Repository navigation
Expand file tree
/
Copy pathdual_net.py
More file actions
626 lines (510 loc) · 23.9 KB
/
Copy pathdual_net.py
File metadata and controls
626 lines (510 loc) · 23.9 KB
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
# Copyright 2018 Google LLC
#
# 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.
"""
The policy and value networks share a majority of their architecture.
This helps the intermediate layers extract concepts that are relevant to both
move prediction and score estimation.
"""
from absl import flags
import argparse
import functools
import os.path
import sys
import argh
from tqdm import tqdm
import numpy as np
import tensorflow as tf
from tensorflow.contrib import summary
from tensorflow.python.training.summary_io import SummaryWriterCache
from tensorflow.contrib.tpu.python.tpu import tpu_config
from tensorflow.contrib.tpu.python.tpu import tpu_estimator
from tensorflow.contrib.tpu.python.tpu import tpu_optimizer
import features as features_lib
import go
import preprocessing
import symmetries
flags.DEFINE_integer('train_batch_size', 256,
'Batch size to use for train/eval evaluation. For GPU '
'this is batch size as expected. If \"use_tpu\" is set,'
'final batch size will be = train_batch_size * num_tpu_cores')
flags.DEFINE_integer('conv_width', 256 if go.N == 19 else 32,
'The width of each conv layer in
8000
the shared trunk.')
flags.DEFINE_integer('policy_conv_width', 2,
'The width of the policy conv layer.')
flags.DEFINE_integer('value_conv_width', 1,
'The width of the value conv layer.')
flags.DEFINE_integer('fc_width', 256 if go.N == 19 else 64,
'The width of the fully connected layer in value head.')
flags.DEFINE_integer('trunk_layers', go.N,
'The number of resnet layers in the shared trunk.')
flags.DEFINE_multi_integer('lr_boundaries', [400000, 600000],
'The number of steps at which the learning rate will decay')
flags.DEFINE_multi_float('lr_rates', [0.01, 0.001, 0.0001],
'The different learning rates')
flags.DEFINE_float('l2_strength', 1e-4,
'The L2 regularization parameter applied to weights.')
flags.DEFINE_float('value_cost_weight', 1.0,
'Scalar for value_cost, AGZ paper suggests 1/100 for '
'supervised learning')
flags.DEFINE_float('sgd_momentum', 0.9,
'Momentum parameter for learning rate.')
flags.DEFINE_string('model_dir', None,
'The working directory of the model')
# See www.moderndescartes.com/essays/shuffle_viz for discussion on sizing
flags.DEFINE_integer('shuffle_buffer_size', 2000,
'Size of buffer used to shuffle train examples.')
flags.DEFINE_bool('use_tpu', False, 'Whether to use TPU for training.')
flags.DEFINE_bool('quantize', False,
'Whether create a quantized model. When loading a model for '
'inference, this must match how the model was trained.')
flags.DEFINE_integer('quant_delay', 700 * 1024,
'Number of training steps after which weights and '
'activations are quantized.')
flags.DEFINE_string(
'tpu_name', None,
'The Cloud TPU to use for training. This should be either the name used'
'when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.')
flags.register_multi_flags_validator(
['lr_boundaries', 'lr_rates'],
lambda flags: len(flags['lr_boundaries']) == len(flags['lr_rates']) - 1,
'Number of learning rates must be exactly one greater than the number of boundaries')
flags.DEFINE_integer(
'iterations_per_loop', 128,
help=('Number of steps to run on TPU before outfeeding metrics to the CPU.'
' If the number of iterations in the loop would exceed the number of'
' train steps, the loop will exit before reaching'
' --iterations_per_loop. The larger this value is, the higher the'
' utilization on the TPU.'))
flags.DEFINE_integer(
'num_tpu_cores', default=8,
help=('Number of TPU cores. For a single TPU device, this is 8 because each'
' TPU has 4 chips each with 2 cores.'))
flags.DEFINE_integer(
'summary_steps', default=256,
help='Number of steps between logging summary scalars.')
flags.DEFINE_integer(
'keep_checkpoint_max', default=5, help='Number of checkpoints to keep.')
flags.DEFINE_bool(
'use_random_symmetry', True,
help='If true random symmetries be used when doing inference.')
flags.register_multi_flags_validator(
['use_tpu', 'iterations_per_loop', 'summary_steps'],
lambda flags: (not flags['use_tpu'] or
flags['summary_steps'] % flags['iterations_per_loop'] == 0),
'If use_tpu, summary_steps must be a multiple of iterations_per_loop')
FLAGS = flags.FLAGS
# How many positions to look at per generation.
# Per AGZ, 2048 minibatch * 1k = 2M positions/generation
EXAMPLES_PER_GENERATION = 2 ** 21
class DualNetwork():
def __init__(self, save_file):
self.save_file = save_file
self.inference_input = None
self.inference_output = None
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(graph=tf.Graph(), config=config)
self.initialize_graph()
def initialize_graph(self):
with self.sess.graph.as_default():
features, labels = get_inference_input()
estimator_spec = model_fn(features, labels,
tf.estimator.ModeKeys.PREDICT)
self.inference_input = features
self.inference_output = estimator_spec.predictions
if self.save_file is not None:
self.initialize_weights(self.save_file)
else:
self.sess.run(tf.global_variables_initializer())
def initialize_weights(self, save_file):
"""Initialize the weights from the given save_file.
Assumes that the graph has been constructed, and the
save_file contains weights that match the graph. Used
to set the weights to a different version of the player
without redifining the entire graph."""
tf.train.Saver().restore(self.sess, save_file)
def run(self, position):
probs, values = self.run_many([position])
return probs[0], values[0]
def run_many(self, positions):
processed = list(map(features_lib.extract_features, positions))
if FLAGS.use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
outputs = self.sess.run(self.inference_output,
feed_dict={self.inference_input: processed})
probabilities, value = outputs['policy_output'], outputs['value_output']
if FLAGS.use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value
def get_inference_input():
"""Set up placeholders for input features/labels.
Returns the feature, output tensors that get passed into model_fn."""
return (tf.placeholder(tf.float32,
[None, go.N, go.N, features_lib.NEW_FEATURES_PLANES],
name='pos_tensor'),
{'pi_tensor': tf.placeholder(tf.float32, [None, go.N * go.N + 1]),
'value_tensor': tf.placeholder(tf.float32, [None])})
def model_fn(features, labels, mode, params=None):
'''
Args:
features: tensor with shape
[BATCH_SIZE, go.N, go.N, features_lib.NEW_FEATURES_PLANES]
labels: dict from string to tensor with shape
'pi_tensor': [BATCH_SIZE, go.N * go.N + 1]
'value_tensor': [BATCH_SIZE]
mode: a tf.estimator.ModeKeys (batchnorm params update for TRAIN only)
params: (Ignored; needed for compat with TPUEstimator)
Returns: tf.estimator.EstimatorSpec with props
mode: same as mode arg
predictions: dict of tensors
'policy': [BATCH_SIZE, go.N * go.N + 1]
'value': [BATCH_SIZE]
loss: a single value tensor
train_op: train op
eval_metric_ops
return dict of tensors
logits: [BATCH_SIZE, go.N * go.N + 1]
'''
policy_output, value_output, logits = model_inference_fn(
features, mode == tf.estimator.ModeKeys.TRAIN)
# train ops
policy_cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
logits=logits, labels=tf.stop_gradient(labels['pi_tensor'])))
value_cost = FLAGS.value_cost_weight * tf.reduce_mean(
tf.square(value_output - labels['value_tensor']))
reg_vars = [v for v in tf.trainable_variables()
if 'bias' not in v.name and 'beta' not in v.name]
l2_cost = FLAGS.l2_strength * \
tf.add_n([tf.nn.l2_loss(v) for v in reg_vars])
combined_cost = policy_cost + value_cost + l2_cost
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.train.piecewise_constant(
global_step, FLAGS.lr_boundaries, FLAGS.lr_rates)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# Insert quantization ops if requested
if FLAGS.quantize:
if mode == tf.estimator.ModeKeys.TRAIN:
tf.contrib.quantize.create_training_graph(
quant_delay=FLAGS.quant_delay)
else:
tf.contrib.quantize.create_eval_graph()
optimizer = tf.train.MomentumOptimizer(learning_rate, FLAGS.sgd_momentum)
if FLAGS.use_tpu:
optimizer = tpu_optimizer.CrossShardOptimizer(optimizer)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(combined_cost, global_step=global_step)
# Computations to be executed on CPU, outside of the main TPU queues.
def eval_metrics_host_call_fn(policy_output, value_output, pi_tensor, policy_cost,
value_cost, l2_cost, combined_cost, step,
est_mode=tf.estimator.ModeKeys.TRAIN):
policy_entropy = -tf.reduce_mean(tf.reduce_sum(
policy_output * tf.log(policy_output), axis=1))
# pi_tensor is one_hot when generated from sgfs (for supervised learning)
# and soft-max when using self-play records. argmax normalizes the two.
policy_target_top_1 = tf.argmax(pi_tensor, axis=1)
policy_output_in_top1 = tf.to_float(
tf.nn.in_top_k(policy_output, policy_target_top_1, k=1))
policy_output_in_top3 = tf.to_float(
tf.nn.in_top_k(policy_output, policy_target_top_1, k=3))
policy_top_1_confidence = tf.reduce_max(policy_output, axis=1)
policy_target_top_1_confidence = tf.boolean_mask(
policy_output,
tf.one_hot(policy_target_top_1, tf.shape(policy_output)[1]))
with tf.variable_scope("metrics"):
metric_ops = {
'policy_cost': tf.metrics.mean(policy_cost),
'value_cost': tf.metrics.mean(value_cost),
'l2_cost': tf.metrics.mean(l2_cost),
'policy_entropy': tf.metrics.mean(policy_entropy),
'combined_cost': tf.metrics.mean(combined_cost),
'policy_accuracy_top_1': tf.metrics.mean(policy_output_in_top1),
'policy_accuracy_top_3': tf.metrics.mean(policy_output_in_top3),
'policy_top_1_confidence': tf.metrics.mean(policy_top_1_confidence),
'policy_target_top_1_confidence': tf.metrics.mean(
policy_target_top_1_confidence),
'value_confidence': tf.metrics.mean(tf.abs(value_output)),
}
if est_mode == tf.estimator.ModeKeys.EVAL:
return metric_ops
# Create summary ops so that they show up in SUMMARIES collection
# That way, they get logged automatically during training
summary_writer = summary.create_file_writer(FLAGS.model_dir)
with summary_writer.as_default(), \
summary.always_record_summaries():
for metric_name, metric_op in metric_ops.items():
summary.scalar(metric_name, metric_op[1])
# Reset metrics occasionally so that they are mean of recent batches.
reset_op = tf.variables_initializer(tf.local_variables("metrics"))
cond_reset_op = tf.cond(
tf.equal(tf.mod(tf.reduce_min(step),
FLAGS.summary_steps), tf.to_int64(1)),
lambda: reset_op,
lambda: tf.no_op())
return summary.all_summary_ops() + [cond_reset_op]
metric_args = [
policy_output,
value_output,
labels['pi_tensor'],
tf.reshape(policy_cost, [1]),
tf.reshape(value_cost, [1]),
tf.reshape(l2_cost, [1]),
tf.reshape(combined_cost, [1]),
tf.reshape(global_step, [1]),
]
predictions = {
'policy_output': policy_output,
'value_output': value_output,
}
eval_metrics_only_fn = functools.partial(
eval_metrics_host_call_fn, est_mode=tf.estimator.ModeKeys.EVAL)
host_call_fn = functools.partial(
eval_metrics_host_call_fn, est_mode=tf.estimator.ModeKeys.TRAIN)
tpu_estimator_spec = tpu_estimator.TPUEstimatorSpec(
mode=mode,
predictions=predictions,
loss=combined_cost,
train_op=train_op,
eval_metrics=(eval_metrics_only_fn, metric_args),
host_call=(host_call_fn, metric_args)
)
if FLAGS.use_tpu:
return tpu_estimator_spec
else:
return tpu_estimator_spec.as_estimator_spec()
def model_inference_fn(features, training):
"""Builds just the inference part of the model graph.
Args:
features: input features tensor.
training: True if the model is training.
Returns:
(policy_output, value_output, logits) tuple of tensors.
"""
my_batchn = functools.partial(
tf.layers.batch_normalization,
axis=-1,
momentum=.95,
epsilon=1e-5,
center=True,
scale=True,
fused=True,
training=training)
my_conv2d = functools.partial(
tf.layers.conv2d,
filters=FLAGS.conv_width,
kernel_size=3,
padding="same",
data_format="channels_last",
use_bias=False)
def my_res_layer(inputs):
int_layer1 = my_batchn(my_conv2d(inputs))
initial_output = tf.nn.relu(int_layer1)
int_layer2 = my_batchn(my_conv2d(initial_output))
output = tf.nn.relu(inputs + int_layer2)
return output
initial_output = tf.nn.relu(my_batchn(my_conv2d(features)))
# the shared stack
shared_output = initial_output
for _ in range(FLAGS.trunk_layers):
shared_output = my_res_layer(shared_output)
# policy head
policy_conv = my_conv2d(
shared_output, filters=FLAGS.policy_conv_width, kernel_size=1)
policy_conv = tf.nn.relu(my_batchn(policy_conv, center=False, scale=False))
logits = tf.layers.dense(
tf.reshape(policy_conv, [-1, FLAGS.policy_conv_width * go.N * go.N]),
go.N * go.N + 1)
policy_output = tf.nn.softmax(logits, name='policy_output')
# value head
value_conv = my_conv2d(
shared_output, filters=FLAGS.value_conv_width, kernel_size=1)
value_conv = tf.nn.relu(my_batchn(value_conv, center=False, scale=False))
value_fc_hidden = tf.nn.relu(tf.layers.dense(
tf.reshape(value_conv, [-1, FLAGS.value_conv_width * go.N * go.N]),
FLAGS.fc_width))
value_output = tf.nn.tanh(
tf.reshape(tf.layers.dense(value_fc_hidden, 1), [-1]),
name='value_output')
return policy_output, value_output, logits
def get_estimator(working_dir):
if FLAGS.use_tpu:
return get_tpu_estimator(working_dir)
run_config = tf.estimator.RunConfig(
save_summary_steps=FLAGS.summary_steps,
keep_checkpoint_max=FLAGS.keep_checkpoint_max)
return tf.estimator.Estimator(
model_fn,
model_dir=working_dir,
config=run_config)
def get_tpu_estimator(working_dir):
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=None, project=None)
tpu_grpc_url = tpu_cluster_resolver.get_master()
run_config = tpu_config.RunConfig(
master=tpu_grpc_url,
evaluation_master=tpu_grpc_url,
model_dir=working_dir,
save_checkpoints_steps=max(1000, FLAGS.iterations_per_loop),
save_summary_steps=FLAGS.summary_steps,
keep_checkpoint_max=FLAGS.keep_checkpoint_max,
session_config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=True),
tpu_config=tpu_config.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=tpu_config.InputPipelineConfig.PER_HOST_V2))
return tpu_estimator.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size * FLAGS.num_tpu_cores,
eval_batch_size=FLAGS.train_batch_size * FLAGS.num_tpu_cores)
def bootstrap(working_dir):
"""Initialize a tf.Estimator run with random initial weights.
Args:
working_dir: The directory where tf.estimator will drop logs,
checkpoints, and so on
"""
# a bit hacky - forge an initial checkpoint with the name that subsequent
# Estimator runs will expect to find.
#
# Estimator will do this automatically when you call train(), but calling
# train() requires data, and I didn't feel like creating training data in
# order to run the full train pipeline for 1 step.
estimator_initial_checkpoint_name = 'model.ckpt-1'
save_file = os.path.join(working_dir, estimator_initial_checkpoint_name)
sess = tf.Session(graph=tf.Graph())
with sess.graph.as_default():
features, labels = get_inference_input()
model_fn(features, labels, tf.estimator.ModeKeys.PREDICT)
sess.run(tf.global_variables_initializer())
tf.train.Saver().save(sess, save_file)
def export_model(working_dir, model_path):
"""Take the latest checkpoint and export it to model_path for selfplay.
Assumes that all relevant model files are prefixed by the same name.
(For example, foo.index, foo.meta and foo.data-00000-of-00001).
Args:
working_dir: The directory where tf.estimator keeps its checkpoints
model_path: The path (can be a gs:// path) to export model to
"""
estimator = tf.estimator.Estimator(model_fn, model_dir=working_dir)
latest_checkpoint = estimator.latest_checkpoint()
all_checkpoint_files = tf.gfile.Glob(latest_checkpoint + '*')
for filename in all_checkpoint_files:
suffix = filename.partition(latest_checkpoint)[2]
destination_path = model_path + suffix
print("Copying {} to {}".format(filename, destination_path))
tf.gfile.Copy(filename, destination_path)
def train(
*tf_records: "Records to train on",
steps: "Number of steps to train. If not set iterates over "
"tf_records and sets steps to examples / batch_size"=-1):
tf.logging.set_verbosity(tf.logging.INFO)
estimator = get_estimator(FLAGS.model_dir)
effective_batch_size = FLAGS.train_batch_size
if FLAGS.use_tpu:
effective_batch_size *= FLAGS.num_tpu_cores
if steps == -1:
def count_examples(tf_record):
opts = preprocessing.TF_RECORD_CONFIG
return sum(1 for _ in tqdm(
tf.python_io.tf_record_iterator(tf_record, opts),
desc=tf_record))
total_examples = sum(map(count_examples, tf_records))
steps = total_examples // effective_batch_size
if FLAGS.use_tpu:
def input_fn(params):
return preprocessing.get_tpu_input_tensors(
params['batch_size'],
tf_records,
random_rotation=True)
# TODO: get hooks working again with TPUestimator.
hooks = []
else:
def input_fn():
return preprocessing.get_input_tensors(
FLAGS.train_batch_size,
tf_records,
filter_amount=1.0,
shuffle_buffer_size=FLAGS.shuffle_buffer_size,
random_rotation=True)
hooks = [UpdateRatioSessionHook(FLAGS.model_dir),
EchoStepCounterHook(output_dir=FLAGS.model_dir)]
print("Training, steps = {} x{} = {} examples".format(
steps, effective_batch_size, steps * effective_batch_size))
estimator.train(input_fn, steps=steps, hooks=hooks)
def validate(tf_records, validate_name=None):
validate_name = validate_name or "selfplay"
if FLAGS.use_tpu:
def input_fn(params):
return preprocessing.get_tpu_input_tensors(
params['batch_size'],
tf_records, filter_amount=0.05)
else:
def input_fn():
return preprocessing.get_input_tensors(
FLAGS.train_batch_size, tf_records, filter_amount=0.05,
shuffle_buffer_size=20000)
estimator = get_estimator(FLAGS.model_dir)
estimator.evaluate(input_fn, steps=50, name=validate_name)
def compute_update_ratio(weight_tensors, before_weights, after_weights):
"""Compute the ratio of gradient norm to weight norm."""
deltas = [after - before for after,
before in zip(after_weights, before_weights)]
delta_norms = [np.linalg.norm(d.ravel()) for d in deltas]
weight_norms = [np.linalg.norm(w.ravel()) for w in before_weights]
ratios = [d / w for d, w in zip(delta_norms, weight_norms)]
all_summaries = [
tf.Summary.Value(tag='update_ratios/' +
tensor.name, simple_value=ratio)
for tensor, ratio in zip(weight_tensors, ratios)]
return tf.Summary(value=all_summaries)
class EchoStepCounterHook(tf.train.StepCounterHook):
def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
s_per_sec = elapsed_steps / elapsed_time
print("{}: {:.3f} steps per second".format(global_step, s_per_sec))
super()._log_and_record(elapsed_steps, elapsed_time, global_step)
class UpdateRatioSessionHook(tf.train.SessionRunHook):
def __init__(self, working_dir, every_n_steps=1000):
self.working_dir = working_dir
self.every_n_steps = every_n_steps
self.before_weights = None
def begin(self):
# These calls only works because the SessionRunHook api guarantees this
# will get called within a graph context containing our model graph.
self.summary_writer = SummaryWriterCache.get(self.working_dir)
self.weight_tensors = tf.trainable_variables()
self.global_step = tf.train.get_or_create_global_step()
def before_run(self, run_context):
global_step = run_context.session.run(self.global_step)
if global_step % self.every_n_steps == 0:
self.before_weights = run_context.session.run(self.weight_tensors)
def after_run(self, run_context, run_values):
global_step = run_context.session.run(self.global_step)
if self.before_weights is not None:
after_weights = run_context.session.run(self.weight_tensors)
weight_update_summaries = compute_update_ratio(
self.weight_tensors, self.before_weights, after_weights)
self.summary_writer.add_summary(
weight_update_summaries, global_step)
self.before_weights = None
parser = argparse.ArgumentParser()
argh.add_commands(parser, [train, export_model, validate])
if __name__ == '__main__':
# Let absl.flags parse known flags from argv, then pass the remaining flags
# into argh for dispatching.
remaining_argv = flags.FLAGS(sys.argv, known_only=True)
argh.dispatch(parser, argv=remaining_argv[1:])