinimizer):
# def __init__(self):
# raise NotImplementedError("Currently a placeholder, has to be implemented (with WrapOptimizer")
#
#
# class GradientDescentMinimizer(AdapterTFOptimizer, tf.train.GradientDescentOptimizer, ZfitMinimizer):
# def __init__(self):
# raise NotImplementedError("Currently a placeholder, has to be implemented (with WrapOptimizer")
#
#
# class RMSPropMinimizer(AdapterTFOptimizer, tf.train.RMSPropOptimizer, ZfitMinimizer):
# def __init__(self):
# raise NotImplementedError("Currently a placeholder, has to be implemented (with WrapOptimizer")
[docs]class Adam(WrapOptimizer):
_DEFAULT_name = 'Adam'
def __init__(self, tolerance=None,
learning_rate=0.2,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
use_locking=False,
name='Adam', **kwargs):
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1, beta2=beta2,
epsilon=epsilon, use_locking=use_locking,
name=name)
super().__init__(optimizer=optimizer, tolerance=tolerance, **kwargs)