/usr/lib/python3/dist-packages/keras/callbacks.py is in python3-keras 2.1.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 | from __future__ import absolute_import
from __future__ import print_function
import os
import csv
import six
import numpy as np
import time
import json
import warnings
from collections import deque
from collections import OrderedDict
from collections import Iterable
from .utils.generic_utils import Progbar
from . import backend as K
try:
import requests
except ImportError:
requests = None
class CallbackList(object):
"""Container abstracting a list of callbacks.
# Arguments
callbacks: List of `Callback` instances.
queue_length: Queue length for keeping
running statistics over callback execution time.
"""
def __init__(self, callbacks=None, queue_length=10):
callbacks = callbacks or []
self.callbacks = [c for c in callbacks]
self.queue_length = queue_length
def append(self, callback):
self.callbacks.append(callback)
def set_params(self, params):
for callback in self.callbacks:
callback.set_params(params)
def set_model(self, model):
for callback in self.callbacks:
callback.set_model(model)
def on_epoch_begin(self, epoch, logs=None):
"""Called at the start of an epoch.
# Arguments
epoch: integer, index of epoch.
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_epoch_begin(epoch, logs)
self._delta_t_batch = 0.
self._delta_ts_batch_begin = deque([], maxlen=self.queue_length)
self._delta_ts_batch_end = deque([], maxlen=self.queue_length)
def on_epoch_end(self, epoch, logs=None):
"""Called at the end of an epoch.
# Arguments
epoch: integer, index of epoch.
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_epoch_end(epoch, logs)
def on_batch_begin(self, batch, logs=None):
"""Called right before processing a batch.
# Arguments
batch: integer, index of batch within the current epoch.
logs: dictionary of logs.
"""
logs = logs or {}
t_before_callbacks = time.time()
for callback in self.callbacks:
callback.on_batch_begin(batch, logs)
self._delta_ts_batch_begin.append(time.time() - t_before_callbacks)
delta_t_median = np.median(self._delta_ts_batch_begin)
if (self._delta_t_batch > 0. and
delta_t_median > 0.95 * self._delta_t_batch and
delta_t_median > 0.1):
warnings.warn('Method on_batch_begin() is slow compared '
'to the batch update (%f). Check your callbacks.'
% delta_t_median)
self._t_enter_batch = time.time()
def on_batch_end(self, batch, logs=None):
"""Called at the end of a batch.
# Arguments
batch: integer, index of batch within the current epoch.
logs: dictionary of logs.
"""
logs = logs or {}
if not hasattr(self, '_t_enter_batch'):
self._t_enter_batch = time.time()
self._delta_t_batch = time.time() - self._t_enter_batch
t_before_callbacks = time.time()
for callback in self.callbacks:
callback.on_batch_end(batch, logs)
self._delta_ts_batch_end.append(time.time() - t_before_callbacks)
delta_t_median = np.median(self._delta_ts_batch_end)
if (self._delta_t_batch > 0. and
(delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1)):
warnings.warn('Method on_batch_end() is slow compared '
'to the batch update (%f). Check your callbacks.'
% delta_t_median)
def on_train_begin(self, logs=None):
"""Called at the beginning of training.
# Arguments
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_train_begin(logs)
def on_train_end(self, logs=None):
"""Called at the end of training.
# Arguments
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_train_end(logs)
def __iter__(self):
return iter(self.callbacks)
class Callback(object):
"""Abstract base class used to build new callbacks.
# Properties
params: dict. Training parameters
(eg. verbosity, batch size, number of epochs...).
model: instance of `keras.models.Model`.
Reference of the model being trained.
The `logs` dictionary that callback methods
take as argument will contain keys for quantities relevant to
the current batch or epoch.
Currently, the `.fit()` method of the `Sequential` model class
will include the following quantities in the `logs` that
it passes to its callbacks:
on_epoch_end: logs include `acc` and `loss`, and
optionally include `val_loss`
(if validation is enabled in `fit`), and `val_acc`
(if validation and accuracy monitoring are enabled).
on_batch_begin: logs include `size`,
the number of samples in the current batch.
on_batch_end: logs include `loss`, and optionally `acc`
(if accuracy monitoring is enabled).
"""
def __init__(self):
self.validation_data = None
def set_params(self, params):
self.params = params
def set_model(self, model):
self.model = model
def on_epoch_begin(self, epoch, logs=None):
pass
def on_epoch_end(self, epoch, logs=None):
pass
def on_batch_begin(self, batch, logs=None):
pass
def on_batch_end(self, batch, logs=None):
pass
def on_train_begin(self, logs=None):
pass
def on_train_end(self, logs=None):
pass
class BaseLogger(Callback):
"""Callback that accumulates epoch averages of metrics.
This callback is automatically applied to every Keras model.
"""
def on_epoch_begin(self, epoch, logs=None):
self.seen = 0
self.totals = {}
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
self.seen += batch_size
for k, v in logs.items():
if k in self.totals:
self.totals[k] += v * batch_size
else:
self.totals[k] = v * batch_size
def on_epoch_end(self, epoch, logs=None):
if logs is not None:
for k in self.params['metrics']:
if k in self.totals:
# Make value available to next callbacks.
logs[k] = self.totals[k] / self.seen
class TerminateOnNaN(Callback):
"""Callback that terminates training when a NaN loss is encountered.
"""
def __init__(self):
super(TerminateOnNaN, self).__init__()
def on_batch_end(self, batch, logs=None):
logs = logs or {}
loss = logs.get('loss')
if loss is not None:
if np.isnan(loss) or np.isinf(loss):
print('Batch %d: Invalid loss, terminating training' % (batch))
self.model.stop_training = True
class ProgbarLogger(Callback):
"""Callback that prints metrics to stdout.
# Arguments
count_mode: One of "steps" or "samples".
Whether the progress bar should
count samples seen or steps (batches) seen.
# Raises
ValueError: In case of invalid `count_mode`.
"""
def __init__(self, count_mode='samples'):
super(ProgbarLogger, self).__init__()
if count_mode == 'samples':
self.use_steps = False
elif count_mode == 'steps':
self.use_steps = True
else:
raise ValueError('Unknown `count_mode`: ' + str(count_mode))
def on_train_begin(self, logs=None):
self.verbose = self.params['verbose']
self.epochs = self.params['epochs']
def on_epoch_begin(self, epoch, logs=None):
if self.verbose:
print('Epoch %d/%d' % (epoch + 1, self.epochs))
if self.use_steps:
target = self.params['steps']
else:
target = self.params['samples']
self.target = target
self.progbar = Progbar(target=self.target,
verbose=self.verbose)
self.seen = 0
def on_batch_begin(self, batch, logs=None):
if self.seen < self.target:
self.log_values = []
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
if self.use_steps:
self.seen += 1
else:
self.seen += batch_size
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
# Skip progbar update for the last batch;
# will be handled by on_epoch_end.
if self.verbose and self.seen < self.target:
self.progbar.update(self.seen, self.log_values)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
if self.verbose:
self.progbar.update(self.seen, self.log_values, force=True)
class History(Callback):
"""Callback that records events into a `History` object.
This callback is automatically applied to
every Keras model. The `History` object
gets returned by the `fit` method of models.
"""
def on_train_begin(self, logs=None):
self.epoch = []
self.history = {}
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
self.epoch.append(epoch)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
class ModelCheckpoint(Callback):
"""Save the model after every epoch.
`filepath` can contain named formatting options,
which will be filled the value of `epoch` and
keys in `logs` (passed in `on_epoch_end`).
For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`,
then the model checkpoints will be saved with the epoch number and
the validation loss in the filename.
# Arguments
filepath: string, path to save the model file.
monitor: quantity to monitor.
verbose: verbosity mode, 0 or 1.
save_best_only: if `save_best_only=True`,
the latest best model according to
the quantity monitored will not be overwritten.
mode: one of {auto, min, max}.
If `save_best_only=True`, the decision
to overwrite the current save file is made
based on either the maximization or the
minimization of the monitored quantity. For `val_acc`,
this should be `max`, for `val_loss` this should
be `min`, etc. In `auto` mode, the direction is
automatically inferred from the name of the monitored quantity.
save_weights_only: if True, then only the model's weights will be
saved (`model.save_weights(filepath)`), else the full model
is saved (`model.save(filepath)`).
period: Interval (number of epochs) between checkpoints.
"""
def __init__(self, filepath, monitor='val_loss', verbose=0,
save_best_only=False, save_weights_only=False,
mode='auto', period=1):
super(ModelCheckpoint, self).__init__()
self.monitor = monitor
self.verbose = verbose
self.filepath = filepath
self.save_best_only = save_best_only
self.save_weights_only = save_weights_only
self.period = period
self.epochs_since_last_save = 0
if mode not in ['auto', 'min', 'max']:
warnings.warn('ModelCheckpoint mode %s is unknown, '
'fallback to auto mode.' % (mode),
RuntimeWarning)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
self.best = np.Inf
elif mode == 'max':
self.monitor_op = np.greater
self.best = -np.Inf
else:
if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
self.epochs_since_last_save += 1
if self.epochs_since_last_save >= self.period:
self.epochs_since_last_save = 0
filepath = self.filepath.format(epoch=epoch + 1, **logs)
if self.save_best_only:
current = logs.get(self.monitor)
if current is None:
warnings.warn('Can save best model only with %s available, '
'skipping.' % (self.monitor), RuntimeWarning)
else:
if self.monitor_op(current, self.best):
if self.verbose > 0:
print('Epoch %05d: %s improved from %0.5f to %0.5f,'
' saving model to %s'
% (epoch + 1, self.monitor, self.best,
current, filepath))
self.best = current
if self.save_weights_only:
self.model.save_weights(filepath, overwrite=True)
else:
self.model.save(filepath, overwrite=True)
else:
if self.verbose > 0:
print('Epoch %05d: %s did not improve' %
(epoch + 1, self.monitor))
else:
if self.verbose > 0:
print('Epoch %05d: saving model to %s' % (epoch + 1, filepath))
if self.save_weights_only:
self.model.save_weights(filepath, overwrite=True)
else:
self.model.save(filepath, overwrite=True)
class EarlyStopping(Callback):
"""Stop training when a monitored quantity has stopped improving.
# Arguments
monitor: quantity to be monitored.
min_delta: minimum change in the monitored quantity
to qualify as an improvement, i.e. an absolute
change of less than min_delta, will count as no
improvement.
patience: number of epochs with no improvement
after which training will be stopped.
verbose: verbosity mode.
mode: one of {auto, min, max}. In `min` mode,
training will stop when the quantity
monitored has stopped decreasing; in `max`
mode it will stop when the quantity
monitored has stopped increasing; in `auto`
mode, the direction is automatically inferred
from the name of the monitored quantity.
"""
def __init__(self, monitor='val_loss',
min_delta=0, patience=0, verbose=0, mode='auto'):
super(EarlyStopping, self).__init__()
self.monitor = monitor
self.patience = patience
self.verbose = verbose
self.min_delta = min_delta
self.wait = 0
self.stopped_epoch = 0
if mode not in ['auto', 'min', 'max']:
warnings.warn('EarlyStopping mode %s is unknown, '
'fallback to auto mode.' % mode,
RuntimeWarning)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
elif mode == 'max':
self.monitor_op = np.greater
else:
if 'acc' in self.monitor:
self.monitor_op = np.greater
else:
self.monitor_op = np.less
if self.monitor_op == np.greater:
self.min_delta *= 1
else:
self.min_delta *= -1
def on_train_begin(self, logs=None):
# Allow instances to be re-used
self.wait = 0
self.stopped_epoch = 0
self.best = np.Inf if self.monitor_op == np.less else -np.Inf
def on_epoch_end(self, epoch, logs=None):
current = logs.get(self.monitor)
if current is None:
warnings.warn(
'Early stopping conditioned on metric `%s` '
'which is not available. Available metrics are: %s' %
(self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
)
return
if self.monitor_op(current - self.min_delta, self.best):
self.best = current
self.wait = 0
else:
self.wait += 1
if self.wait >= self.patience:
self.stopped_epoch = epoch
self.model.stop_training = True
def on_train_end(self, logs=None):
if self.stopped_epoch > 0 and self.verbose > 0:
print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))
class RemoteMonitor(Callback):
"""Callback used to stream events to a server.
Requires the `requests` library.
Events are sent to `root + '/publish/epoch/end/'` by default. Calls are
HTTP POST, with a `data` argument which is a
JSON-encoded dictionary of event data.
# Arguments
root: String; root url of the target server.
path: String; path relative to `root` to which the events will be sent.
field: String; JSON field under which the data will be stored.
headers: Dictionary; optional custom HTTP headers.
"""
def __init__(self,
root='http://localhost:9000',
path='/publish/epoch/end/',
field='data',
headers=None):
super(RemoteMonitor, self).__init__()
self.root = root
self.path = path
self.field = field
self.headers = headers
def on_epoch_end(self, epoch, logs=None):
if requests is None:
raise ImportError('RemoteMonitor requires '
'the `requests` library.')
logs = logs or {}
send = {}
send['epoch'] = epoch
for k, v in logs.items():
send[k] = v
try:
requests.post(self.root + self.path,
{self.field: json.dumps(send)},
headers=self.headers)
except requests.exceptions.RequestException:
warnings.warn('Warning: could not reach RemoteMonitor '
'root server at ' + str(self.root))
class LearningRateScheduler(Callback):
"""Learning rate scheduler.
# Arguments
schedule: a function that takes an epoch index as input
(integer, indexed from 0) and returns a new
learning rate as output (float).
"""
def __init__(self, schedule):
super(LearningRateScheduler, self).__init__()
self.schedule = schedule
def on_epoch_begin(self, epoch, logs=None):
if not hasattr(self.model.optimizer, 'lr'):
raise ValueError('Optimizer must have a "lr" attribute.')
lr = self.schedule(epoch)
if not isinstance(lr, (float, np.float32, np.float64)):
raise ValueError('The output of the "schedule" function '
'should be float.')
K.set_value(self.model.optimizer.lr, lr)
class TensorBoard(Callback):
"""Tensorboard basic visualizations.
[TensorBoard](https://www.tensorflow.org/get_started/summaries_and_tensorboard)
is a visualization tool provided with TensorFlow.
This callback writes a log for TensorBoard, which allows
you to visualize dynamic graphs of your training and test
metrics, as well as activation histograms for the different
layers in your model.
If you have installed TensorFlow with pip, you should be able
to launch TensorBoard from the command line:
```sh
tensorboard --logdir=/full_path_to_your_logs
```
# Arguments
log_dir: the path of the directory where to save the log
files to be parsed by TensorBoard.
histogram_freq: frequency (in epochs) at which to compute activation
and weight histograms for the layers of the model. If set to 0,
histograms won't be computed. Validation data (or split) must be
specified for histogram visualizations.
write_graph: whether to visualize the graph in TensorBoard.
The log file can become quite large when
write_graph is set to True.
write_grads: whether to visualize gradient histograms in TensorBoard.
`histogram_freq` must be greater than 0.
batch_size: size of batch of inputs to feed to the network
for histograms computation.
write_images: whether to write model weights to visualize as
image in TensorBoard.
embeddings_freq: frequency (in epochs) at which selected embedding
layers will be saved.
embeddings_layer_names: a list of names of layers to keep eye on. If
None or empty list all the embedding layer will be watched.
embeddings_metadata: a dictionary which maps layer name to a file name
in which metadata for this embedding layer is saved. See the
[details](https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional)
about metadata files format. In case if the same metadata file is
used for all embedding layers, string can be passed.
"""
def __init__(self, log_dir='./logs',
histogram_freq=0,
batch_size=32,
write_graph=True,
write_grads=False,
write_images=False,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None):
super(TensorBoard, self).__init__()
if K.backend() != 'tensorflow':
raise RuntimeError('TensorBoard callback only works '
'with the TensorFlow backend.')
global tf, projector
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
self.log_dir = log_dir
self.histogram_freq = histogram_freq
self.merged = None
self.write_graph = write_graph
self.write_grads = write_grads
self.write_images = write_images
self.embeddings_freq = embeddings_freq
self.embeddings_layer_names = embeddings_layer_names
self.embeddings_metadata = embeddings_metadata or {}
self.batch_size = batch_size
def set_model(self, model):
self.model = model
self.sess = K.get_session()
if self.histogram_freq and self.merged is None:
for layer in self.model.layers:
for weight in layer.weights:
mapped_weight_name = weight.name.replace(':', '_')
tf.summary.histogram(mapped_weight_name, weight)
if self.write_grads:
grads = model.optimizer.get_gradients(model.total_loss,
weight)
def is_indexed_slices(grad):
return type(grad).__name__ == 'IndexedSlices'
grads = [
grad.values if is_indexed_slices(grad) else grad
for grad in grads]
tf.summary.histogram('{}_grad'.format(mapped_weight_name), grads)
if self.write_images:
w_img = tf.squeeze(weight)
shape = K.int_shape(w_img)
if len(shape) == 2: # dense layer kernel case
if shape[0] > shape[1]:
w_img = tf.transpose(w_img)
shape = K.int_shape(w_img)
w_img = tf.reshape(w_img, [1,
shape[0],
shape[1],
1])
elif len(shape) == 3: # convnet case
if K.image_data_format() == 'channels_last':
# switch to channels_first to display
# every kernel as a separate image
w_img = tf.transpose(w_img, perm=[2, 0, 1])
shape = K.int_shape(w_img)
w_img = tf.reshape(w_img, [shape[0],
shape[1],
shape[2],
1])
elif len(shape) == 1: # bias case
w_img = tf.reshape(w_img, [1,
shape[0],
1,
1])
else:
# not possible to handle 3D convnets etc.
continue
shape = K.int_shape(w_img)
assert len(shape) == 4 and shape[-1] in [1, 3, 4]
tf.summary.image(mapped_weight_name, w_img)
if hasattr(layer, 'output'):
tf.summary.histogram('{}_out'.format(layer.name),
layer.output)
self.merged = tf.summary.merge_all()
if self.write_graph:
self.writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.summary.FileWriter(self.log_dir)
if self.embeddings_freq:
embeddings_layer_names = self.embeddings_layer_names
if not embeddings_layer_names:
embeddings_layer_names = [layer.name for layer in self.model.layers
if type(layer).__name__ == 'Embedding']
embeddings = {layer.name: layer.weights[0]
for layer in self.model.layers
if layer.name in embeddings_layer_names}
self.saver = tf.train.Saver(list(embeddings.values()))
embeddings_metadata = {}
if not isinstance(self.embeddings_metadata, str):
embeddings_metadata = self.embeddings_metadata
else:
embeddings_metadata = {layer_name: self.embeddings_metadata
for layer_name in embeddings.keys()}
config = projector.ProjectorConfig()
self.embeddings_ckpt_path = os.path.join(self.log_dir,
'keras_embedding.ckpt')
for layer_name, tensor in embeddings.items():
embedding = config.embeddings.add()
embedding.tensor_name = tensor.name
if layer_name in embeddings_metadata:
embedding.metadata_path = embeddings_metadata[layer_name]
projector.visualize_embeddings(self.writer, config)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if not self.validation_data and self.histogram_freq:
raise ValueError('If printing histograms, validation_data must be '
'provided, and cannot be a generator.')
if self.validation_data and self.histogram_freq:
if epoch % self.histogram_freq == 0:
val_data = self.validation_data
tensors = (self.model.inputs +
self.model.targets +
self.model.sample_weights)
if self.model.uses_learning_phase:
tensors += [K.learning_phase()]
assert len(val_data) == len(tensors)
val_size = val_data[0].shape[0]
i = 0
while i < val_size:
step = min(self.batch_size, val_size - i)
if self.model.uses_learning_phase:
# do not slice the learning phase
batch_val = [x[i:i + step] for x in val_data[:-1]]
batch_val.append(val_data[-1])
else:
batch_val = [x[i:i + step] for x in val_data]
assert len(batch_val) == len(tensors)
feed_dict = dict(zip(tensors, batch_val))
result = self.sess.run([self.merged], feed_dict=feed_dict)
summary_str = result[0]
self.writer.add_summary(summary_str, epoch)
i += self.batch_size
if self.embeddings_freq and self.embeddings_ckpt_path:
if epoch % self.embeddings_freq == 0:
self.saver.save(self.sess,
self.embeddings_ckpt_path,
epoch)
for name, value in logs.items():
if name in ['batch', 'size']:
continue
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value.item()
summary_value.tag = name
self.writer.add_summary(summary, epoch)
self.writer.flush()
def on_train_end(self, _):
self.writer.close()
class ReduceLROnPlateau(Callback):
"""Reduce learning rate when a metric has stopped improving.
Models often benefit from reducing the learning rate by a factor
of 2-10 once learning stagnates. This callback monitors a
quantity and if no improvement is seen for a 'patience' number
of epochs, the learning rate is reduced.
# Example
```python
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=5, min_lr=0.001)
model.fit(X_train, Y_train, callbacks=[reduce_lr])
```
# Arguments
monitor: quantity to be monitored.
factor: factor by which the learning rate will
be reduced. new_lr = lr * factor
patience: number of epochs with no improvement
after which learning rate will be reduced.
verbose: int. 0: quiet, 1: update messages.
mode: one of {auto, min, max}. In `min` mode,
lr will be reduced when the quantity
monitored has stopped decreasing; in `max`
mode it will be reduced when the quantity
monitored has stopped increasing; in `auto`
mode, the direction is automatically inferred
from the name of the monitored quantity.
epsilon: threshold for measuring the new optimum,
to only focus on significant changes.
cooldown: number of epochs to wait before resuming
normal operation after lr has been reduced.
min_lr: lower bound on the learning rate.
"""
def __init__(self, monitor='val_loss', factor=0.1, patience=10,
verbose=0, mode='auto', epsilon=1e-4, cooldown=0, min_lr=0):
super(ReduceLROnPlateau, self).__init__()
self.monitor = monitor
if factor >= 1.0:
raise ValueError('ReduceLROnPlateau '
'does not support a factor >= 1.0.')
self.factor = factor
self.min_lr = min_lr
self.epsilon = epsilon
self.patience = patience
self.verbose = verbose
self.cooldown = cooldown
self.cooldown_counter = 0 # Cooldown counter.
self.wait = 0
self.best = 0
self.mode = mode
self.monitor_op = None
self._reset()
def _reset(self):
"""Resets wait counter and cooldown counter.
"""
if self.mode not in ['auto', 'min', 'max']:
warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
'fallback to auto mode.' % (self.mode),
RuntimeWarning)
self.mode = 'auto'
if (self.mode == 'min' or
(self.mode == 'auto' and 'acc' not in self.monitor)):
self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
self.best = -np.Inf
self.cooldown_counter = 0
self.wait = 0
self.lr_epsilon = self.min_lr * 1e-4
def on_train_begin(self, logs=None):
self._reset()
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
logs['lr'] = K.get_value(self.model.optimizer.lr)
current = logs.get(self.monitor)
if current is None:
warnings.warn(
'Reduce LR on plateau conditioned on metric `%s` '
'which is not available. Available metrics are: %s' %
(self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
)
else:
if self.in_cooldown():
self.cooldown_counter -= 1
self.wait = 0
if self.monitor_op(current, self.best):
self.best = current
self.wait = 0
elif not self.in_cooldown():
if self.wait >= self.patience:
old_lr = float(K.get_value(self.model.optimizer.lr))
if old_lr > self.min_lr + self.lr_epsilon:
new_lr = old_lr * self.factor
new_lr = max(new_lr, self.min_lr)
K.set_value(self.model.optimizer.lr, new_lr)
if self.verbose > 0:
print('\nEpoch %05d: reducing learning rate to %s.' % (epoch + 1, new_lr))
self.cooldown_counter = self.cooldown
self.wait = 0
self.wait += 1
def in_cooldown(self):
return self.cooldown_counter > 0
class CSVLogger(Callback):
"""Callback that streams epoch results to a csv file.
Supports all values that can be represented as a string,
including 1D iterables such as np.ndarray.
# Example
```python
csv_logger = CSVLogger('training.log')
model.fit(X_train, Y_train, callbacks=[csv_logger])
```
# Arguments
filename: filename of the csv file, e.g. 'run/log.csv'.
separator: string used to separate elements in the csv file.
append: True: append if file exists (useful for continuing
training). False: overwrite existing file,
"""
def __init__(self, filename, separator=',', append=False):
self.sep = separator
self.filename = filename
self.append = append
self.writer = None
self.keys = None
self.append_header = True
self.file_flags = 'b' if six.PY2 and os.name == 'nt' else ''
super(CSVLogger, self).__init__()
def on_train_begin(self, logs=None):
if self.append:
if os.path.exists(self.filename):
with open(self.filename, 'r' + self.file_flags) as f:
self.append_header = not bool(len(f.readline()))
self.csv_file = open(self.filename, 'a' + self.file_flags)
else:
self.csv_file = open(self.filename, 'w' + self.file_flags)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
def handle_value(k):
is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0
if isinstance(k, six.string_types):
return k
elif isinstance(k, Iterable) and not is_zero_dim_ndarray:
return '"[%s]"' % (', '.join(map(str, k)))
else:
return k
if self.model.stop_training:
# We set NA so that csv parsers do not fail for this last epoch.
logs = dict([(k, logs[k]) if k in logs else (k, 'NA') for k in self.keys])
if not self.writer:
self.keys = sorted(logs.keys())
class CustomDialect(csv.excel):
delimiter = self.sep
self.writer = csv.DictWriter(self.csv_file,
fieldnames=['epoch'] + self.keys, dialect=CustomDialect)
if self.append_header:
self.writer.writeheader()
row_dict = OrderedDict({'epoch': epoch})
row_dict.update((key, handle_value(logs[key])) for key in self.keys)
self.writer.writerow(row_dict)
self.csv_file.flush()
def on_train_end(self, logs=None):
self.csv_file.close()
self.writer = None
class LambdaCallback(Callback):
r"""Callback for creating simple, custom callbacks on-the-fly.
This callback is constructed with anonymous functions that will be called
at the appropriate time. Note that the callbacks expects positional
arguments, as:
- `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
`epoch`, `logs`
- `on_batch_begin` and `on_batch_end` expect two positional arguments:
`batch`, `logs`
- `on_train_begin` and `on_train_end` expect one positional argument:
`logs`
# Arguments
on_epoch_begin: called at the beginning of every epoch.
on_epoch_end: called at the end of every epoch.
on_batch_begin: called at the beginning of every batch.
on_batch_end: called at the end of every batch.
on_train_begin: called at the beginning of model training.
on_train_end: called at the end of model training.
# Example
```python
# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
on_batch_begin=lambda batch,logs: print(batch))
# Stream the epoch loss to a file in JSON format. The file content
# is not well-formed JSON but rather has a JSON object per line.
import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
on_epoch_end=lambda epoch, logs: json_log.write(
json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
on_train_end=lambda logs: json_log.close()
)
# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
on_train_end=lambda logs: [
p.terminate() for p in processes if p.is_alive()])
model.fit(...,
callbacks=[batch_print_callback,
json_logging_callback,
cleanup_callback])
```
"""
def __init__(self,
on_epoch_begin=None,
on_epoch_end=None,
on_batch_begin=None,
on_batch_end=None,
on_train_begin=None,
on_train_end=None,
**kwargs):
super(LambdaCallback, self).__init__()
self.__dict__.update(kwargs)
if on_epoch_begin is not None:
self.on_epoch_begin = on_epoch_begin
else:
self.on_epoch_begin = lambda epoch, logs: None
if on_epoch_end is not None:
self.on_epoch_end = on_epoch_end
else:
self.on_epoch_end = lambda epoch, logs: None
if on_batch_begin is not None:
self.on_batch_begin = on_batch_begin
else:
self.on_batch_begin = lambda batch, logs: None
if on_batch_end is not None:
self.on_batch_end = on_batch_end
else:
self.on_batch_end = lambda batch, logs: None
if on_train_begin is not None:
self.on_train_begin = on_train_begin
else:
self.on_train_begin = lambda logs: None
if on_train_end is not None:
self.on_train_end = on_train_end
else:
self.on_train_end = lambda logs: None
|