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tensorflow/tensorboard | tensorboard/backend/event_processing/io_wrapper.py | ListDirectoryAbsolute | def ListDirectoryAbsolute(directory):
"""Yields all files in the given directory. The paths are absolute."""
return (os.path.join(directory, path)
for path in tf.io.gfile.listdir(directory)) | python | def ListDirectoryAbsolute(directory):
"""Yields all files in the given directory. The paths are absolute."""
return (os.path.join(directory, path)
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tensorflow/tensorboard | tensorboard/backend/event_processing/io_wrapper.py | _EscapeGlobCharacters | def _EscapeGlobCharacters(path):
"""Escapes the glob characters in a path.
Python 3 has a glob.escape method, but python 2 lacks it, so we manually
implement this method.
Args:
path: The absolute path to escape.
Returns:
The escaped path string.
"""
drive, path = os.path.splitdrive(path)
retu... | python | def _EscapeGlobCharacters(path):
"""Escapes the glob characters in a path.
Python 3 has a glob.escape method, but python 2 lacks it, so we manually
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Args:
path: The absolute path to escape.
Returns:
The escaped path string.
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tensorflow/tensorboard | tensorboard/backend/event_processing/io_wrapper.py | ListRecursivelyViaGlobbing | def ListRecursivelyViaGlobbing(top):
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"""Recursively lists all files within the directory.
This method does not list subdirectories (in addition to regular files), and
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tensorflow/tensorboard | tensorboard/backend/event_processing/io_wrapper.py | ListRecursivelyViaWalking | def ListRecursivelyViaWalking(top):
"""Walks a directory tree, yielding (dir_path, file_paths) tuples.
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to the directory and the path to each of the contained files. Note that
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"""Walks a directory tree, yielding (dir_path, file_paths) tuples.
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tensorflow/tensorboard | tensorboard/backend/event_processing/io_wrapper.py | GetLogdirSubdirectories | def GetLogdirSubdirectories(path):
"""Obtains all subdirectories with events files.
The order of the subdirectories returned is unspecified. The internal logic
that determines order varies by scenario.
Args:
path: The path to a directory under which to find subdirectories.
Returns:
A tuple of absol... | python | def GetLogdirSubdirectories(path):
"""Obtains all subdirectories with events files.
The order of the subdirectories returned is unspecified. The internal logic
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path: The path to a directory under which to find subdirectories.
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tensorflow/tensorboard | tensorboard/plugins/audio/summary_v2.py | audio | def audio(name,
data,
sample_rate,
step=None,
max_outputs=3,
encoding=None,
description=None):
"""Write an audio summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
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data,
sample_rate,
step=None,
max_outputs=3,
encoding=None,
description=None):
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name: A name for this summary. The summary tag used for TensorBoard will
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tensorflow/tensorboard | tensorboard/plugins/debugger/numerics_alert.py | extract_numerics_alert | def extract_numerics_alert(event):
"""Determines whether a health pill event contains bad values.
A bad value is one of NaN, -Inf, or +Inf.
Args:
event: (`Event`) A `tensorflow.Event` proto from `DebugNumericSummary`
ops.
Returns:
An instance of `NumericsAlert`, if bad values are found.
`No... | python | def extract_numerics_alert(event):
"""Determines whether a health pill event contains bad values.
A bad value is one of NaN, -Inf, or +Inf.
Args:
event: (`Event`) A `tensorflow.Event` proto from `DebugNumericSummary`
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An instance of `NumericsAlert`, if bad values are found.
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tensorflow/tensorboard | tensorboard/plugins/debugger/numerics_alert.py | NumericsAlertHistory.first_timestamp | def first_timestamp(self, event_key=None):
"""Obtain the first timestamp.
Args:
event_key: the type key of the sought events (e.g., constants.NAN_KEY).
If None, includes all event type keys.
Returns:
First (earliest) timestamp of all the events of the given type (or all
event typ... | python | def first_timestamp(self, event_key=None):
"""Obtain the first timestamp.
Args:
event_key: the type key of the sought events (e.g., constants.NAN_KEY).
If None, includes all event type keys.
Returns:
First (earliest) timestamp of all the events of the given type (or all
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tensorflow/tensorboard | tensorboard/plugins/debugger/numerics_alert.py | NumericsAlertHistory.last_timestamp | def last_timestamp(self, event_key=None):
"""Obtain the last timestamp.
Args:
event_key: the type key of the sought events (e.g., constants.NAN_KEY). If
None, includes all event type keys.
Returns:
Last (latest) timestamp of all the events of the given type (or all
event types if... | python | def last_timestamp(self, event_key=None):
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Args:
event_key: the type key of the sought events (e.g., constants.NAN_KEY). If
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tensorflow/tensorboard | tensorboard/plugins/debugger/numerics_alert.py | NumericsAlertHistory.create_jsonable_history | def create_jsonable_history(self):
"""Creates a JSON-able representation of this object.
Returns:
A dictionary mapping key to EventTrackerDescription (which can be used to
create event trackers).
"""
return {value_category_key: tracker.get_description()
for (value_category_key, ... | python | def create_jsonable_history(self):
"""Creates a JSON-able representation of this object.
Returns:
A dictionary mapping key to EventTrackerDescription (which can be used to
create event trackers).
"""
return {value_category_key: tracker.get_description()
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tensorflow/tensorboard | tensorboard/plugins/debugger/numerics_alert.py | NumericsAlertRegistry.register | def register(self, numerics_alert):
"""Register an alerting numeric event.
Args:
numerics_alert: An instance of `NumericsAlert`.
"""
key = (numerics_alert.device_name, numerics_alert.tensor_name)
if key in self._data:
self._data[key].add(numerics_alert)
else:
if len(self._data... | python | def register(self, numerics_alert):
"""Register an alerting numeric event.
Args:
numerics_alert: An instance of `NumericsAlert`.
"""
key = (numerics_alert.device_name, numerics_alert.tensor_name)
if key in self._data:
self._data[key].add(numerics_alert)
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tensorflow/tensorboard | tensorboard/plugins/debugger/numerics_alert.py | NumericsAlertRegistry.report | def report(self, device_name_filter=None, tensor_name_filter=None):
"""Get a report of offending device/tensor names.
The report includes information about the device name, tensor name, first
(earliest) timestamp of the alerting events from the tensor, in addition to
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tensorflow/tensorboard | tensorboard/plugins/debugger/numerics_alert.py | NumericsAlertRegistry.create_jsonable_registry | def create_jsonable_registry(self):
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Returns:
A dictionary mapping (device, tensor name) to JSON-able object
representations of NumericsAlertHistory.
"""
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# the ... | python | def create_jsonable_registry(self):
"""Creates a JSON-able representation of this object.
Returns:
A dictionary mapping (device, tensor name) to JSON-able object
representations of NumericsAlertHistory.
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_demo.py | run | def run(logdir, run_name, wave_name, wave_constructor):
"""Generate wave data of the given form.
The provided function `wave_constructor` should accept a scalar tensor
of type float32, representing the frequency (in Hz) at which to
construct a wave, and return a tensor of shape [1, _samples(), `n`]
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"""Generate wave data of the given form.
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_demo.py | sine_wave | def sine_wave(frequency):
"""Emit a sine wave at the given frequency."""
xs = tf.reshape(tf.range(_samples(), dtype=tf.float32), [1, _samples(), 1])
ts = xs / FLAGS.sample_rate
return tf.sin(2 * math.pi * frequency * ts) | python | def sine_wave(frequency):
"""Emit a sine wave at the given frequency."""
xs = tf.reshape(tf.range(_samples(), dtype=tf.float32), [1, _samples(), 1])
ts = xs / FLAGS.sample_rate
return tf.sin(2 * math.pi * frequency * ts) | [
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_demo.py | triangle_wave | def triangle_wave(frequency):
"""Emit a triangle wave at the given frequency."""
xs = tf.reshape(tf.range(_samples(), dtype=tf.float32), [1, _samples(), 1])
ts = xs / FLAGS.sample_rate
#
# A triangle wave looks like this:
#
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# / \ / \
# \ / \ /
# \/ ... | python | def triangle_wave(frequency):
"""Emit a triangle wave at the given frequency."""
xs = tf.reshape(tf.range(_samples(), dtype=tf.float32), [1, _samples(), 1])
ts = xs / FLAGS.sample_rate
#
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_demo.py | bisine_wave | def bisine_wave(frequency):
"""Emit two sine waves, in stereo at different octaves."""
#
# We can first our existing sine generator to generate two different
# waves.
f_hi = frequency
f_lo = frequency / 2.0
with tf.name_scope('hi'):
sine_hi = sine_wave(f_hi)
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sine_lo = s... | python | def bisine_wave(frequency):
"""Emit two sine waves, in stereo at different octaves."""
#
# We can first our existing sine generator to generate two different
# waves.
f_hi = frequency
f_lo = frequency / 2.0
with tf.name_scope('hi'):
sine_hi = sine_wave(f_hi)
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_demo.py | bisine_wahwah_wave | def bisine_wahwah_wave(frequency):
"""Emit two sine waves with balance oscillating left and right."""
#
# This is clearly intended to build on the bisine wave defined above,
# so we can start by generating that.
waves_a = bisine_wave(frequency)
#
# Then, by reversing axis 2, we swap the stereo channels. B... | python | def bisine_wahwah_wave(frequency):
"""Emit two sine waves with balance oscillating left and right."""
#
# This is clearly intended to build on the bisine wave defined above,
# so we can start by generating that.
waves_a = bisine_wave(frequency)
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_demo.py | run_all | def run_all(logdir, verbose=False):
"""Generate waves of the shapes defined above.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
"""
waves = [sine_wave, square_wave, triangle_wave,
bisine_wave, bisine_wahwah_w... | python | def run_all(logdir, verbose=False):
"""Generate waves of the shapes defined above.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
"""
waves = [sine_wave, square_wave, triangle_wave,
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tensorflow/tensorboard | tensorboard/backend/process_graph.py | prepare_graph_for_ui | def prepare_graph_for_ui(graph, limit_attr_size=1024,
large_attrs_key='_too_large_attrs'):
"""Prepares (modifies in-place) the graph to be served to the front-end.
For now, it supports filtering out attributes that are
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Args:
graph: The GraphD... | python | def prepare_graph_for_ui(graph, limit_attr_size=1024,
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"""Prepares (modifies in-place) the graph to be served to the front-end.
For now, it supports filtering out attributes that are
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tensorflow/tensorboard | tensorboard/plugins/graph/graphs_plugin.py | GraphsPlugin.info_impl | def info_impl(self):
"""Returns a dict of all runs and tags and their data availabilities."""
result = {}
def add_row_item(run, tag=None):
run_item = result.setdefault(run, {
'run': run,
'tags': {},
# A run-wide GraphDef of ops.
'run_graph': False})
tag_i... | python | def info_impl(self):
"""Returns a dict of all runs and tags and their data availabilities."""
result = {}
def add_row_item(run, tag=None):
run_item = result.setdefault(run, {
'run': run,
'tags': {},
# A run-wide GraphDef of ops.
'run_graph': False})
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tensorflow/tensorboard | tensorboard/plugins/graph/graphs_plugin.py | GraphsPlugin.graph_impl | def graph_impl(self, run, tag, is_conceptual, limit_attr_size=None, large_attrs_key=None):
"""Result of the form `(body, mime_type)`, or `None` if no graph exists."""
if is_conceptual:
tensor_events = self._multiplexer.Tensors(run, tag)
# Take the first event if there are multiple events written fro... | python | def graph_impl(self, run, tag, is_conceptual, limit_attr_size=None, large_attrs_key=None):
"""Result of the form `(body, mime_type)`, or `None` if no graph exists."""
if is_conceptual:
tensor_events = self._multiplexer.Tensors(run, tag)
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tensorflow/tensorboard | tensorboard/plugins/graph/graphs_plugin.py | GraphsPlugin.run_metadata_impl | def run_metadata_impl(self, run, tag):
"""Result of the form `(body, mime_type)`, or `None` if no data exists."""
try:
run_metadata = self._multiplexer.RunMetadata(run, tag)
except ValueError:
# TODO(stephanwlee): Should include whether FE is fetching for v1 or v2 RunMetadata
# so we can r... | python | def run_metadata_impl(self, run, tag):
"""Result of the form `(body, mime_type)`, or `None` if no data exists."""
try:
run_metadata = self._multiplexer.RunMetadata(run, tag)
except ValueError:
# TODO(stephanwlee): Should include whether FE is fetching for v1 or v2 RunMetadata
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tensorflow/tensorboard | tensorboard/plugins/graph/graphs_plugin.py | GraphsPlugin.graph_route | def graph_route(self, request):
"""Given a single run, return the graph definition in protobuf format."""
run = request.args.get('run')
tag = request.args.get('tag', '')
conceptual_arg = request.args.get('conceptual', False)
is_conceptual = True if conceptual_arg == 'true' else False
if run is ... | python | def graph_route(self, request):
"""Given a single run, return the graph definition in protobuf format."""
run = request.args.get('run')
tag = request.args.get('tag', '')
conceptual_arg = request.args.get('conceptual', False)
is_conceptual = True if conceptual_arg == 'true' else False
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tensorflow/tensorboard | tensorboard/plugins/graph/graphs_plugin.py | GraphsPlugin.run_metadata_route | def run_metadata_route(self, request):
"""Given a tag and a run, return the session.run() metadata."""
tag = request.args.get('tag')
run = request.args.get('run')
if tag is None:
return http_util.Respond(
request, 'query parameter "tag" is required', 'text/plain', 400)
if run is None... | python | def run_metadata_route(self, request):
"""Given a tag and a run, return the session.run() metadata."""
tag = request.args.get('tag')
run = request.args.get('run')
if tag is None:
return http_util.Respond(
request, 'query parameter "tag" is required', 'text/plain', 400)
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin_loader.py | ProfilePluginLoader.load | def load(self, context):
"""Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A ProfilePlugin instance or None if it couldn't be loaded.
"""
try:
# pylint: disable=g-import-not-at-top,unused-import
import tensorflow
# Available in TensorFlo... | python | def load(self, context):
"""Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A ProfilePlugin instance or None if it couldn't be loaded.
"""
try:
# pylint: disable=g-import-not-at-top,unused-import
import tensorflow
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_demo.py | model_fn | def model_fn(hparams, seed):
"""Create a Keras model with the given hyperparameters.
Args:
hparams: A dict mapping hyperparameters in `HPARAMS` to values.
seed: A hashable object to be used as a random seed (e.g., to
construct dropout layers in the model).
Returns:
A compiled Keras model.
""... | python | def model_fn(hparams, seed):
"""Create a Keras model with the given hyperparameters.
Args:
hparams: A dict mapping hyperparameters in `HPARAMS` to values.
seed: A hashable object to be used as a random seed (e.g., to
construct dropout layers in the model).
Returns:
A compiled Keras model.
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_demo.py | run | def run(data, base_logdir, session_id, group_id, hparams):
"""Run a training/validation session.
Flags must have been parsed for this function to behave.
Args:
data: The data as loaded by `prepare_data()`.
base_logdir: The top-level logdir to which to write summary data.
session_id: A unique string ... | python | def run(data, base_logdir, session_id, group_id, hparams):
"""Run a training/validation session.
Flags must have been parsed for this function to behave.
Args:
data: The data as loaded by `prepare_data()`.
base_logdir: The top-level logdir to which to write summary data.
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_demo.py | prepare_data | def prepare_data():
"""Load and normalize data."""
((x_train, y_train), (x_test, y_test)) = DATASET.load_data()
x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255.0
x_test /= 255.0
return ((x_train, y_train), (x_test, y_test)) | python | def prepare_data():
"""Load and normalize data."""
((x_train, y_train), (x_test, y_test)) = DATASET.load_data()
x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255.0
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_demo.py | run_all | def run_all(logdir, verbose=False):
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_demo.py | sample_uniform | def sample_uniform(domain, rng):
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"""Sample a value uniformly from a domain.
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domain: An `IntInterval`, `RealInterval`, or `Discrete` domain.
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curves_plugin.py | PrCurvesPlugin.pr_curves_route | def pr_curves_route(self, request):
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curves_plugin.py | PrCurvesPlugin.pr_curves_impl | def pr_curves_impl(self, runs, tag):
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runs: A list of runs to fetch the curves for.
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runs: A list of runs to fetch the curves for.
tag: The tag to fetch the curves for.
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curves_plugin.py | PrCurvesPlugin.tags_impl | def tags_impl(self):
"""Creates the JSON object for the tags route response.
Returns:
The JSON object for the tags route response.
"""
if self._db_connection_provider:
# Read tags from the database.
db = self._db_connection_provider()
cursor = db.execute('''
SELECT
... | python | def tags_impl(self):
"""Creates the JSON object for the tags route response.
Returns:
The JSON object for the tags route response.
"""
if self._db_connection_provider:
# Read tags from the database.
db = self._db_connection_provider()
cursor = db.execute('''
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curves_plugin.py | PrCurvesPlugin.available_time_entries_impl | def available_time_entries_impl(self):
"""Creates the JSON object for the available time entries route response.
Returns:
The JSON object for the available time entries route response.
"""
result = {}
if self._db_connection_provider:
db = self._db_connection_provider()
# For each ... | python | def available_time_entries_impl(self):
"""Creates the JSON object for the available time entries route response.
Returns:
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"""
result = {}
if self._db_connection_provider:
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curves_plugin.py | PrCurvesPlugin.is_active | def is_active(self):
"""Determines whether this plugin is active.
This plugin is active only if PR curve summary data is read by TensorBoard.
Returns:
Whether this plugin is active.
"""
if self._db_connection_provider:
# The plugin is active if one relevant tag can be found in the data... | python | def is_active(self):
"""Determines whether this plugin is active.
This plugin is active only if PR curve summary data is read by TensorBoard.
Returns:
Whether this plugin is active.
"""
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curves_plugin.py | PrCurvesPlugin._process_tensor_event | def _process_tensor_event(self, event, thresholds):
"""Converts a TensorEvent into a dict that encapsulates information on it.
Args:
event: The TensorEvent to convert.
thresholds: An array of floats that ranges from 0 to 1 (in that
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Returns:
A ... | python | def _process_tensor_event(self, event, thresholds):
"""Converts a TensorEvent into a dict that encapsulates information on it.
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event: The TensorEvent to convert.
thresholds: An array of floats that ranges from 0 to 1 (in that
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curves_plugin.py | PrCurvesPlugin._make_pr_entry | def _make_pr_entry(self, step, wall_time, data_array, thresholds):
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step: The step.
wall_time: The wall time.
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step: The step.
wall_time: The wall time.
data_array: A numpy array of PR curve data stored in the summary format.
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tensorflow/tensorboard | tensorboard/plugins/hparams/api.py | _normalize_hparams | def _normalize_hparams(hparams):
"""Normalize a dict keyed by `HParam`s and/or raw strings.
Args:
hparams: A `dict` whose keys are `HParam` objects and/or strings
representing hyperparameter names, and whose values are
hyperparameter values. No two keys may have the same name.
Returns:
A `di... | python | def _normalize_hparams(hparams):
"""Normalize a dict keyed by `HParam`s and/or raw strings.
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hparams: A `dict` whose keys are `HParam` objects and/or strings
representing hyperparameter names, and whose values are
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tensorflow/tensorboard | tensorboard/plugins/hparams/api.py | Experiment.summary_pb | def summary_pb(self):
"""Create a top-level experiment summary describing this experiment.
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Analogous to the low-level `experiment_pb` function in the
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... | python | def summary_pb(self):
"""Create a top-level experiment summary describing this experiment.
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tensorflow/tensorboard | tensorboard/plugins/histogram/histograms_demo.py | run_all | def run_all(logdir, verbose=False, num_summaries=400):
"""Generate a bunch of histogram data, and write it to logdir."""
del verbose
tf.compat.v1.set_random_seed(0)
k = tf.compat.v1.placeholder(tf.float32)
# Make a normal distribution, with a shifting mean
mean_moving_normal = tf.random.normal(shape=[100... | python | def run_all(logdir, verbose=False, num_summaries=400):
"""Generate a bunch of histogram data, and write it to logdir."""
del verbose
tf.compat.v1.set_random_seed(0)
k = tf.compat.v1.placeholder(tf.float32)
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tensorflow/tensorboard | tensorboard/plugins/projector/projector_plugin.py | _parse_positive_int_param | def _parse_positive_int_param(request, param_name):
"""Parses and asserts a positive (>0) integer query parameter.
Args:
request: The Werkzeug Request object
param_name: Name of the parameter.
Returns:
Param, or None, or -1 if parameter is not a positive integer.
"""
param = request.args.get(par... | python | def _parse_positive_int_param(request, param_name):
"""Parses and asserts a positive (>0) integer query parameter.
Args:
request: The Werkzeug Request object
param_name: Name of the parameter.
Returns:
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tensorflow/tensorboard | tensorboard/plugins/projector/projector_plugin.py | EmbeddingMetadata.add_column | def add_column(self, column_name, column_values):
"""Adds a named column of metadata values.
Args:
column_name: Name of the column.
column_values: 1D array/list/iterable holding the column values. Must be
of length `num_points`. The i-th value corresponds to the i-th point.
Raises:
... | python | def add_column(self, column_name, column_values):
"""Adds a named column of metadata values.
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column_name: Name of the column.
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tensorflow/tensorboard | tensorboard/plugins/projector/projector_plugin.py | ProjectorPlugin.is_active | def is_active(self):
"""Determines whether this plugin is active.
This plugin is only active if any run has an embedding.
Returns:
Whether any run has embedding data to show in the projector.
"""
if not self.multiplexer:
return False
if self._is_active:
# We have already det... | python | def is_active(self):
"""Determines whether this plugin is active.
This plugin is only active if any run has an embedding.
Returns:
Whether any run has embedding data to show in the projector.
"""
if not self.multiplexer:
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tensorflow/tensorboard | tensorboard/plugins/projector/projector_plugin.py | ProjectorPlugin.configs | def configs(self):
"""Returns a map of run paths to `ProjectorConfig` protos."""
run_path_pairs = list(self.run_paths.items())
self._append_plugin_asset_directories(run_path_pairs)
# If there are no summary event files, the projector should still work,
# treating the `logdir` as the model checkpoint... | python | def configs(self):
"""Returns a map of run paths to `ProjectorConfig` protos."""
run_path_pairs = list(self.run_paths.items())
self._append_plugin_asset_directories(run_path_pairs)
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_multiplexer.py | EventMultiplexer.Reload | def Reload(self):
"""Call `Reload` on every `EventAccumulator`."""
logger.info('Beginning EventMultiplexer.Reload()')
self._reload_called = True
# Build a list so we're safe even if the list of accumulators is modified
# even while we're reloading.
with self._accumulators_mutex:
items = li... | python | def Reload(self):
"""Call `Reload` on every `EventAccumulator`."""
logger.info('Beginning EventMultiplexer.Reload()')
self._reload_called = True
# Build a list so we're safe even if the list of accumulators is modified
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_multiplexer.py | EventMultiplexer.Histograms | def Histograms(self, run, tag):
"""Retrieve the histogram events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not a... | python | def Histograms(self, run, tag):
"""Retrieve the histogram events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_multiplexer.py | EventMultiplexer.CompressedHistograms | def CompressedHistograms(self, run, tag):
"""Retrieve the compressed histogram events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found... | python | def CompressedHistograms(self, run, tag):
"""Retrieve the compressed histogram events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_multiplexer.py | EventMultiplexer.Images | def Images(self, run, tag):
"""Retrieve the image events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not available... | python | def Images(self, run, tag):
"""Retrieve the image events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
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tensorflow/tensorboard | tensorboard/plugins/histogram/summary_v2.py | histogram | def histogram(name, data, step=None, buckets=None, description=None):
"""Write a histogram summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A `Tensor` of any shape. Must be castable to `float64`.
st... | python | def histogram(name, data, step=None, buckets=None, description=None):
"""Write a histogram summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A `Tensor` of any shape. Must be castable to `float64`.
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tensorflow/tensorboard | tensorboard/plugins/histogram/summary_v2.py | histogram_pb | def histogram_pb(tag, data, buckets=None, description=None):
"""Create a histogram summary protobuf.
Arguments:
tag: String tag for the summary.
data: A `np.array` or array-like form of any shape. Must have type
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buckets: Optional positive `int`. The output will have this
... | python | def histogram_pb(tag, data, buckets=None, description=None):
"""Create a histogram summary protobuf.
Arguments:
tag: String tag for the summary.
data: A `np.array` or array-like form of any shape. Must have type
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tensorflow/tensorboard | tensorboard/program.py | setup_environment | def setup_environment():
"""Makes recommended modifications to the environment.
This functions changes global state in the Python process. Calling
this function is a good idea, but it can't appropriately be called
from library routines.
"""
absl.logging.set_verbosity(absl.logging.WARNING)
# The default ... | python | def setup_environment():
"""Makes recommended modifications to the environment.
This functions changes global state in the Python process. Calling
this function is a good idea, but it can't appropriately be called
from library routines.
"""
absl.logging.set_verbosity(absl.logging.WARNING)
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tensorflow/tensorboard | tensorboard/program.py | get_default_assets_zip_provider | def get_default_assets_zip_provider():
"""Opens stock TensorBoard web assets collection.
Returns:
Returns function that returns a newly opened file handle to zip file
containing static assets for stock TensorBoard, or None if webfiles.zip
could not be found. The value the callback returns must be close... | python | def get_default_assets_zip_provider():
"""Opens stock TensorBoard web assets collection.
Returns:
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tensorflow/tensorboard | tensorboard/program.py | with_port_scanning | def with_port_scanning(cls):
"""Create a server factory that performs port scanning.
This function returns a callable whose signature matches the
specification of `TensorBoardServer.__init__`, using `cls` as an
underlying implementation. It passes through `flags` unchanged except
in the case that `flags.port... | python | def with_port_scanning(cls):
"""Create a server factory that performs port scanning.
This function returns a callable whose signature matches the
specification of `TensorBoardServer.__init__`, using `cls` as an
underlying implementation. It passes through `flags` unchanged except
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tensorflow/tensorboard | tensorboard/program.py | TensorBoard.configure | def configure(self, argv=('',), **kwargs):
"""Configures TensorBoard behavior via flags.
This method will populate the "flags" property with an argparse.Namespace
representing flag values parsed from the provided argv list, overridden by
explicit flags from remaining keyword arguments.
Args:
... | python | def configure(self, argv=('',), **kwargs):
"""Configures TensorBoard behavior via flags.
This method will populate the "flags" property with an argparse.Namespace
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tensorflow/tensorboard | tensorboard/program.py | TensorBoard.main | def main(self, ignored_argv=('',)):
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ignored_argv: Do not pass. Requir... | python | def main(self, ignored_argv=('',)):
"""Blocking main function for TensorBoard.
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tensorflow/tensorboard | tensorboard/program.py | TensorBoard.launch | def launch(self):
"""Python API for launching TensorBoard.
This method is the same as main() except it launches TensorBoard in
a separate permanent thread. The configure() method must be called
first.
Returns:
The URL of the TensorBoard web server.
:rtype: str
"""
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"""Python API for launching TensorBoard.
This method is the same as main() except it launches TensorBoard in
a separate permanent thread. The configure() method must be called
first.
Returns:
The URL of the TensorBoard web server.
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tensorflow/tensorboard | tensorboard/program.py | TensorBoard._register_info | def _register_info(self, server):
"""Write a TensorBoardInfo file and arrange for its cleanup.
Args:
server: The result of `self._make_server()`.
"""
server_url = urllib.parse.urlparse(server.get_url())
info = manager.TensorBoardInfo(
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start_time=int(ti... | python | def _register_info(self, server):
"""Write a TensorBoardInfo file and arrange for its cleanup.
Args:
server: The result of `self._make_server()`.
"""
server_url = urllib.parse.urlparse(server.get_url())
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tensorflow/tensorboard | tensorboard/program.py | WerkzeugServer.handle_error | def handle_error(self, request, client_address):
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del request # unused
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# example, `curl -N http://localhost... | python | def handle_error(self, request, client_address):
"""Override to get rid of noisy EPIPE errors."""
del request # unused
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tensorflow/tensorboard | tensorboard/plugins/profile/trace_events_json.py | TraceEventsJsonStream._events | def _events(self):
"""Iterator over all catapult trace events, as python values."""
for did, device in sorted(six.iteritems(self._proto.devices)):
if device.name:
yield dict(
ph=_TYPE_METADATA,
pid=did,
name='process_name',
args=dict(name=device.name... | python | def _events(self):
"""Iterator over all catapult trace events, as python values."""
for did, device in sorted(six.iteritems(self._proto.devices)):
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tensorflow/tensorboard | tensorboard/plugins/profile/trace_events_json.py | TraceEventsJsonStream._event | def _event(self, event):
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name=event.name,
ts=event.timestamp_ps / 1000000.0)
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name=event.name,
ts=event.timestamp_ps / 1000000.0)
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tensorflow/tensorboard | tensorboard/plugins/scalar/summary.py | op | def op(name,
data,
display_name=None,
description=None,
collections=None):
"""Create a legacy scalar summary op.
Arguments:
name: A unique name for the generated summary node.
data: A real numeric rank-0 `Tensor`. Must have `dtype` castable
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display_name: ... | python | def op(name,
data,
display_name=None,
description=None,
collections=None):
"""Create a legacy scalar summary op.
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name: A unique name for the generated summary node.
data: A real numeric rank-0 `Tensor`. Must have `dtype` castable
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name: A unique name for the generated summary, including any desired
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tensorflow/tensorboard | tensorboard/scripts/execrooter.py | run | def run(inputs, program, outputs):
"""Creates temp symlink tree, runs program, and copies back outputs.
Args:
inputs: List of fake paths to real paths, which are used for symlink tree.
program: List containing real path of program and its arguments. The
execroot directory will be appended as the la... | python | def run(inputs, program, outputs):
"""Creates temp symlink tree, runs program, and copies back outputs.
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tensorflow/tensorboard | tensorboard/scripts/execrooter.py | main | def main(args):
"""Invokes run function using a JSON file config.
Args:
args: CLI args, which can be a JSON file containing an object whose
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"""Invokes run function using a JSON file config.
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tensorflow/tensorboard | tensorboard/backend/event_processing/sqlite_writer.py | initialize_schema | def initialize_schema(connection):
"""Initializes the TensorBoard sqlite schema using the given connection.
Args:
connection: A sqlite DB connection.
"""
cursor = connection.cursor()
cursor.execute("PRAGMA application_id={}".format(_TENSORBOARD_APPLICATION_ID))
cursor.execute("PRAGMA user_version={}".f... | python | def initialize_schema(connection):
"""Initializes the TensorBoard sqlite schema using the given connection.
Args:
connection: A sqlite DB connection.
"""
cursor = connection.cursor()
cursor.execute("PRAGMA application_id={}".format(_TENSORBOARD_APPLICATION_ID))
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tensorflow/tensorboard | tensorboard/backend/event_processing/sqlite_writer.py | SqliteWriter._create_id | def _create_id(self):
"""Returns a freshly created DB-wide unique ID."""
cursor = self._db.cursor()
cursor.execute('INSERT INTO Ids DEFAULT VALUES')
return cursor.lastrowid | python | def _create_id(self):
"""Returns a freshly created DB-wide unique ID."""
cursor = self._db.cursor()
cursor.execute('INSERT INTO Ids DEFAULT VALUES')
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tensorflow/tensorboard | tensorboard/backend/event_processing/sqlite_writer.py | SqliteWriter._maybe_init_user | def _maybe_init_user(self):
"""Returns the ID for the current user, creating the row if needed."""
user_name = os.environ.get('USER', '') or os.environ.get('USERNAME', '')
cursor = self._db.cursor()
cursor.execute('SELECT user_id FROM Users WHERE user_name = ?',
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row ... | python | def _maybe_init_user(self):
"""Returns the ID for the current user, creating the row if needed."""
user_name = os.environ.get('USER', '') or os.environ.get('USERNAME', '')
cursor = self._db.cursor()
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tensorflow/tensorboard | tensorboard/backend/event_processing/sqlite_writer.py | SqliteWriter._maybe_init_experiment | def _maybe_init_experiment(self, experiment_name):
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experiment_name: name of experiment.
"""
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"""Returns the ID for the given experiment, creating the row if needed.
Args:
experiment_name: name of experiment.
"""
user_id = self._maybe_init_user()
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tensorflow/tensorboard | tensorboard/backend/event_processing/sqlite_writer.py | SqliteWriter._maybe_init_run | def _maybe_init_run(self, experiment_name, run_name):
"""Returns the ID for the given run, creating the row if needed.
Args:
experiment_name: name of experiment containing this run.
run_name: name of run.
"""
experiment_id = self._maybe_init_experiment(experiment_name)
cursor = self._db... | python | def _maybe_init_run(self, experiment_name, run_name):
"""Returns the ID for the given run, creating the row if needed.
Args:
experiment_name: name of experiment containing this run.
run_name: name of run.
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experiment_id = self._maybe_init_experiment(experiment_name)
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tensorflow/tensorboard | tensorboard/backend/event_processing/sqlite_writer.py | SqliteWriter._maybe_init_tags | def _maybe_init_tags(self, run_id, tag_to_metadata):
"""Returns a tag-to-ID map for the given tags, creating rows if needed.
Args:
run_id: the ID of the run to which these tags belong.
tag_to_metadata: map of tag name to SummaryMetadata for the tag.
"""
cursor = self._db.cursor()
# TODO... | python | def _maybe_init_tags(self, run_id, tag_to_metadata):
"""Returns a tag-to-ID map for the given tags, creating rows if needed.
Args:
run_id: the ID of the run to which these tags belong.
tag_to_metadata: map of tag name to SummaryMetadata for the tag.
"""
cursor = self._db.cursor()
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tensorflow/tensorboard | tensorboard/backend/event_processing/sqlite_writer.py | SqliteWriter.write_summaries | def write_summaries(self, tagged_data, experiment_name, run_name):
"""Transactionally writes the given tagged summary data to the DB.
Args:
tagged_data: map from tag to TagData instances.
experiment_name: name of experiment.
run_name: name of run.
"""
logger.debug('Writing summaries f... | python | def write_summaries(self, tagged_data, experiment_name, run_name):
"""Transactionally writes the given tagged summary data to the DB.
Args:
tagged_data: map from tag to TagData instances.
experiment_name: name of experiment.
run_name: name of run.
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tensorflow/tensorboard | tensorboard/plugins/image/images_demo.py | image_data | def image_data(verbose=False):
"""Get the raw encoded image data, downloading it if necessary."""
# This is a principled use of the `global` statement; don't lint me.
global _IMAGE_DATA # pylint: disable=global-statement
if _IMAGE_DATA is None:
if verbose:
logger.info("--- Downloading image.")
wi... | python | def image_data(verbose=False):
"""Get the raw encoded image data, downloading it if necessary."""
# This is a principled use of the `global` statement; don't lint me.
global _IMAGE_DATA # pylint: disable=global-statement
if _IMAGE_DATA is None:
if verbose:
logger.info("--- Downloading image.")
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tensorflow/tensorboard | tensorboard/plugins/image/images_demo.py | convolve | def convolve(image, pixel_filter, channels=3, name=None):
"""Perform a 2D pixel convolution on the given image.
Arguments:
image: A 3D `float32` `Tensor` of shape `[height, width, channels]`,
where `channels` is the third argument to this function and the
first two dimensions are arbitrary.
pix... | python | def convolve(image, pixel_filter, channels=3, name=None):
"""Perform a 2D pixel convolution on the given image.
Arguments:
image: A 3D `float32` `Tensor` of shape `[height, width, channels]`,
where `channels` is the third argument to this function and the
first two dimensions are arbitrary.
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tensorflow/tensorboard | tensorboard/plugins/image/images_demo.py | get_image | def get_image(verbose=False):
"""Get the image as a TensorFlow variable.
Returns:
A `tf.Variable`, which must be initialized prior to use:
invoke `sess.run(result.initializer)`."""
base_data = tf.constant(image_data(verbose=verbose))
base_image = tf.image.decode_image(base_data, channels=3)
base_imag... | python | def get_image(verbose=False):
"""Get the image as a TensorFlow variable.
Returns:
A `tf.Variable`, which must be initialized prior to use:
invoke `sess.run(result.initializer)`."""
base_data = tf.constant(image_data(verbose=verbose))
base_image = tf.image.decode_image(base_data, channels=3)
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tensorflow/tensorboard | tensorboard/plugins/image/images_demo.py | run_box_to_gaussian | def run_box_to_gaussian(logdir, verbose=False):
"""Run a box-blur-to-Gaussian-blur demonstration.
See the summary description for more details.
Arguments:
logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output.
"""
if verbose:
logger.info('--- Starting run:... | python | def run_box_to_gaussian(logdir, verbose=False):
"""Run a box-blur-to-Gaussian-blur demonstration.
See the summary description for more details.
Arguments:
logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output.
"""
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tensorflow/tensorboard | tensorboard/plugins/image/images_demo.py | run_sobel | def run_sobel(logdir, verbose=False):
"""Run a Sobel edge detection demonstration.
See the summary description for more details.
Arguments:
logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output.
"""
if verbose:
logger.info('--- Starting run: sobel')
tf.... | python | def run_sobel(logdir, verbose=False):
"""Run a Sobel edge detection demonstration.
See the summary description for more details.
Arguments:
logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output.
"""
if verbose:
logger.info('--- Starting run: sobel')
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tensorflow/tensorboard | tensorboard/plugins/image/images_demo.py | run_all | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
"""
run_box_to_gaussian(logdir, verbose=verbose)
run_sobel(logdir, verbose=verbose... | python | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
"""
run_box_to_gaussian(logdir, verbose=verbose)
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | proto_value_for_feature | def proto_value_for_feature(example, feature_name):
"""Get the value of a feature from Example regardless of feature type."""
feature = get_example_features(example)[feature_name]
if feature is None:
raise ValueError('Feature {} is not on example proto.'.format(feature_name))
feature_type = feature.WhichOne... | python | def proto_value_for_feature(example, feature_name):
"""Get the value of a feature from Example regardless of feature type."""
feature = get_example_features(example)[feature_name]
if feature is None:
raise ValueError('Feature {} is not on example proto.'.format(feature_name))
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | parse_original_feature_from_example | def parse_original_feature_from_example(example, feature_name):
"""Returns an `OriginalFeatureList` for the specified feature_name.
Args:
example: An example.
feature_name: A string feature name.
Returns:
A filled in `OriginalFeatureList` object representing the feature.
"""
feature = get_exampl... | python | def parse_original_feature_from_example(example, feature_name):
"""Returns an `OriginalFeatureList` for the specified feature_name.
Args:
example: An example.
feature_name: A string feature name.
Returns:
A filled in `OriginalFeatureList` object representing the feature.
"""
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | wrap_inference_results | def wrap_inference_results(inference_result_proto):
"""Returns packaged inference results from the provided proto.
Args:
inference_result_proto: The classification or regression response proto.
Returns:
An InferenceResult proto with the result from the response.
"""
inference_proto = inference_pb2.I... | python | def wrap_inference_results(inference_result_proto):
"""Returns packaged inference results from the provided proto.
Args:
inference_result_proto: The classification or regression response proto.
Returns:
An InferenceResult proto with the result from the response.
"""
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | get_numeric_feature_names | def get_numeric_feature_names(example):
"""Returns a list of feature names for float and int64 type features.
Args:
example: An example.
Returns:
A list of strings of the names of numeric features.
"""
numeric_features = ('float_list', 'int64_list')
features = get_example_features(example)
retur... | python | def get_numeric_feature_names(example):
"""Returns a list of feature names for float and int64 type features.
Args:
example: An example.
Returns:
A list of strings of the names of numeric features.
"""
numeric_features = ('float_list', 'int64_list')
features = get_example_features(example)
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | get_categorical_feature_names | def get_categorical_feature_names(example):
"""Returns a list of feature names for byte type features.
Args:
example: An example.
Returns:
A list of categorical feature names (e.g. ['education', 'marital_status'] )
"""
features = get_example_features(example)
return sorted([
feature_name for... | python | def get_categorical_feature_names(example):
"""Returns a list of feature names for byte type features.
Args:
example: An example.
Returns:
A list of categorical feature names (e.g. ['education', 'marital_status'] )
"""
features = get_example_features(example)
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | get_numeric_features_to_observed_range | def get_numeric_features_to_observed_range(examples):
"""Returns numerical features and their observed ranges.
Args:
examples: Examples to read to get ranges.
Returns:
A dict mapping feature_name -> {'observedMin': 'observedMax': } dicts,
with a key for each numerical feature.
"""
observed_featu... | python | def get_numeric_features_to_observed_range(examples):
"""Returns numerical features and their observed ranges.
Args:
examples: Examples to read to get ranges.
Returns:
A dict mapping feature_name -> {'observedMin': 'observedMax': } dicts,
with a key for each numerical feature.
"""
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | get_categorical_features_to_sampling | def get_categorical_features_to_sampling(examples, top_k):
"""Returns categorical features and a sampling of their most-common values.
The results of this slow function are used by the visualization repeatedly,
so the results are cached.
Args:
examples: Examples to read to get feature samples.
top_k: ... | python | def get_categorical_features_to_sampling(examples, top_k):
"""Returns categorical features and a sampling of their most-common values.
The results of this slow function are used by the visualization repeatedly,
so the results are cached.
Args:
examples: Examples to read to get feature samples.
top_k: ... | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | make_mutant_features | def make_mutant_features(original_feature, index_to_mutate, viz_params):
"""Return a list of `MutantFeatureValue`s that are variants of original."""
lower = viz_params.x_min
upper = viz_params.x_max
examples = viz_params.examples
num_mutants = viz_params.num_mutants
if original_feature.feature_type == 'flo... | python | def make_mutant_features(original_feature, index_to_mutate, viz_params):
"""Return a list of `MutantFeatureValue`s that are variants of original."""
lower = viz_params.x_min
upper = viz_params.x_max
examples = viz_params.examples
num_mutants = viz_params.num_mutants
if original_feature.feature_type == 'flo... | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | make_mutant_tuples | def make_mutant_tuples(example_protos, original_feature, index_to_mutate,
viz_params):
"""Return a list of `MutantFeatureValue`s and a list of mutant Examples.
Args:
example_protos: The examples to mutate.
original_feature: A `OriginalFeatureList` that encapsulates the feature to
... | python | def make_mutant_tuples(example_protos, original_feature, index_to_mutate,
viz_params):
"""Return a list of `MutantFeatureValue`s and a list of mutant Examples.
Args:
example_protos: The examples to mutate.
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | mutant_charts_for_feature | def mutant_charts_for_feature(example_protos, feature_name, serving_bundles,
viz_params):
"""Returns JSON formatted for rendering all charts for a feature.
Args:
example_proto: The example protos to mutate.
feature_name: The string feature name to mutate.
serving_bundles: ... | python | def mutant_charts_for_feature(example_protos, feature_name, serving_bundles,
viz_params):
"""Returns JSON formatted for rendering all charts for a feature.
Args:
example_proto: The example protos to mutate.
feature_name: The string feature name to mutate.
serving_bundles: ... | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | make_json_formatted_for_single_chart | def make_json_formatted_for_single_chart(mutant_features,
inference_result_proto,
index_to_mutate):
"""Returns JSON formatted for a single mutant chart.
Args:
mutant_features: An iterable of `MutantFeatureValue`s representing the... | python | def make_json_formatted_for_single_chart(mutant_features,
inference_result_proto,
index_to_mutate):
"""Returns JSON formatted for a single mutant chart.
Args:
mutant_features: An iterable of `MutantFeatureValue`s representing the... | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | get_example_features | def get_example_features(example):
"""Returns the non-sequence features from the provided example."""
return (example.features.feature if isinstance(example, tf.train.Example)
else example.context.feature) | python | def get_example_features(example):
"""Returns the non-sequence features from the provided example."""
return (example.features.feature if isinstance(example, tf.train.Example)
else example.context.feature) | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | run_inference_for_inference_results | def run_inference_for_inference_results(examples, serving_bundle):
"""Calls servo and wraps the inference results."""
inference_result_proto = run_inference(examples, serving_bundle)
inferences = wrap_inference_results(inference_result_proto)
infer_json = json_format.MessageToJson(
inferences, including_def... | python | def run_inference_for_inference_results(examples, serving_bundle):
"""Calls servo and wraps the inference results."""
inference_result_proto = run_inference(examples, serving_bundle)
inferences = wrap_inference_results(inference_result_proto)
infer_json = json_format.MessageToJson(
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | get_eligible_features | def get_eligible_features(examples, num_mutants):
"""Returns a list of JSON objects for each feature in the examples.
This list is used to drive partial dependence plots in the plugin.
Args:
examples: Examples to examine to determine the eligible features.
num_mutants: The number of mutations to... | python | def get_eligible_features(examples, num_mutants):
"""Returns a list of JSON objects for each feature in the examples.
This list is used to drive partial dependence plots in the plugin.
Args:
examples: Examples to examine to determine the eligible features.
num_mutants: The number of mutations to... | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | get_label_vocab | def get_label_vocab(vocab_path):
"""Returns a list of label strings loaded from the provided path."""
if vocab_path:
try:
with tf.io.gfile.GFile(vocab_path, 'r') as f:
return [line.rstrip('\n') for line in f]
except tf.errors.NotFoundError as err:
tf.logging.error('error reading vocab fi... | python | def get_label_vocab(vocab_path):
"""Returns a list of label strings loaded from the provided path."""
if vocab_path:
try:
with tf.io.gfile.GFile(vocab_path, 'r') as f:
return [line.rstrip('\n') for line in f]
except tf.errors.NotFoundError as err:
tf.logging.error('error reading vocab fi... | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | create_sprite_image | def create_sprite_image(examples):
"""Returns an encoded sprite image for use in Facets Dive.
Args:
examples: A list of serialized example protos to get images for.
Returns:
An encoded PNG.
"""
def generate_image_from_thubnails(thumbnails, thumbnail_dims):
"""Generates a sprite ... | python | def create_sprite_image(examples):
"""Returns an encoded sprite image for use in Facets Dive.
Args:
examples: A list of serialized example protos to get images for.
Returns:
An encoded PNG.
"""
def generate_image_from_thubnails(thumbnails, thumbnail_dims):
"""Generates a sprite ... | [
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/inference_utils.py | run_inference | def run_inference(examples, serving_bundle):
"""Run inference on examples given model information
Args:
examples: A list of examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the inference request.
Returns:
A ClassificationRespo... | python | def run_inference(examples, serving_bundle):
"""Run inference on examples given model information
Args:
examples: A list of examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the inference request.
Returns:
A ClassificationRespo... | [
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tensorflow/tensorboard | tensorboard/backend/event_processing/reservoir.py | Reservoir.Items | def Items(self, key):
"""Return items associated with given key.
Args:
key: The key for which we are finding associated items.
Raises:
KeyError: If the key is not found in the reservoir.
Returns:
[list, of, items] associated with that key.
"""
with self._mutex:
if key ... | python | def Items(self, key):
"""Return items associated with given key.
Args:
key: The key for which we are finding associated items.
Raises:
KeyError: If the key is not found in the reservoir.
Returns:
[list, of, items] associated with that key.
"""
with self._mutex:
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