-
Notifications
You must be signed in to change notification settings - Fork 7
/
online.py
646 lines (577 loc) · 24.5 KB
/
online.py
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
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
#!/usr/bin/env python
# =============================================================================
# Copyright (c) 2018 Mengye Ren
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# =============================================================================
"""Online experiment.
Author: Mengye Ren (mren@cs.toronto.edu)
Usage:
./online.py [--dataset {DATASET}] \
[--num_meta_steps {NUM_META_STEPS}] \
[--steps_per_update {STEPS_PER_UPDATE}]
Flags:
--dataset: String. Name of the dataset. Options: `mnist`, `cifar-10`, default `mnist`.
--num_meta_steps: Int. Number of meta optimization steps every meta update, default 100.
--steps_per_update: Int. Number of steps every meta update, default 10.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import matplotlib
matplotlib.use('Agg')
from matplotlib import ticker
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import pickle as pkl
import six
import tensorflow as tf
from collections import namedtuple
from matplotlib import pyplot as plt
from tqdm import tqdm
from checkpoint import build_checkpoint
from checkpoint import read_checkpoint
from checkpoint import write_checkpoint
from get_dataset import get_dataset
from logger import get as get_logger
from look_ahead import look_ahead_grads
from models import get_cifar_cnn_config
from models import get_cifar_cnn_model
from models import get_mnist_mlp_config
from models import get_mnist_mlp_model
from models import mlp
from optimizers import LogOptimizer
from train import meta_step
from train import save_results
log = get_logger()
flags = tf.flags
flags.DEFINE_integer('num_meta_steps', 10, 'Number of meta optimization steps')
flags.DEFINE_integer('steps_per_update', 100,
'Number of steps per meta updates')
flags.DEFINE_string('dataset', 'mnist', 'Dataset name')
FLAGS = flags.FLAGS
# --------------------------------------------------------------------
# Constants.
# Training curves.
Results = namedtuple('Results', [
'step', 'train_xent', 'train_acc', 'test_xent', 'test_acc', 'lr',
'momentum'
])
def _get_exp_logger(sess, log_folder):
"""Gets a TensorBoard logger."""
if not os.path.exists(log_folder):
os.makedirs(log_folder)
with tf.name_scope('Summary'):
writer = tf.summary.FileWriter(log_folder, sess.graph)
summaries = dict()
class ExperimentLogger():
def log(self, niter, name, value):
summary = tf.Summary()
summary.value.add(tag=name, simple_value=value)
writer.add_summary(summary, niter)
def flush(self):
"""Flushes results to disk."""
def close(self):
"""Closes writer."""
writer.close()
return ExperimentLogger()
def online_smd(dataset_name='mnist',
init_lr=1e-1,
momentum=0.9,
num_steps=20000,
middle_decay=False,
steps_per_update=10,
smd=True,
steps_look_ahead=5,
num_meta_steps=10,
steps_per_eval=100,
batch_size=100,
meta_lr=1e-2,
print_step=False,
effective_lr=True,
negative_momentum=True,
optimizer='momentum',
stochastic=True,
exp_folder='.'):
"""Train an MLP for MNIST.
Args:
dataset_name: String. Name of the dataset.
init_lr: Float. Initial learning rate, default 0.1.
momentum: Float. Initial momentum, default 0.9.
num_steps: Int. Total number of steps, default 20000.
middle_decay: Whether applying manual learning rate decay to 1e-4 from the middle, default False.
steps_per_update: Int. Number of steps per update, default 10.
smd: Bool. Whether run SMD.
steps_look_ahead: Int. Number of steps to look ahead, default 5.
num_meta_steps: Int. Number of meta steps, default 10.
steps_per_eval: Int. Number of training steps per evaluation, default 100.
batch_size: Int. Mini-batch size, default 100.
meta_lr: Float. Meta learning rate, default 1e-2.
print_step: Bool. Whether to print loss during training, default True.
effective_lr: Bool. Whether to re-parameterize learning rate as lr / (1 - momentum), default True.
negative_momentum: Bool. Whether to re-parameterize momentum as (1 - momentum), default True.
optimizer: String. Name of the optimizer. Options: `momentum`, `adam, default `momentum`.
stochastic: Bool. Whether to do stochastic or deterministic look ahead, default True.
Returns:
results: Results tuple object.
"""
dataset = get_dataset(dataset_name)
dataset_train = get_dataset(
dataset_name) # For evaluate training progress (full epoch).
dataset_test = get_dataset(
dataset_name, test=True) # For evaluate test progress (full epoch).
if dataset_name == 'mnist':
input_shape = [None, 28, 28, 1]
elif dataset_name.startswith('cifar'):
input_shape = [None, 32, 32, 3]
x = tf.placeholder(tf.float32, input_shape, name="x")
y = tf.placeholder(tf.int64, [None], name="y")
if effective_lr:
init_lr_ = init_lr / (1.0 - momentum)
else:
init_lr_ = init_lr
if negative_momentum:
init_mom_ = 1.0 - momentum
else:
init_mom_ = momentum
if dataset_name == 'mnist':
config = get_mnist_mlp_config(
init_lr_,
init_mom_,
effective_lr=effective_lr,
negative_momentum=negative_momentum)
elif dataset_name == 'cifar-10':
config = get_cifar_cnn_config(
init_lr_,
init_mom_,
effective_lr=effective_lr,
negative_momentum=negative_momentum)
else:
raise NotImplemented
with tf.name_scope('Train'):
with tf.variable_scope('Model'):
if dataset_name == 'mnist':
m = get_mnist_mlp_model(
config, x, y, optimizer=optimizer, training=True)
model = m
elif dataset_name == 'cifar-10':
m = get_cifar_cnn_model(
config, x, y, optimizer=optimizer, training=True)
model = m
with tf.name_scope('Test'):
with tf.variable_scope('Model', reuse=True):
if dataset_name == 'mnist':
mtest = get_mnist_mlp_model(config, x, y, training=False)
elif dataset_name == 'cifar-10':
mtest = get_cifar_cnn_model(config, x, y, training=False)
final_lr = 1e-4
midpoint = num_steps // 2
if dataset_name == 'mnist':
num_train = 60000
num_test = 10000
elif dataset_name.startswith('cifar'):
num_train = 50000
num_test = 10000
lr_ = init_lr_
mom_ = init_mom_
bsize = batch_size
steps_per_epoch = num_train // bsize
steps_test_per_epoch = num_test // bsize
train_xent_list = []
train_acc_list = []
test_xent_list = []
test_acc_list = []
lr_list = []
mom_list = []
step_list = []
log.info(
'Applying decay at midpoint with final learning rate = {:.3e}'.format(
final_lr))
if 'momentum' in optimizer:
mom_name = 'mom'
elif 'adam' in optimizer:
mom_name = 'beta1'
else:
raise ValueError('Unknown optimizer')
hp_dict = {'lr': init_lr, mom_name: momentum}
hp_names = hp_dict.keys()
hyperparams = dict([(hp_name, model.optimizer.hyperparams[hp_name])
for hp_name in hp_names])
grads = model.optimizer.grads
accumulators = model.optimizer.accumulators
new_accumulators = model.optimizer.new_accumulators
loss = model.cost
# Build look ahead graph.
look_ahead_ops, hp_grad_ops, zero_out_ops = look_ahead_grads(
hyperparams, grads, accumulators, new_accumulators, loss)
# Meta optimizer, use Adam on the log space.
meta_opt = LogOptimizer(tf.train.AdamOptimizer(meta_lr))
hp = [model.optimizer.hyperparams[hp_name] for hp_name in hp_names]
hp_grads_dict = {
'lr': tf.placeholder(tf.float32, [], name='lr_grad'),
mom_name: tf.placeholder(
tf.float32, [], name='{}_grad'.format(mom_name))
}
hp_grads_plh = [hp_grads_dict[hp_name] for hp_name in hp_names]
hp_grads_and_vars = list(zip(hp_grads_plh, hp))
cgrad = {'lr': (-1e1, 1e1), mom_name: (-1e1, 1e1)}
cval = {'lr': (1e-4, 1e1), mom_name: (1e-4, 1e0)}
cgrad_ = [cgrad[hp_name] for hp_name in hp_names]
cval_ = [cval[hp_name] for hp_name in hp_names]
meta_train_op = meta_opt.apply_gradients(
hp_grads_and_vars, clip_gradients=cgrad_, clip_values=cval_)
var_list = tf.global_variables()
ckpt = build_checkpoint(tf.global_variables())
write_op = write_checkpoint(ckpt, var_list)
read_op = read_checkpoint(ckpt, var_list)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
exp_logger = _get_exp_logger(sess, exp_folder)
def log_hp(hp_dict):
lr_ = hp_dict['lr']
mom_ = hp_dict['mom']
# Log current learning rate and momentum.
if negative_momentum:
exp_logger.log(ii, 'mom', 1.0 - mom_)
exp_logger.log(ii, 'log neg mom', np.log10(mom_))
mom__ = 1.0 - mom_
else:
exp_logger.log(ii, 'mom', mom_)
exp_logger.log(ii, 'log neg mom', np.log10(1.0 - mom_))
mom__ = mom_
if effective_lr:
lr__ = lr_ * (1.0 - mom__)
eflr_ = lr_
else:
lr__ = lr_
eflr_ = lr_ / (1.0 - mom__)
exp_logger.log(ii, 'eff lr', eflr_)
exp_logger.log(ii, 'log eff lr', np.log10(eflr_))
exp_logger.log(ii, 'lr', lr__)
exp_logger.log(ii, 'log lr', np.log10(lr__))
exp_logger.flush()
return lr__, mom__
# Assign initial learning rate and momentum.
m.optimizer.assign_hyperparam(sess, 'lr', lr_)
m.optimizer.assign_hyperparam(sess, 'mom', mom_)
train_iter = six.moves.xrange(num_steps)
if not print_step:
train_iter = tqdm(train_iter, ncols=0)
for ii in train_iter:
# Meta-optimization loop.
if ii == 0 or ii % steps_per_update == 0:
if ii < midpoint and smd:
if stochastic:
data_list = [
dataset.next_batch(bsize)
for step in six.moves.xrange(steps_look_ahead)
]
# Take next few batches for last step evaluation.
eval_data_list = [
dataset.next_batch(bsize)
for step in six.moves.xrange(steps_look_ahead)
]
else:
data_entry = dataset.next_batch(bsize)
data_list = [data_entry] * steps_look_ahead
# Use the deterministic batch for last step evaluation.
eval_data_list = [data_list[0]]
sess.run(write_op)
for ms in six.moves.xrange(num_meta_steps):
cost, hp_dict = meta_step(sess, model, data_list,
look_ahead_ops, hp_grad_ops,
hp_grads_plh, meta_train_op,
eval_data_list)
sess.run(read_op)
for hpname, hpval in hp_dict.items():
model.optimizer.assign_hyperparam(
sess, hpname, hpval)
lr_ = hp_dict['lr']
mom_ = hp_dict['mom']
else:
hp_dict = sess.run(model.optimizer.hyperparams)
lr_log, mom_log = log_hp(hp_dict)
lr_list.append(lr_log)
mom_list.append(mom_log)
if ii == midpoint:
lr_before_mid = hp_dict['lr']
tau = (num_steps - midpoint) / np.log(lr_before_mid / final_lr)
if ii > midpoint:
lr_ = np.exp(-(ii - midpoint) / tau) * lr_before_mid
m.optimizer.assign_hyperparam(sess, 'lr', lr_)
# Run regular training.
if lr_ > 1e-6:
xd, yd = dataset.next_batch(bsize)
cost_, _ = sess.run(
[m.cost, m.train_op], feed_dict={
m.x: xd,
m.y: yd
})
# Evaluate every certain number of steps.
if ii == 0 or (ii + 1) % steps_per_eval == 0:
test_acc = 0.0
test_xent = 0.0
train_acc = 0.0
train_xent = 0.0
# Report full epoch training loss.
for jj in six.moves.xrange(steps_per_epoch):
xd, yd = dataset_train.next_batch(bsize)
xent_, acc_ = sess.run(
[m.cost, m.acc], feed_dict={
x: xd,
y: yd
})
train_xent += xent_ / float(steps_per_epoch)
train_acc += acc_ / float(steps_per_epoch)
step_list.append(ii + 1)
train_xent_list.append(train_xent)
train_acc_list.append(train_acc)
dataset_train.reset()
# Report full epoch validation loss.
for jj in six.moves.xrange(steps_test_per_epoch):
xd, yd = dataset_test.next_batch(bsize)
xent_, acc_ = sess.run(
[mtest.cost, mtest.acc], feed_dict={
x: xd,
y: yd
})
test_xent += xent_ / float(steps_test_per_epoch)
test_acc += acc_ / float(steps_test_per_epoch)
test_xent_list.append(test_xent)
test_acc_list.append(test_acc)
dataset_test.reset()
# Log training progress.
exp_logger.log(ii, 'train loss', train_xent)
exp_logger.log(ii, 'log train loss', np.log10(train_xent))
exp_logger.log(ii, 'test loss', test_xent)
exp_logger.log(ii, 'log test loss', np.log10(test_xent))
exp_logger.log(ii, 'train acc', train_acc)
exp_logger.log(ii, 'test acc', test_acc)
exp_logger.flush()
if print_step:
log.info((
'Steps {:d} T Xent {:.3e} T Acc {:.3f} V Xent {:.3e} V Acc {:.3f} '
'LR {:.3e}').format(ii + 1, train_xent,
train_acc * 100.0, test_xent,
test_acc * 100.0, lr_))
return Results(
step=np.array(step_list),
train_xent=np.array(train_xent_list),
train_acc=np.array(train_acc_list),
test_xent=np.array(test_xent_list),
test_acc=np.array(test_acc_list),
lr=np.array(lr_list),
momentum=np.array(mom_list))
def plot_report_figure_combined(steps,
values1,
values2,
condition,
title,
ylabel1,
ylabel2,
filename,
subsample=1,
figsize=(8, 7),
include_legend=True,
top_left=None,
ylim1=None,
ylim2=None):
fig, axes = plt.subplots(2, 1, figsize=figsize)
ax = axes.flatten()
ax1 = ax[0]
ax2 = ax[1]
num_steps = values1.shape[0]
num_exp = values1.shape[1]
values1 = values1.reshape([-1, subsample, num_exp])
values1 = np.mean(values1, axis=1)
values1 = np.expand_dims(values1, 0)
def empty_fmt(x, pos):
return ''
empty_formatter = ticker.FuncFormatter(empty_fmt)
lns = []
color_list = ['red'] + ['blue'] * (num_exp - 1)
for ii in range(num_exp):
values_ = values1[0, :, ii]
color_ = color_list[ii]
lns.append(ax1.plot(steps, values_, color=color_, linewidth=2)[0])
ax1.grid(color='k', linestyle=':', linewidth=1)
ax1.set_yscale('log')
if ylim1 is not None:
ax1.set_ylim(*ylim1)
ax1.tick_params(labelsize=18)
ax1.xaxis.set_major_formatter(empty_formatter)
ax1.legend(labels=condition, handles=lns[:2], loc=3)
plt.setp(ax1.get_legend().get_texts(), fontsize=18)
ax1.set_title(title, fontsize=30)
ax1.set_ylabel(ylabel1, fontsize=24)
ax2.xaxis.major.formatter._useMathText = True
ax2.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax2.set_yscale('log')
ax2.tick_params(labelsize=18)
values2 = values2.reshape([-1, subsample, num_exp])
values2 = values2[:, 0, :]
for ii in range(num_exp):
values_ = values2[:, ii]
color_ = color_list[ii]
ax2.plot(steps, values_, color=color_, linewidth=2)
ax2.grid(color='k', linestyle=':', linewidth=1)
ax2.set_xlabel("Steps", fontsize=24)
ax2.set_ylabel(ylabel2, fontsize=24)
plt.tight_layout(pad=2.0)
plt.savefig(filename)
def open_folder(folder, stochastic=True):
"""Reads experiment results from a bunch of pklz files.
Args:
folder: String. Path to the experiment folder.
Returns:
configs: A list of experimental configurations.
results: A list of experimental results.
"""
if not os.path.exists(folder):
raise ValueError("{} not found".format(folder))
print(folder)
files = list(
sorted(filter(lambda x: x.startswith('0'), os.listdir(folder))))
print(files)
if stochastic:
files = list(filter(lambda x: 'stoc' in x or 'manual' in x, files))
else:
files = list(filter(lambda x: 'det' in x or 'manual' in x, files))
files = list(map(lambda x: os.path.join(x, 'result.npy'), files))
results_list = []
print(files)
for fname in files:
results_list.append(
np.load(os.path.join(folder, fname), encoding='latin1'))
return results_list
def plot_folder(folder, title, stochastic=True):
"""Plots an experiment folder.
Args:
folder: String. Path to the folder.
show: Bool. Whether to show the plots in a window.
"""
results = open_folder(folder, stochastic=stochastic)
assert len(results) > 0, 'Cannot find any results'
ce_values = np.concatenate(
[np.expand_dims(r.item()['train_xent'][:-1], 1) for r in results],
axis=1)
steps = np.concatenate(
[np.expand_dims(r.item()['step'][:-1], 1) for r in results], axis=1)
alpha_eff_values = np.concatenate(
[
np.expand_dims(r.item()['lr'] / (1.0 - r.item()['momentum']), 1)
for r in results
],
axis=1)
condition = ['Manual', 'SMD']
plot_report_figure_combined(
steps,
ce_values,
alpha_eff_values,
condition,
title,
'Loss',
'Eff. Learning Rate',
os.path.join(
folder, 'combined_{}.pdf'.format('stoc' if stochastic else 'det')),
include_legend=True)
def run_dataset(exp_folder, dataset_name, best_lr, lr_list, optimizer,
num_steps, num_meta_steps, steps_per_update):
"""Run online experiments for a dataset.
Args:
dataset_name: String. Name of the dataset.
best_lr: Float. Best LR for the dataset.
lr_list: List of initial learning rate to try from.
num_steps: Int. Number of total training step.
num_meta_steps: Int. Number of meta optimization steps per update.
steps_per_update: Int. Number of regular training steps per update.
"""
with tf.Graph().as_default():
result_folder = os.path.join(exp_folder, '000_manual_best')
savepath = os.path.join(result_folder, 'result.npy')
if os.path.exists(savepath):
log.info('{} exists, skip'.format(savepath))
else:
save_results(savepath,
online_smd(
dataset_name=dataset_name,
init_lr=best_lr,
steps_per_update=steps_per_update,
smd=False,
optimizer=optimizer,
num_steps=num_steps,
exp_folder=result_folder))
id_len = 1
for jj, init_lr in enumerate(lr_list):
with tf.Graph().as_default():
result_folder = os.path.join(exp_folder,
'{:03d}_stoc_lr_{:.0e}'.format(
jj + id_len, init_lr))
savepath = os.path.join(result_folder, 'result.npy')
if os.path.exists(savepath):
log.info('{} exists, skip'.format(savepath))
else:
save_results(savepath,
online_smd(
dataset_name=dataset_name,
init_lr=init_lr,
num_meta_steps=num_meta_steps,
steps_per_update=steps_per_update,
stochastic=True,
optimizer=optimizer,
num_steps=num_steps,
exp_folder=result_folder))
id_len = len(lr_list) + 1
for jj, init_lr in enumerate(lr_list):
with tf.Graph().as_default():
result_folder = os.path.join(exp_folder,
'{:03d}_det_lr_{:.0e}'.format(
jj + id_len, init_lr))
savepath = os.path.join(result_folder, 'result.npy')
if os.path.exists(savepath):
log.info('{} exists, skip'.format(savepath))
else:
save_results(savepath,
online_smd(
dataset_name=dataset_name,
init_lr=init_lr,
num_meta_steps=num_meta_steps,
steps_per_update=steps_per_update,
stochastic=False,
optimizer=optimizer,
num_steps=num_steps,
exp_folder=result_folder))
def main():
optimizer = 'momentum'
exp_folder = os.path.join('results', FLAGS.dataset, 'online', optimizer)
if not os.path.exists(exp_folder):
os.makedirs(exp_folder)
if FLAGS.dataset == 'mnist':
run_dataset(exp_folder, 'mnist', 1e-1, [1e-3, 5e-3, 1e-2, 5e-2, 1e-1],
optimizer, 50000, FLAGS.num_meta_steps,
FLAGS.steps_per_update)
elif FLAGS.dataset == 'cifar-10':
run_dataset(exp_folder, 'cifar-10', 5e-3, [5e-4, 1e-3, 5e-3, 1e-2],
optimizer, 50000, FLAGS.num_meta_steps,
FLAGS.steps_per_update)
else:
raise ValueError('Dataset not supported.')
plot_folder(exp_folder, FLAGS.dataset.upper(), True)
plot_folder(exp_folder, FLAGS.dataset.upper(), False)
if __name__ == '__main__':
main()