1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
# -*- coding: utf-8 -*-
# CCP in Tomographic Imaging (CCPi) Core Imaging Library (CIL).
# Copyright 2017 UKRI-STFC
# Copyright 2017 University of Manchester
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ccpi.optimisation.algorithms import Algorithm
class GradientDescent(Algorithm):
'''Implementation of Gradient Descent algorithm
'''
def __init__(self, **kwargs):
'''initialisation can be done at creation time if all
proper variables are passed or later with set_up'''
super(GradientDescent, self).__init__()
x_init = kwargs.get('x_init', None)
objective_function = kwargs.get('objective_function', None)
rate = kwargs.get('rate', None)
if x_init is not None and objective_function is not None and rate is not None:
print(self.__class__.__name__, "set_up called from creator")
self.set_up(x_init=x_init, objective_function=objective_function, rate=rate)
def should_stop(self):
'''stopping cryterion, currently only based on number of iterations'''
return self.iteration >= self.max_iteration
def set_up(self, x_init, objective_function, rate):
'''initialisation of the algorithm'''
self.x = x_init.copy()
self.objective_function = objective_function
self.rate = rate
self.loss.append(objective_function(x_init))
self.iteration = 0
try:
self.memopt = self.objective_function.memopt
except AttributeError as ae:
self.memopt = False
if self.memopt:
self.x_update = x_init.copy()
self.configured = True
def update(self):
'''Single iteration'''
if self.memopt:
self.objective_function.gradient(self.x, out=self.x_update)
self.x_update *= -self.rate
self.x += self.x_update
else:
self.x += -self.rate * self.objective_function.gradient(self.x)
def update_objective(self):
self.loss.append(self.objective_function(self.x))
|
