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train | GraphsPlugin.run_metadata_route | Given a tag and a run, return the session.run() metadata. | tensorboard/plugins/graph/graphs_plugin.py | 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... | 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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train | ProfilePluginLoader.load | Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A ProfilePlugin instance or None if it couldn't be loaded. | tensorboard/plugins/profile/profile_plugin_loader.py | 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... | 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.
"""
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import tensorflow
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train | model_fn | 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
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Returns:
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"""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
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Returns:
A compiled Keras model.
""... | def model_fn(hparams, seed):
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Args:
hparams: A dict mapping hyperparameters in `HPARAMS` to values.
seed: A hashable object to be used as a random seed (e.g., to
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Returns:
A compiled Keras model.
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train | run | Run a training/validation session.
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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 ID for this session.
group_id: The string ID of the session ... | tensorboard/plugins/hparams/hparams_demo.py | 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 ... | def run(data, base_logdir, session_id, group_id, hparams):
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Args:
data: The data as loaded by `prepare_data()`.
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train | prepare_data | Load and normalize data. | tensorboard/plugins/hparams/hparams_demo.py | 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)) | 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)) | [
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train | run_all | Perform random search over the hyperparameter space.
Arguments:
logdir: The top-level directory into which to write data. This
directory should be empty or nonexistent.
verbose: If true, print out each run's name as it begins. | tensorboard/plugins/hparams/hparams_demo.py | def run_all(logdir, verbose=False):
"""Perform random search over the hyperparameter space.
Arguments:
logdir: The top-level directory into which to write data. This
directory should be empty or nonexistent.
verbose: If true, print out each run's name as it begins.
"""
data = prepare_data()
rng... | def run_all(logdir, verbose=False):
"""Perform random search over the hyperparameter space.
Arguments:
logdir: The top-level directory into which to write data. This
directory should be empty or nonexistent.
verbose: If true, print out each run's name as it begins.
"""
data = prepare_data()
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train | sample_uniform | Sample a value uniformly from a domain.
Args:
domain: An `IntInterval`, `RealInterval`, or `Discrete` domain.
rng: A `random.Random` object; defaults to the `random` module.
Raises:
TypeError: If `domain` is not a known kind of domain.
IndexError: If the domain is empty. | tensorboard/plugins/hparams/hparams_demo.py | def sample_uniform(domain, rng):
"""Sample a value uniformly from a domain.
Args:
domain: An `IntInterval`, `RealInterval`, or `Discrete` domain.
rng: A `random.Random` object; defaults to the `random` module.
Raises:
TypeError: If `domain` is not a known kind of domain.
IndexError: If the domai... | def sample_uniform(domain, rng):
"""Sample a value uniformly from a domain.
Args:
domain: An `IntInterval`, `RealInterval`, or `Discrete` domain.
rng: A `random.Random` object; defaults to the `random` module.
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train | PrCurvesPlugin.pr_curves_route | A route that returns a JSON mapping between runs and PR curve data.
Returns:
Given a tag and a comma-separated list of runs (both stored within GET
parameters), fetches a JSON object that maps between run name and objects
containing data required for PR curves for that run. Runs that either
... | tensorboard/plugins/pr_curve/pr_curves_plugin.py | def pr_curves_route(self, request):
"""A route that returns a JSON mapping between runs and PR curve data.
Returns:
Given a tag and a comma-separated list of runs (both stored within GET
parameters), fetches a JSON object that maps between run name and objects
containing data required for PR ... | def pr_curves_route(self, request):
"""A route that returns a JSON mapping between runs and PR curve data.
Returns:
Given a tag and a comma-separated list of runs (both stored within GET
parameters), fetches a JSON object that maps between run name and objects
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train | PrCurvesPlugin.pr_curves_impl | Creates the JSON object for the PR curves response for a run-tag combo.
Arguments:
runs: A list of runs to fetch the curves for.
tag: The tag to fetch the curves for.
Raises:
ValueError: If no PR curves could be fetched for a run and tag.
Returns:
The JSON object for the PR curves... | tensorboard/plugins/pr_curve/pr_curves_plugin.py | def pr_curves_impl(self, runs, tag):
"""Creates the JSON object for the PR curves response for a run-tag combo.
Arguments:
runs: A list of runs to fetch the curves for.
tag: The tag to fetch the curves for.
Raises:
ValueError: If no PR curves could be fetched for a run and tag.
Retu... | def pr_curves_impl(self, runs, tag):
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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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train | PrCurvesPlugin.tags_impl | Creates the JSON object for the tags route response.
Returns:
The JSON object for the tags route response. | tensorboard/plugins/pr_curve/pr_curves_plugin.py | 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
... | 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()
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train | PrCurvesPlugin.available_time_entries_impl | Creates the JSON object for the available time entries route response.
Returns:
The JSON object for the available time entries route response. | tensorboard/plugins/pr_curve/pr_curves_plugin.py | 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 ... | 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:
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train | PrCurvesPlugin.is_active | 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. | tensorboard/plugins/pr_curve/pr_curves_plugin.py | 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... | 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:
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train | PrCurvesPlugin._process_tensor_event | 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
direction and inclusive of 0 and 1).
Returns:
A JSON-able dictionary of PR curve data for 1 step. | tensorboard/plugins/pr_curve/pr_curves_plugin.py | 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
direction and inclusive of 0 and 1).
Returns:
A ... | 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
direction and inclusive of 0 and 1).
Returns:
A ... | [
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train | PrCurvesPlugin._make_pr_entry | Creates an entry for PR curve data. Each entry corresponds to 1 step.
Args:
step: The step.
wall_time: The wall time.
data_array: A numpy array of PR curve data stored in the summary format.
thresholds: An array of floating point thresholds.
Returns:
A PR curve entry. | tensorboard/plugins/pr_curve/pr_curves_plugin.py | def _make_pr_entry(self, step, wall_time, data_array, thresholds):
"""Creates an entry for PR curve data. Each entry corresponds to 1 step.
Args:
step: The step.
wall_time: The wall time.
data_array: A numpy array of PR curve data stored in the summary format.
thresholds: An array of fl... | def _make_pr_entry(self, step, wall_time, data_array, thresholds):
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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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train | _normalize_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 `dict` whose keys are hyperparameter name... | tensorboard/plugins/hparams/api.py | 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... | 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... | [
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train | Experiment.summary_pb | Create a top-level experiment summary describing this experiment.
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Analogous to the low-level `experiment_pb` function in the
`hparams.summary` module. | tensorboard/plugins/hparams/api.py | def summary_pb(self):
"""Create a top-level experiment summary describing this experiment.
The resulting summary should be written to a log directory that
encloses all the individual sessions' log directories.
Analogous to the low-level `experiment_pb` function in the
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"""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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train | run_all | Generate a bunch of histogram data, and write it to logdir. | tensorboard/plugins/histogram/histograms_demo.py | def run_all(logdir, verbose=False, num_summaries=400):
"""Generate a bunch of histogram data, and write it to logdir."""
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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... | def run_all(logdir, verbose=False, num_summaries=400):
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train | _parse_positive_int_param | 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. | tensorboard/plugins/projector/projector_plugin.py | 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.
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train | EmbeddingMetadata.add_column | Adds a named column of metadata values.
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column_values: 1D array/list/iterable holding the column values. Must be
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"""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
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Raises:
... | 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
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train | ProjectorPlugin.is_active | Determines whether this plugin is active.
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Returns:
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Returns:
Whether any run has embedding data to show in the projector.
"""
if not self.multiplexer:
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if self._is_active:
# We have already det... | 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.
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train | ProjectorPlugin.configs | Returns a map of run paths to `ProjectorConfig` protos. | tensorboard/plugins/projector/projector_plugin.py | 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... | 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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train | EventMultiplexer.Reload | Call `Reload` on every `EventAccumulator`. | tensorboard/backend/event_processing/event_multiplexer.py | 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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with self._accumulators_mutex:
items = li... | def Reload(self):
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train | EventMultiplexer.Histograms | 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 available for
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... | tensorboard/backend/event_processing/event_multiplexer.py | 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... | 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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train | EventMultiplexer.CompressedHistograms | Retrieve the compressed histogram events associated with a run and tag.
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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 for
the giv... | tensorboard/backend/event_processing/event_multiplexer.py | 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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KeyError: If the run is not found... | def CompressedHistograms(self, run, tag):
"""Retrieve the compressed histogram events associated with a run and tag.
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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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train | EventMultiplexer.Images | Retrieve the image events associated with a run and tag.
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tag: A string name of the tag for which values are retrieved.
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Re... | tensorboard/backend/event_processing/event_multiplexer.py | def Images(self, run, tag):
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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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"""Retrieve the image events associated with a run and tag.
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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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train | histogram | Write a histogram summary.
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name: A name for this summary. The summary tag used for TensorBoard will
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data: A `Tensor` of any shape. Must be castable to `float64`.
step: Explicit `int64`-castable monotonic step value for this summary. If
... | tensorboard/plugins/histogram/summary_v2.py | def histogram(name, data, step=None, buckets=None, description=None):
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Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
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st... | 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
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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
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tag: String tag for the summary.
data: A `np.array` or array-like form of any shape. Must have type
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train | 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. | tensorboard/program.py | 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 ... | def setup_environment():
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This functions changes global state in the Python process. Calling
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"""
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train | 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 closed. The
paths inside the zip file are con... | tensorboard/program.py | 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
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"""Opens stock TensorBoard web assets collection.
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Returns function that returns a newly opened file handle to zip file
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train | TensorBoard.configure | Configures TensorBoard behavior via flags.
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train | TensorBoard.launch | Python API for launching TensorBoard.
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Returns:
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train | TensorBoard._register_info | Write a TensorBoardInfo file and arrange for its cleanup.
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train | TensorBoard._install_signal_handler | Set a signal handler to gracefully exit on the given signal.
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Args:
signal_number: The numeric code for the signal to handle, like
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Args:
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train | TensorBoard._make_server | Constructs the TensorBoard WSGI app and instantiates the server. | tensorboard/program.py | def _make_server(self):
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train | WerkzeugServer._get_wildcard_address | Returns a wildcard address for the port in question.
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train | WerkzeugServer.handle_error | Override to get rid of noisy EPIPE errors. | tensorboard/program.py | def handle_error(self, request, client_address):
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train | TraceEventsJsonStream._events | Iterator over all catapult trace events, as python values. | tensorboard/plugins/profile/trace_events_json.py | def _events(self):
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train | TraceEventsJsonStream._event | Converts a TraceEvent proto into a catapult trace event python value. | tensorboard/plugins/profile/trace_events_json.py | def _event(self, event):
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train | op | 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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display_name: Optional name for this summary in TensorBoard, as a
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descriptio... | tensorboard/plugins/scalar/summary.py | def op(name,
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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: ... | def op(name,
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train | pb | Create a legacy scalar summary protobuf.
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data: A rank-0 `np.array` or array-like form (so raw `int`s and
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train | run | 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 last argument.
outputs: List of fake o... | tensorboard/scripts/execrooter.py | def run(inputs, program, outputs):
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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
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train | initialize_schema | Initializes the TensorBoard sqlite schema using the given connection.
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"""Initializes the TensorBoard sqlite schema using the given connection.
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train | SqliteWriter._create_id | Returns a freshly created DB-wide unique ID. | tensorboard/backend/event_processing/sqlite_writer.py | def _create_id(self):
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train | SqliteWriter._maybe_init_user | Returns the ID for the current user, creating the row if needed. | tensorboard/backend/event_processing/sqlite_writer.py | 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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train | SqliteWriter._maybe_init_experiment | Returns the ID for the given experiment, creating the row if needed.
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experiment_name: name of experiment.
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train | SqliteWriter._maybe_init_run | Returns the ID for the given run, creating the row if needed.
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experiment_name: name of experiment containing this run.
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train | SqliteWriter._maybe_init_tags | Returns a tag-to-ID map for the given tags, creating rows if needed.
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run_id: the ID of the run to which these tags belong.
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run_id: the ID of the run to which these tags belong.
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run_id: the ID of the run to which these tags belong.
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train | SqliteWriter.write_summaries | Transactionally writes the given tagged summary data to the DB.
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tagged_data: map from tag to TagData instances.
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train | image_data | Get the raw encoded image data, downloading it if necessary. | tensorboard/plugins/image/images_demo.py | 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... | 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
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train | convolve | Perform a 2D pixel convolution on the given image.
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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.
pixel_filter: A 2D `Tensor`, representing pixel weightings for the... | tensorboard/plugins/image/images_demo.py | 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
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image: A 3D `float32` `Tensor` of shape `[height, width, channels]`,
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train | get_image | Get the image as a TensorFlow variable.
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invoke `sess.run(result.initializer)`. | tensorboard/plugins/image/images_demo.py | def get_image(verbose=False):
"""Get the image as a TensorFlow variable.
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A `tf.Variable`, which must be initialized prior to use:
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train | run_box_to_gaussian | 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. | tensorboard/plugins/image/images_demo.py | 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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logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output.
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train | run_sobel | Run a Sobel edge detection demonstration.
See the summary description for more details.
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logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output. | tensorboard/plugins/image/images_demo.py | def run_sobel(logdir, verbose=False):
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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.... | def run_sobel(logdir, verbose=False):
"""Run a Sobel edge detection demonstration.
See the summary description for more details.
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logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output.
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train | run_all | 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 | tensorboard/plugins/image/images_demo.py | 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
"""
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"""Run simulations on a reasonable set of parameters.
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logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
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train | proto_value_for_feature | Get the value of a feature from Example regardless of feature type. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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... | 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... | [
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train | parse_original_feature_from_example | 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. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | def parse_original_feature_from_example(example, feature_name):
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Args:
example: An example.
feature_name: A string feature name.
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example: An example.
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A filled in `OriginalFeatureList` object representing the feature.
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train | wrap_inference_results | Returns packaged inference results from the provided proto.
Args:
inference_result_proto: The classification or regression response proto.
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inference_result_proto: The classification or regression response proto.
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inference_result_proto: The classification or regression response proto.
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An InferenceResult proto with the result from the response.
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train | get_numeric_feature_names | 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. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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... | 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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train | get_categorical_feature_names | 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'] ) | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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... | 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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train | get_numeric_features_to_observed_range | 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. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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,
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"""
observed_featu... | 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,
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train | get_categorical_features_to_sampling | 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: Max number of samples to return per feature.
Returns:
A d... | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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: ... | 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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train | make_mutant_features | Return a list of `MutantFeatureValue`s that are variants of original. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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... | 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
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train | make_mutant_tuples | 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
mutate.
index_to_mutate: The index of the int64_list or float_list to mutate.
viz_params: A `VizParams` ... | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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
... | 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
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train | mutant_charts_for_feature | 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: One `ServingBundle` object per model, that contains the
information to make the serving request.
viz_params: A `Viz... | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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: ... | 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.
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train | make_json_formatted_for_single_chart | Returns JSON formatted for a single mutant chart.
Args:
mutant_features: An iterable of `MutantFeatureValue`s representing the
X-axis.
inference_result_proto: A ClassificationResponse or RegressionResponse
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It contains one 'classification' or 'regr... | tensorboard/plugins/interactive_inference/utils/inference_utils.py | def make_json_formatted_for_single_chart(mutant_features,
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index_to_mutate):
"""Returns JSON formatted for a single mutant chart.
Args:
mutant_features: An iterable of `MutantFeatureValue`s representing the... | def make_json_formatted_for_single_chart(mutant_features,
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train | get_example_features | Returns the non-sequence features from the provided example. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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) | def get_example_features(example):
"""Returns the non-sequence features from the provided example."""
return (example.features.feature if isinstance(example, tf.train.Example)
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train | run_inference_for_inference_results | Calls servo and wraps the inference results. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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... | 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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train | get_eligible_features | 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 make over each feature.
Returns:
A list wit... | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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... | 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.
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train | get_label_vocab | Returns a list of label strings loaded from the provided path. | tensorboard/plugins/interactive_inference/utils/inference_utils.py | 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... | 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:
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train | create_sprite_image | Returns an encoded sprite image for use in Facets Dive.
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examples: A list of serialized example protos to get images for.
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train | _GetDenseDimensions | Returns the inferred dense dimensions of a list of lists. | tensorboard/util/tensor_util.py | def _GetDenseDimensions(list_of_lists):
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train | make_tensor_proto | Create a TensorProto.
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shape: List of integers representing the dimensions of tensor.
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shape: List of integers representing the dimensions of tensor.
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train | make_ndarray | Create a numpy ndarray from a tensor.
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Args:
tensor: A TensorProto.
Returns:
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train | op | Creates a summary that contains a layout.
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train | pb | Creates a summary that contains a layout.
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train | Dimension.is_convertible_with | Returns true if `other` is convertible with this Dimension.
Two known Dimensions are convertible if they have the same value.
An unknown Dimension is convertible with all other Dimensions.
Args:
other: Another Dimension.
Returns:
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An unknown Dimension is convertible with all other Dimensions.
Args:
other: Another Dimension.
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train | Dimension.merge_with | Returns a Dimension that combines the information in `self` and `other`.
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train | TensorShape.ndims | Returns the rank of this shape, or None if it is unspecified. | tensorboard/compat/tensorflow_stub/tensor_shape.py | def ndims(self):
"""Returns the rank of this shape, or None if it is unspecified."""
if self._dims is None:
return None
else:
if self._ndims is None:
self._ndims = len(self._dims)
return self._ndims | def ndims(self):
"""Returns the rank of this shape, or None if it is unspecified."""
if self._dims is None:
return None
else:
if self._ndims is None:
self._ndims = len(self._dims)
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train | TensorShape.num_elements | Returns the total number of elements, or none for incomplete shapes. | tensorboard/compat/tensorflow_stub/tensor_shape.py | def num_elements(self):
"""Returns the total number of elements, or none for incomplete shapes."""
if self.is_fully_defined():
size = 1
for dim in self._dims:
size *= dim.value
return size
else:
return None | def num_elements(self):
"""Returns the total number of elements, or none for incomplete shapes."""
if self.is_fully_defined():
size = 1
for dim in self._dims:
size *= dim.value
return size
else:
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train | TensorShape.merge_with | Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` containin... | tensorboard/compat/tensorflow_stub/tensor_shape.py | def merge_with(self, other):
"""Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
Retu... | def merge_with(self, other):
"""Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
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train | TensorShape.concatenate | Returns the concatenation of the dimension in `self` and `other`.
*N.B.* If either `self` or `other` is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
information for use with slicing.
... | tensorboard/compat/tensorflow_stub/tensor_shape.py | def concatenate(self, other):
"""Returns the concatenation of the dimension in `self` and `other`.
*N.B.* If either `self` or `other` is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
... | def concatenate(self, other):
"""Returns the concatenation of the dimension in `self` and `other`.
*N.B.* If either `self` or `other` is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
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train | TensorShape.assert_same_rank | Raises an exception if `self` and `other` do not have convertible ranks.
Args:
other: Another `TensorShape`.
Raises:
ValueError: If `self` and `other` do not represent shapes with the
same rank. | tensorboard/compat/tensorflow_stub/tensor_shape.py | def assert_same_rank(self, other):
"""Raises an exception if `self` and `other` do not have convertible ranks.
Args:
other: Another `TensorShape`.
Raises:
ValueError: If `self` and `other` do not represent shapes with the
same rank.
"""
other = a... | def assert_same_rank(self, other):
"""Raises an exception if `self` and `other` do not have convertible ranks.
Args:
other: Another `TensorShape`.
Raises:
ValueError: If `self` and `other` do not represent shapes with the
same rank.
"""
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train | TensorShape.with_rank | Returns a shape based on `self` with the given rank.
This method promotes a completely unknown shape to one with a
known rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with the given rank.
Raises:
ValueError... | tensorboard/compat/tensorflow_stub/tensor_shape.py | def with_rank(self, rank):
"""Returns a shape based on `self` with the given rank.
This method promotes a completely unknown shape to one with a
known rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with the given rank.... | def with_rank(self, rank):
"""Returns a shape based on `self` with the given rank.
This method promotes a completely unknown shape to one with a
known rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with the given rank.... | [
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train | TensorShape.with_rank_at_least | Returns a shape based on `self` with at least the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at least the given
rank.
Raises:
ValueError: If `self` does not represent a shape with at least the given
... | tensorboard/compat/tensorflow_stub/tensor_shape.py | def with_rank_at_least(self, rank):
"""Returns a shape based on `self` with at least the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at least the given
rank.
Raises:
ValueError: If `self` does... | def with_rank_at_least(self, rank):
"""Returns a shape based on `self` with at least the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at least the given
rank.
Raises:
ValueError: If `self` does... | [
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train | TensorShape.with_rank_at_most | Returns a shape based on `self` with at most the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at most the given
rank.
Raises:
ValueError: If `self` does not represent a shape with at most the given
... | tensorboard/compat/tensorflow_stub/tensor_shape.py | def with_rank_at_most(self, rank):
"""Returns a shape based on `self` with at most the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at most the given
rank.
Raises:
ValueError: If `self` does no... | def with_rank_at_most(self, rank):
"""Returns a shape based on `self` with at most the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at most the given
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train | TensorShape.is_convertible_with | Returns True iff `self` is convertible with `other`.
Two possibly-partially-defined shapes are convertible if there
exists a fully-defined shape that both shapes can represent. Thus,
convertibility allows the shape inference code to reason about
partially-defined shapes. For example:
... | tensorboard/compat/tensorflow_stub/tensor_shape.py | def is_convertible_with(self, other):
"""Returns True iff `self` is convertible with `other`.
Two possibly-partially-defined shapes are convertible if there
exists a fully-defined shape that both shapes can represent. Thus,
convertibility allows the shape inference code to reason about
... | def is_convertible_with(self, other):
"""Returns True iff `self` is convertible with `other`.
Two possibly-partially-defined shapes are convertible if there
exists a fully-defined shape that both shapes can represent. Thus,
convertibility allows the shape inference code to reason about
... | [
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train | TensorShape.most_specific_convertible_shape | Returns the most specific TensorShape convertible with `self` and `other`.
* TensorShape([None, 1]) is the most specific TensorShape convertible with
both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
TensorShape(None) is also convertible with above mentioned TensorShapes.
... | tensorboard/compat/tensorflow_stub/tensor_shape.py | def most_specific_convertible_shape(self, other):
"""Returns the most specific TensorShape convertible with `self` and `other`.
* TensorShape([None, 1]) is the most specific TensorShape convertible with
both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
TensorShape(None) is... | def most_specific_convertible_shape(self, other):
"""Returns the most specific TensorShape convertible with `self` and `other`.
* TensorShape([None, 1]) is the most specific TensorShape convertible with
both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
TensorShape(None) is... | [
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train | TensorShape.is_fully_defined | Returns True iff `self` is fully defined in every dimension. | tensorboard/compat/tensorflow_stub/tensor_shape.py | def is_fully_defined(self):
"""Returns True iff `self` is fully defined in every dimension."""
return self._dims is not None and all(
dim.value is not None for dim in self._dims
) | def is_fully_defined(self):
"""Returns True iff `self` is fully defined in every dimension."""
return self._dims is not None and all(
dim.value is not None for dim in self._dims
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train | TensorShape.as_list | Returns a list of integers or `None` for each dimension.
Returns:
A list of integers or `None` for each dimension.
Raises:
ValueError: If `self` is an unknown shape with an unknown rank. | tensorboard/compat/tensorflow_stub/tensor_shape.py | def as_list(self):
"""Returns a list of integers or `None` for each dimension.
Returns:
A list of integers or `None` for each dimension.
Raises:
ValueError: If `self` is an unknown shape with an unknown rank.
"""
if self._dims is None:
raise Valu... | def as_list(self):
"""Returns a list of integers or `None` for each dimension.
Returns:
A list of integers or `None` for each dimension.
Raises:
ValueError: If `self` is an unknown shape with an unknown rank.
"""
if self._dims is None:
raise Valu... | [
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train | TensorShape.as_proto | Returns this shape as a `TensorShapeProto`. | tensorboard/compat/tensorflow_stub/tensor_shape.py | def as_proto(self):
"""Returns this shape as a `TensorShapeProto`."""
if self._dims is None:
return tensor_shape_pb2.TensorShapeProto(unknown_rank=True)
else:
return tensor_shape_pb2.TensorShapeProto(
dim=[
tensor_shape_pb2.TensorShapeP... | def as_proto(self):
"""Returns this shape as a `TensorShapeProto`."""
if self._dims is None:
return tensor_shape_pb2.TensorShapeProto(unknown_rank=True)
else:
return tensor_shape_pb2.TensorShapeProto(
dim=[
tensor_shape_pb2.TensorShapeP... | [
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