chainerのメモリ不足を解消したい.

GitHub-chainerの/examples/cifar/train_cifar.pyにhyperoptを組み込んで畳み込み層のフィルタ数を最適化しようとしています.
コードは以下のようになります.

python
1# coding:utf-8
2from __future__ import print_function
3import argparse
4import random
5from hyperopt import hp, tpe, Trials, fmin
6
7import chainer
8import chainer.links as L
9from chainer import training
10from chainer.training import extensions
11from chainer import Variable
12from chainer import serializers
13
14from chainer.datasets import get_cifar10
15from chainer.datasets import get_cifar100
16import json
17import models.VGG
18import optuna
19import numpy as np
20
21
22###目的関数
23def objective(params):
24    parser = argparse.ArgumentParser(description='Chainer CIFAR example:')
25    parser.add_argument('--dataset', '-d', default='cifar10',
26                        help='The dataset to use: cifar10 or cifar100')
27    parser.add_argument('--batchsize', '-b', type=int, default=32,
28                       help='Number of images in each mini-batch')
29    parser.add_argument('--learnrate', '-l', type=float, default=0.05,
30                        help='Learning rate for SGD')
31    parser.add_argument('--epoch', '-e', type=int, default=300,
32                        help='Number of sweeps over the dataset to train')
33    parser.add_argument('--gpu', '-g', type=int, default=0,
34                        help='GPU ID (negative value indicates CPU)')
35    parser.add_argument('--out', '-o', default='result',
36                        help='Directory to output the result')
37    parser.add_argument('--resume', '-r', default='',
38                        help='Resume the training from snapshot')
39    parser.add_argument('--line', '-line', default='',
40                        help='line')
41    args = parser.parse_args()
42
43    print('GPU: {}'.format(args.gpu))
44    print('# Minibatch-size: {}'.format(args.batchsize))
45    print('# epoch: {}'.format(args.epoch))
46    print('')
47
48    # Set up a neural network to train.
49    # Classifier reports softmax cross entropy loss and accuracy at every
50    # iteration, which will be used by the PrintReport extension below.
51    if args.dataset == 'cifar10':
52        print('Using CIFAR10 dataset.')
53        class_labels = 10
54        train, test = get_cifar10()
55    elif args.dataset == 'cifar100':
56        print('Using CIFAR100 dataset.')
57        class_labels = 100
58        train, test = get_cifar100()
59    else:
60        raise RuntimeError('Invalid dataset choice.')
61
62    ###最適化するフィルタ数
63    gane = [int(params['conv'+str(i+1)]) for i in range(13)]
64    print(gane)
65    model = L.Classifier(models.VGG.VGG(gane, class_labels))
66
67    if args.gpu >= 0:
68        # Make a specified GPU current
69        chainer.cuda.get_device_from_id(args.gpu).use()
70        model.to_gpu()  # Copy the model to the GPU
71
72    optimizer = chainer.optimizers.MomentumSGD(args.learnrate)
73    optimizer.setup(model)
74    optimizer.add_hook(chainer.optimizer.WeightDecay(5e-4))
75
76    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
77    test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
78                                                 repeat=False, shuffle=False)
79    # Set up a trainer
80    updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
81    trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
82
83    # Evaluate the model with the test dataset for each epoch
84    trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))
85
86    # Reduce the learning rate by half every 25 epochs.
87    trainer.extend(extensions.ExponentialShift('lr', 0.5),
88                   trigger=(25, 'epoch'))
89
90    # Dump a computational graph from 'loss' variable at the first iteration
91    # The "main" refers to the target link of the "main" optimizer.
92    trainer.extend(extensions.dump_graph('main/loss'))
93
94    # Take a snapshot at each epoch
95    trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))
96
97    # Write a log of evaluation statistics for each epoch
98    trainer.extend(extensions.LogReport())
99
100    # Print selected entries of the log to stdout
101    # Here "main" refers to the target link of the "main" optimizer again, and
102    # "validation" refers to the default name of the Evaluator extension.
103    # Entries other than 'epoch' are reported by the Classifier link, called by
104    # either the updater or the evaluator.
105
106    # Print a progress bar to stdout
107    trainer.extend(extensions.ProgressBar())
108
109    if args.resume:
110        # Resume from a snapshot
111        chainer.serializers.load_npz(args.resume, trainer)
112
113    # Run the training
114    trainer.run()
115
116    # 最終epochの正答率を格納
117    log_file = open("result/log", "r")
118    lines = log_file.readlines()
119    log_file.close()
120    train_acc = '"validation/main/accuracy": '
121    count = 0
122    for line in lines:
123        if train_acc in line:
124            count = count + 1
125            first = line.find('0.')
126            end = line.find(' ')
127            slice = line[first:]
128            if count == args.epoch:
129                # print(slice)
130                # print(type(slice))
131                fitness = float(slice)
132
133    err = 1.0 - fitness # 誤識別率を返す
134
135    return err
136
137if __name__ == '__main__':
138
139    space = {'conv1': hp.choice('conv1', [32, 64, 128, 256, 512]),
140             'conv2': hp.choice('conv2', [32, 64, 128, 256, 512]),
141             'conv3': hp.choice('conv3', [32, 64, 128, 256, 512]),
142             'conv4': hp.choice('conv4', [32, 64, 128, 256, 512]),
143             'conv5': hp.choice('conv5', [32, 64, 128, 256, 512]),
144             'conv6': hp.choice('conv6', [32, 64, 128, 256, 512]),
145             'conv7': hp.choice('conv7', [32, 64, 128, 256, 512]),
146             'conv8': hp.choice('conv8', [32, 64, 128, 256, 512]),
147             'conv9': hp.choice('conv9', [32, 64, 128, 256, 512]),
148             'conv10': hp.choice('conv10', [32, 64, 128, 256, 512]),
149             'conv11': hp.choice('conv11', [32, 64, 128, 256, 512]),
150             'conv12': hp.choice('conv12', [32, 64, 128, 256, 512]),
151             'conv13': hp.choice('conv13', [32, 64, 128, 256, 512])}
152
153    best = fmin(objective, space, algo=tpe.suggest, max_evals=500)
154    print("best parameters", best)