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31,900 | tensorflow/tensorboard | tensorboard/plugins/pr_curve/summary.py | raw_data_pb | def raw_data_pb(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None):
"""Create a PR curves summary protobuf from raw data values.
Args:
name: A tag a... | python | def raw_data_pb(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None):
"""Create a PR curves summary protobuf from raw data values.
Args:
name: A tag a... | [
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true_positive_counts: A rank-1 numpy array of true positive counts. Must
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31,901 | tensorflow/tensorboard | tensorboard/plugins/pr_curve/summary.py | _create_tensor_summary | def _create_tensor_summary(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None,
collections=None):
"""A private helper method for generating a tensor sum... | python | def _create_tensor_summary(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None,
collections=None):
"""A private helper method for generating a tensor sum... | [
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Arguments are the same as for raw_data_op.
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31,902 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_metric_evals.py | Handler.run | def run(self):
"""Executes the request.
Returns:
An array of tuples representing the metric evaluations--each of the form
(<wall time in secs>, <training step>, <metric value>).
"""
run, tag = metrics.run_tag_from_session_and_metric(
self._request.session_name, self._request.metri... | python | def run(self):
"""Executes the request.
Returns:
An array of tuples representing the metric evaluations--each of the form
(<wall time in secs>, <training step>, <metric value>).
"""
run, tag = metrics.run_tag_from_session_and_metric(
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31,903 | tensorflow/tensorboard | tensorboard/plugins/histogram/histograms_plugin.py | HistogramsPlugin.histograms_route | def histograms_route(self, request):
"""Given a tag and single run, return array of histogram values."""
tag = request.args.get('tag')
run = request.args.get('run')
try:
(body, mime_type) = self.histograms_impl(
tag, run, downsample_to=self.SAMPLE_SIZE)
code = 200
except ValueE... | python | def histograms_route(self, request):
"""Given a tag and single run, return array of histogram values."""
tag = request.args.get('tag')
run = request.args.get('run')
try:
(body, mime_type) = self.histograms_impl(
tag, run, downsample_to=self.SAMPLE_SIZE)
code = 200
except ValueE... | [
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31,904 | tensorflow/tensorboard | tensorboard/util/op_evaluator.py | PersistentOpEvaluator._lazily_initialize | def _lazily_initialize(self):
"""Initialize the graph and session, if this has not yet been done."""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
with self._initialization_lock:
if self._session:
return
graph = tf.Graph()
... | python | def _lazily_initialize(self):
"""Initialize the graph and session, if this has not yet been done."""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
with self._initialization_lock:
if self._session:
return
graph = tf.Graph()
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31,905 | tensorflow/tensorboard | tensorboard/plugins/custom_scalar/custom_scalars_plugin.py | CustomScalarsPlugin._get_scalars_plugin | def _get_scalars_plugin(self):
"""Tries to get the scalars plugin.
Returns:
The scalars plugin. Or None if it is not yet registered.
"""
if scalars_metadata.PLUGIN_NAME in self._plugin_name_to_instance:
# The plugin is registered.
return self._plugin_name_to_instance[scalars_metadata.... | python | def _get_scalars_plugin(self):
"""Tries to get the scalars plugin.
Returns:
The scalars plugin. Or None if it is not yet registered.
"""
if scalars_metadata.PLUGIN_NAME in self._plugin_name_to_instance:
# The plugin is registered.
return self._plugin_name_to_instance[scalars_metadata.... | [
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31,906 | tensorflow/tensorboard | tensorboard/plugins/custom_scalar/custom_scalars_plugin.py | CustomScalarsPlugin.is_active | def is_active(self):
"""This plugin is active if 2 conditions hold.
1. The scalars plugin is registered and active.
2. There is a custom layout for the dashboard.
Returns: A boolean. Whether the plugin is active.
"""
if not self._multiplexer:
return False
scalars_plugin_instance = s... | python | def is_active(self):
"""This plugin is active if 2 conditions hold.
1. The scalars plugin is registered and active.
2. There is a custom layout for the dashboard.
Returns: A boolean. Whether the plugin is active.
"""
if not self._multiplexer:
return False
scalars_plugin_instance = s... | [
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31,907 | tensorflow/tensorboard | tensorboard/plugins/custom_scalar/custom_scalars_plugin.py | CustomScalarsPlugin.download_data_impl | def download_data_impl(self, run, tag, response_format):
"""Provides a response for downloading scalars data for a data series.
Args:
run: The run.
tag: The specific tag.
response_format: A string. One of the values of the OutputFormat enum of
the scalar plugin.
Raises:
Val... | python | def download_data_impl(self, run, tag, response_format):
"""Provides a response for downloading scalars data for a data series.
Args:
run: The run.
tag: The specific tag.
response_format: A string. One of the values of the OutputFormat enum of
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31,908 | tensorflow/tensorboard | tensorboard/plugins/custom_scalar/custom_scalars_plugin.py | CustomScalarsPlugin.layout_route | def layout_route(self, request):
r"""Fetches the custom layout specified by the config file in the logdir.
If more than 1 run contains a layout, this method merges the layouts by
merging charts within individual categories. If 2 categories with the same
name are found, the charts within are merged. The... | python | def layout_route(self, request):
r"""Fetches the custom layout specified by the config file in the logdir.
If more than 1 run contains a layout, this method merges the layouts by
merging charts within individual categories. If 2 categories with the same
name are found, the charts within are merged. The... | [
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31,909 | tensorflow/tensorboard | tensorboard/plugins/text/text_plugin.py | make_table_row | def make_table_row(contents, tag='td'):
"""Given an iterable of string contents, make a table row.
Args:
contents: An iterable yielding strings.
tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
Returns:
A string containing the content strings, organized into a table row.
... | python | def make_table_row(contents, tag='td'):
"""Given an iterable of string contents, make a table row.
Args:
contents: An iterable yielding strings.
tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
Returns:
A string containing the content strings, organized into a table row.
... | [
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31,910 | tensorflow/tensorboard | tensorboard/plugins/text/text_plugin.py | make_table | def make_table(contents, headers=None):
"""Given a numpy ndarray of strings, concatenate them into a html table.
Args:
contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
table is laid out vertically (i.e. row-major).
headers: A np.ndarray or list of string header names for the ta... | python | def make_table(contents, headers=None):
"""Given a numpy ndarray of strings, concatenate them into a html table.
Args:
contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
table is laid out vertically (i.e. row-major).
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31,911 | tensorflow/tensorboard | tensorboard/plugins/text/text_plugin.py | reduce_to_2d | def reduce_to_2d(arr):
"""Given a np.npdarray with nDims > 2, reduce it to 2d.
It does this by selecting the zeroth coordinate for every dimension greater
than two.
Args:
arr: a numpy ndarray of dimension at least 2.
Returns:
A two-dimensional subarray from the input array.
Raises:
ValueErro... | python | def reduce_to_2d(arr):
"""Given a np.npdarray with nDims > 2, reduce it to 2d.
It does this by selecting the zeroth coordinate for every dimension greater
than two.
Args:
arr: a numpy ndarray of dimension at least 2.
Returns:
A two-dimensional subarray from the input array.
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31,912 | tensorflow/tensorboard | tensorboard/plugins/text/text_plugin.py | text_array_to_html | def text_array_to_html(text_arr):
"""Take a numpy.ndarray containing strings, and convert it into html.
If the ndarray contains a single scalar string, that string is converted to
html via our sanitized markdown parser. If it contains an array of strings,
the strings are individually converted to html and then... | python | def text_array_to_html(text_arr):
"""Take a numpy.ndarray containing strings, and convert it into html.
If the ndarray contains a single scalar string, that string is converted to
html via our sanitized markdown parser. If it contains an array of strings,
the strings are individually converted to html and then... | [
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31,913 | tensorflow/tensorboard | tensorboard/plugins/text/text_plugin.py | process_string_tensor_event | def process_string_tensor_event(event):
"""Convert a TensorEvent into a JSON-compatible response."""
string_arr = tensor_util.make_ndarray(event.tensor_proto)
html = text_array_to_html(string_arr)
return {
'wall_time': event.wall_time,
'step': event.step,
'text': html,
} | python | def process_string_tensor_event(event):
"""Convert a TensorEvent into a JSON-compatible response."""
string_arr = tensor_util.make_ndarray(event.tensor_proto)
html = text_array_to_html(string_arr)
return {
'wall_time': event.wall_time,
'step': event.step,
'text': html,
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31,914 | tensorflow/tensorboard | tensorboard/plugins/pr_curve/metadata.py | create_summary_metadata | def create_summary_metadata(display_name, description, num_thresholds):
"""Create a `summary_pb2.SummaryMetadata` proto for pr_curves plugin data.
Arguments:
display_name: The display name used in TensorBoard.
description: The description to show in TensorBoard.
num_thresholds: The number of thresholds... | python | def create_summary_metadata(display_name, description, num_thresholds):
"""Create a `summary_pb2.SummaryMetadata` proto for pr_curves plugin data.
Arguments:
display_name: The display name used in TensorBoard.
description: The description to show in TensorBoard.
num_thresholds: The number of thresholds... | [
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31,915 | tensorflow/tensorboard | tensorboard/plugins/pr_curve/metadata.py | parse_plugin_metadata | def parse_plugin_metadata(content):
"""Parse summary metadata to a Python object.
Arguments:
content: The `content` field of a `SummaryMetadata` proto
corresponding to the pr_curves plugin.
Returns:
A `PrCurvesPlugin` protobuf object.
"""
if not isinstance(content, bytes):
raise TypeError(... | python | def parse_plugin_metadata(content):
"""Parse summary metadata to a Python object.
Arguments:
content: The `content` field of a `SummaryMetadata` proto
corresponding to the pr_curves plugin.
Returns:
A `PrCurvesPlugin` protobuf object.
"""
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31,916 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | get_field_to_observations_map | def get_field_to_observations_map(generator, query_for_tag=''):
"""Return a field to `Observations` dict for the event generator.
Args:
generator: A generator over event protos.
query_for_tag: A string that if specified, only create observations for
events with this tag name.
Returns:
A dict m... | python | def get_field_to_observations_map(generator, query_for_tag=''):
"""Return a field to `Observations` dict for the event generator.
Args:
generator: A generator over event protos.
query_for_tag: A string that if specified, only create observations for
events with this tag name.
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31,917 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | get_unique_tags | def get_unique_tags(field_to_obs):
"""Returns a dictionary of tags that a user could query over.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict that maps keys in `TAG_FIELDS` to a list of string tags present in
the event files. If the dict does not have any ob... | python | def get_unique_tags(field_to_obs):
"""Returns a dictionary of tags that a user could query over.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict that maps keys in `TAG_FIELDS` to a list of string tags present in
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31,918 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | print_dict | def print_dict(d, show_missing=True):
"""Prints a shallow dict to console.
Args:
d: Dict to print.
show_missing: Whether to show keys with empty values.
"""
for k, v in sorted(d.items()):
if (not v) and show_missing:
# No instances of the key, so print missing symbol.
print('{} -'.forma... | python | def print_dict(d, show_missing=True):
"""Prints a shallow dict to console.
Args:
d: Dict to print.
show_missing: Whether to show keys with empty values.
"""
for k, v in sorted(d.items()):
if (not v) and show_missing:
# No instances of the key, so print missing symbol.
print('{} -'.forma... | [
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31,919 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | get_dict_to_print | def get_dict_to_print(field_to_obs):
"""Transform the field-to-obs mapping into a printable dictionary.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict with the keys and values to print to console.
"""
def compressed_steps(steps):
return {'num_steps': len(... | python | def get_dict_to_print(field_to_obs):
"""Transform the field-to-obs mapping into a printable dictionary.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict with the keys and values to print to console.
"""
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31,920 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | get_out_of_order | def get_out_of_order(list_of_numbers):
"""Returns elements that break the monotonically non-decreasing trend.
This is used to find instances of global step values that are "out-of-order",
which may trigger TensorBoard event discarding logic.
Args:
list_of_numbers: A list of numbers.
Returns:
A list... | python | def get_out_of_order(list_of_numbers):
"""Returns elements that break the monotonically non-decreasing trend.
This is used to find instances of global step values that are "out-of-order",
which may trigger TensorBoard event discarding logic.
Args:
list_of_numbers: A list of numbers.
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31,921 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | generators_from_logdir | def generators_from_logdir(logdir):
"""Returns a list of event generators for subdirectories with event files.
The number of generators returned should equal the number of directories
within logdir that contain event files. If only logdir contains event files,
returns a list of length one.
Args:
logdir:... | python | def generators_from_logdir(logdir):
"""Returns a list of event generators for subdirectories with event files.
The number of generators returned should equal the number of directories
within logdir that contain event files. If only logdir contains event files,
returns a list of length one.
Args:
logdir:... | [
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31,922 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | get_inspection_units | def get_inspection_units(logdir='', event_file='', tag=''):
"""Returns a list of InspectionUnit objects given either logdir or event_file.
If logdir is given, the number of InspectionUnits should equal the
number of directories or subdirectories that contain event files.
If event_file is given, the number of ... | python | def get_inspection_units(logdir='', event_file='', tag=''):
"""Returns a list of InspectionUnit objects given either logdir or event_file.
If logdir is given, the number of InspectionUnits should equal the
number of directories or subdirectories that contain event files.
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31,923 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_inspector.py | inspect | def inspect(logdir='', event_file='', tag=''):
"""Main function for inspector that prints out a digest of event files.
Args:
logdir: A log directory that contains event files.
event_file: Or, a particular event file path.
tag: An optional tag name to query for.
Raises:
ValueError: If neither log... | python | def inspect(logdir='', event_file='', tag=''):
"""Main function for inspector that prints out a digest of event files.
Args:
logdir: A log directory that contains event files.
event_file: Or, a particular event file path.
tag: An optional tag name to query for.
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31,924 | tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin_loader.py | DebuggerPluginLoader.load | def load(self, context):
"""Returns the debugger plugin, if possible.
Args:
context: The TBContext flags including `add_arguments`.
Returns:
A DebuggerPlugin instance or None if it couldn't be loaded.
"""
if not (context.flags.debugger_data_server_grpc_port > 0 or
context.f... | python | def load(self, context):
"""Returns the debugger plugin, if possible.
Args:
context: The TBContext flags including `add_arguments`.
Returns:
A DebuggerPlugin instance or None if it couldn't be loaded.
"""
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31,925 | tensorflow/tensorboard | tensorboard/plugins/hparams/metadata.py | create_summary_metadata | def create_summary_metadata(hparams_plugin_data_pb):
"""Returns a summary metadata for the HParams plugin.
Returns a summary_pb2.SummaryMetadata holding a copy of the given
HParamsPluginData message in its plugin_data.content field.
Sets the version field of the hparams_plugin_data_pb copy to
PLUGIN_DATA_VER... | python | def create_summary_metadata(hparams_plugin_data_pb):
"""Returns a summary metadata for the HParams plugin.
Returns a summary_pb2.SummaryMetadata holding a copy of the given
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Sets the version field of the hparams_plugin_data_pb copy to
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31,926 | tensorflow/tensorboard | tensorboard/plugins/hparams/metadata.py | _parse_plugin_data_as | def _parse_plugin_data_as(content, data_oneof_field):
"""Returns a data oneof's field from plugin_data.content.
Raises HParamsError if the content doesn't have 'data_oneof_field' set or
this file is incompatible with the version of the metadata stored.
Args:
content: The SummaryMetadata.plugin_data.conten... | python | def _parse_plugin_data_as(content, data_oneof_field):
"""Returns a data oneof's field from plugin_data.content.
Raises HParamsError if the content doesn't have 'data_oneof_field' set or
this file is incompatible with the version of the metadata stored.
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31,927 | tensorflow/tensorboard | tensorboard/plugins/debugger/events_writer_manager.py | EventsWriterManager.write_event | def write_event(self, event):
"""Writes an event proto to disk.
This method is threadsafe with respect to invocations of itself.
Args:
event: The event proto.
Raises:
IOError: If writing the event proto to disk fails.
"""
self._lock.acquire()
try:
self._events_writer.Wri... | python | def write_event(self, event):
"""Writes an event proto to disk.
This method is threadsafe with respect to invocations of itself.
Args:
event: The event proto.
Raises:
IOError: If writing the event proto to disk fails.
"""
self._lock.acquire()
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31,928 | tensorflow/tensorboard | tensorboard/plugins/debugger/events_writer_manager.py | EventsWriterManager.dispose | def dispose(self):
"""Disposes of this events writer manager, making it no longer usable.
Call this method when this object is done being used in order to clean up
resources and handlers. This method should ever only be called once.
"""
self._lock.acquire()
self._events_writer.Close()
self.... | python | def dispose(self):
"""Disposes of this events writer manager, making it no longer usable.
Call this method when this object is done being used in order to clean up
resources and handlers. This method should ever only be called once.
"""
self._lock.acquire()
self._events_writer.Close()
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31,929 | tensorflow/tensorboard | tensorboard/plugins/debugger/events_writer_manager.py | EventsWriterManager._create_events_writer | def _create_events_writer(self, directory):
"""Creates a new events writer.
Args:
directory: The directory in which to write files containing events.
Returns:
A new events writer, which corresponds to a new events file.
"""
total_size = 0
events_files = self._fetch_events_files_on_... | python | def _create_events_writer(self, directory):
"""Creates a new events writer.
Args:
directory: The directory in which to write files containing events.
Returns:
A new events writer, which corresponds to a new events file.
"""
total_size = 0
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31,930 | tensorflow/tensorboard | tensorboard/plugins/debugger/events_writer_manager.py | EventsWriterManager._fetch_events_files_on_disk | def _fetch_events_files_on_disk(self):
"""Obtains the names of debugger-related events files within the directory.
Returns:
The names of the debugger-related events files written to disk. The names
are sorted in increasing events file index.
"""
all_files = tf.io.gfile.listdir(self._events_... | python | def _fetch_events_files_on_disk(self):
"""Obtains the names of debugger-related events files within the directory.
Returns:
The names of the debugger-related events files written to disk. The names
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31,931 | tensorflow/tensorboard | tensorboard/summary/_tf/summary/__init__.py | reexport_tf_summary | def reexport_tf_summary():
"""Re-export all symbols from the original tf.summary.
This function finds the original tf.summary V2 API and re-exports all the
symbols from it within this module as well, so that when this module is
patched into the TF API namespace as the new tf.summary, the effect is an
overlay... | python | def reexport_tf_summary():
"""Re-export all symbols from the original tf.summary.
This function finds the original tf.summary V2 API and re-exports all the
symbols from it within this module as well, so that when this module is
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31,932 | tensorflow/tensorboard | tensorboard/encode_png_benchmark.py | bench | def bench(image, thread_count):
"""Encode `image` to PNG on `thread_count` threads in parallel.
Returns:
A `float` representing number of seconds that it takes all threads
to finish encoding `image`.
"""
threads = [threading.Thread(target=lambda: encoder.encode_png(image))
for _ in xrange(... | python | def bench(image, thread_count):
"""Encode `image` to PNG on `thread_count` threads in parallel.
Returns:
A `float` representing number of seconds that it takes all threads
to finish encoding `image`.
"""
threads = [threading.Thread(target=lambda: encoder.encode_png(image))
for _ in xrange(... | [
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31,933 | tensorflow/tensorboard | tensorboard/encode_png_benchmark.py | _image_of_size | def _image_of_size(image_size):
"""Generate a square RGB test image of the given side length."""
return np.random.uniform(0, 256, [image_size, image_size, 3]).astype(np.uint8) | python | def _image_of_size(image_size):
"""Generate a square RGB test image of the given side length."""
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31,934 | tensorflow/tensorboard | tensorboard/encode_png_benchmark.py | _format_line | def _format_line(headers, fields):
"""Format a line of a table.
Arguments:
headers: A list of strings that are used as the table headers.
fields: A list of the same length as `headers` where `fields[i]` is
the entry for `headers[i]` in this row. Elements can be of
arbitrary types. Pass `headers... | python | def _format_line(headers, fields):
"""Format a line of a table.
Arguments:
headers: A list of strings that are used as the table headers.
fields: A list of the same length as `headers` where `fields[i]` is
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31,935 | tensorflow/tensorboard | tensorboard/plugins/debugger/debug_graphs_helper.py | DebugGraphWrapper.get_gated_grpc_tensors | def get_gated_grpc_tensors(self, matching_debug_op=None):
"""Extract all nodes with gated-gRPC debug ops attached.
Uses cached values if available.
This method is thread-safe.
Args:
graph_def: A tf.GraphDef proto.
matching_debug_op: Return tensors and nodes with only matching the
s... | python | def get_gated_grpc_tensors(self, matching_debug_op=None):
"""Extract all nodes with gated-gRPC debug ops attached.
Uses cached values if available.
This method is thread-safe.
Args:
graph_def: A tf.GraphDef proto.
matching_debug_op: Return tensors and nodes with only matching the
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31,936 | tensorflow/tensorboard | tensorboard/plugins/debugger/debug_graphs_helper.py | DebugGraphWrapper.maybe_base_expanded_node_name | def maybe_base_expanded_node_name(self, node_name):
"""Expand the base name if there are node names nested under the node.
For example, if there are two nodes in the graph, "a" and "a/read", then
calling this function on "a" will give "a/(a)", a form that points at
a leaf node in the nested TensorBoard... | python | def maybe_base_expanded_node_name(self, node_name):
"""Expand the base name if there are node names nested under the node.
For example, if there are two nodes in the graph, "a" and "a/read", then
calling this function on "a" will give "a/(a)", a form that points at
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31,937 | tensorflow/tensorboard | tensorboard/backend/event_processing/db_import_multiplexer.py | DbImportMultiplexer.Reload | def Reload(self):
"""Load events from every detected run."""
logger.info('Beginning DbImportMultiplexer.Reload()')
# Defer event sink creation until needed; this ensures it will only exist in
# the thread that calls Reload(), since DB connections must be thread-local.
if not self._event_sink:
... | python | def Reload(self):
"""Load events from every detected run."""
logger.info('Beginning DbImportMultiplexer.Reload()')
# Defer event sink creation until needed; this ensures it will only exist in
# the thread that calls Reload(), since DB connections must be thread-local.
if not self._event_sink:
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31,938 | tensorflow/tensorboard | tensorboard/backend/event_processing/db_import_multiplexer.py | _RunLoader.load_batches | def load_batches(self):
"""Returns a batched event iterator over the run directory event files."""
event_iterator = self._directory_watcher.Load()
while True:
events = []
event_bytes = 0
start = time.time()
for event_proto in event_iterator:
events.append(event_proto)
... | python | def load_batches(self):
"""Returns a batched event iterator over the run directory event files."""
event_iterator = self._directory_watcher.Load()
while True:
events = []
event_bytes = 0
start = time.time()
for event_proto in event_iterator:
events.append(event_proto)
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31,939 | tensorflow/tensorboard | tensorboard/backend/event_processing/db_import_multiplexer.py | _SqliteWriterEventSink._process_event | def _process_event(self, event, tagged_data):
"""Processes a single tf.Event and records it in tagged_data."""
event_type = event.WhichOneof('what')
# Handle the most common case first.
if event_type == 'summary':
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... | python | def _process_event(self, event, tagged_data):
"""Processes a single tf.Event and records it in tagged_data."""
event_type = event.WhichOneof('what')
# Handle the most common case first.
if event_type == 'summary':
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31,940 | tensorflow/tensorboard | tensorboard/plugins/histogram/summary.py | _buckets | def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets.
Arguments:
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
Returns:
A `Tensor` of shape `[k, 3]` and type `float64`. T... | python | def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets.
Arguments:
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
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31,941 | tensorflow/tensorboard | tensorboard/plugins/histogram/summary.py | op | def op(name,
data,
bucket_count=None,
display_name=None,
description=None,
collections=None):
"""Create a legacy histogram summary op.
Arguments:
name: A unique name for the generated summary node.
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_c... | python | def op(name,
data,
bucket_count=None,
display_name=None,
description=None,
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name: A unique name for the generated summary node.
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31,942 | tensorflow/tensorboard | tensorboard/plugins/histogram/summary.py | pb | def pb(name, data, bucket_count=None, display_name=None, description=None):
"""Create a legacy histogram summary protobuf.
Arguments:
name: A unique name for the generated summary, including any desired
name scopes.
data: A `np.array` or array-like form of any shape. Must have type
castable to ... | python | def pb(name, data, bucket_count=None, display_name=None, description=None):
"""Create a legacy histogram summary protobuf.
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31,943 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_store.py | _WatchStore.add | def add(self, value):
"""Add a tensor the watch store."""
if self._disposed:
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self._data.append(value)
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"""Add a tensor the watch store."""
if self._disposed:
raise ValueError(
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31,944 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_store.py | _WatchStore.num_in_memory | def num_in_memory(self):
"""Get number of values in memory."""
n = len(self._data) - 1
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if isinstance(self._data[n], _TensorValueDiscarded):
break
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31,946 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_store.py | _WatchStore.query | def query(self, time_indices):
"""Query the values at given time indices.
Args:
time_indices: 0-based time indices to query, as a `list` of `int`.
Returns:
Values as a list of `numpy.ndarray` (for time indices in memory) or
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"""
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"""Query the values at given time indices.
Args:
time_indices: 0-based time indices to query, as a `list` of `int`.
Returns:
Values as a list of `numpy.ndarray` (for time indices in memory) or
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31,947 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_store.py | TensorStore.add | def add(self, watch_key, tensor_value):
"""Add a tensor value.
Args:
watch_key: A string representing the debugger tensor watch, e.g.,
'Dense_1/BiasAdd:0:DebugIdentity'.
tensor_value: The value of the tensor as a numpy.ndarray.
"""
if watch_key not in self._tensor_data:
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"""Add a tensor value.
Args:
watch_key: A string representing the debugger tensor watch, e.g.,
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tensor_value: The value of the tensor as a numpy.ndarray.
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31,948 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_store.py | TensorStore.query | def query(self,
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watch_key: The watch key to query.
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31,949 | tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._obtain_sampled_health_pills | def _obtain_sampled_health_pills(self, run, node_names):
"""Obtains the health pills for a run sampled by the event multiplexer.
This is much faster than the alternative path of reading health pills from
disk.
Args:
run: The run to fetch health pills for.
node_names: A list of node names f... | python | def _obtain_sampled_health_pills(self, run, node_names):
"""Obtains the health pills for a run sampled by the event multiplexer.
This is much faster than the alternative path of reading health pills from
disk.
Args:
run: The run to fetch health pills for.
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31,950 | tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._tensor_proto_to_health_pill | def _tensor_proto_to_health_pill(self, tensor_event, node_name, device,
output_slot):
"""Converts an event_accumulator.TensorEvent to a HealthPillEvent.
Args:
tensor_event: The event_accumulator.TensorEvent to convert.
node_name: The name of the node (without the ... | python | def _tensor_proto_to_health_pill(self, tensor_event, node_name, device,
output_slot):
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tensor_event: The event_accumulator.TensorEvent to convert.
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31,951 | tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._obtain_health_pills_at_step | def _obtain_health_pills_at_step(self, events_directory, node_names, step):
"""Reads disk to obtain the health pills for a run at a specific step.
This could be much slower than the alternative path of just returning all
health pills sampled by the event multiplexer. It could take tens of minutes
to co... | python | def _obtain_health_pills_at_step(self, events_directory, node_names, step):
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31,952 | tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._process_health_pill_event | def _process_health_pill_event(self, node_name_set, mapping, target_step,
file_path):
"""Creates health pills out of data in an event.
Creates health pills out of the event and adds them to the mapping.
Args:
node_name_set: A set of node names that are relevant.
... | python | def _process_health_pill_event(self, node_name_set, mapping, target_step,
file_path):
"""Creates health pills out of data in an event.
Creates health pills out of the event and adds them to the mapping.
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31,953 | tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._process_health_pill_value | def _process_health_pill_value(self,
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31,954 | tensorflow/tensorboard | tensorboard/manager.py | _info_to_string | def _info_to_string(info):
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The format returned by this function is opaque and should only be
interpreted by `_info_from_string`.
Args:
info: A valid `TensorBoardInfo` object.
Raises:
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"""Convert a `TensorBoardInfo` to string form to be stored on disk.
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Args:
info_string: A string representation of a `TensorBoardInfo`, as
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Returns:
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31,956 | tensorflow/tensorboard | tensorboard/manager.py | cache_key | def cache_key(working_directory, arguments, configure_kwargs):
"""Compute a `TensorBoardInfo.cache_key` field.
The format returned by this function is opaque. Clients may only
inspect it by comparing it for equality with other results from this
function.
Args:
working_directory: The directory from which... | python | def cache_key(working_directory, arguments, configure_kwargs):
"""Compute a `TensorBoardInfo.cache_key` field.
The format returned by this function is opaque. Clients may only
inspect it by comparing it for equality with other results from this
function.
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working_directory: The directory from which... | [
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31,957 | tensorflow/tensorboard | tensorboard/manager.py | _get_info_dir | def _get_info_dir():
"""Get path to directory in which to store info files.
The directory returned by this function is "owned" by this module. If
the contents of the directory are modified other than via the public
functions of this module, subsequent behavior is undefined.
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"""Get path to directory in which to store info files.
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31,958 | tensorflow/tensorboard | tensorboard/manager.py | write_info_file | def write_info_file(tensorboard_info):
"""Write TensorBoardInfo to the current process's info file.
This should be called by `main` once the server is ready. When the
server shuts down, `remove_info_file` should be called.
Args:
tensorboard_info: A valid `TensorBoardInfo` object.
Raises:
ValueError... | python | def write_info_file(tensorboard_info):
"""Write TensorBoardInfo to the current process's info file.
This should be called by `main` once the server is ready. When the
server shuts down, `remove_info_file` should be called.
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tensorboard_info: A valid `TensorBoardInfo` object.
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31,959 | tensorflow/tensorboard | tensorboard/manager.py | remove_info_file | def remove_info_file():
"""Remove the current process's TensorBoardInfo file, if it exists.
If the file does not exist, no action is taken and no error is raised.
"""
try:
os.unlink(_get_info_file_path())
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if e.errno == errno.ENOENT:
# The user may have wiped their temporary... | python | def remove_info_file():
"""Remove the current process's TensorBoardInfo file, if it exists.
If the file does not exist, no action is taken and no error is raised.
"""
try:
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31,960 | tensorflow/tensorboard | tensorboard/manager.py | get_all | def get_all():
"""Return TensorBoardInfo values for running TensorBoard processes.
This function may not provide a perfect snapshot of the set of running
processes. Its result set may be incomplete if the user has cleaned
their /tmp/ directory while TensorBoard processes are running. It may
contain extraneou... | python | def get_all():
"""Return TensorBoardInfo values for running TensorBoard processes.
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31,961 | tensorflow/tensorboard | tensorboard/manager.py | start | def start(arguments, timeout=datetime.timedelta(seconds=60)):
"""Start a new TensorBoard instance, or reuse a compatible one.
If the cache key determined by the provided arguments and the current
working directory (see `cache_key`) matches the cache key of a running
TensorBoard process (see `get_all`), that pr... | python | def start(arguments, timeout=datetime.timedelta(seconds=60)):
"""Start a new TensorBoard instance, or reuse a compatible one.
If the cache key determined by the provided arguments and the current
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31,962 | tensorflow/tensorboard | tensorboard/manager.py | _find_matching_instance | def _find_matching_instance(cache_key):
"""Find a running TensorBoard instance compatible with the cache key.
Returns:
A `TensorBoardInfo` object, or `None` if none matches the cache key.
"""
infos = get_all()
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for candidate in sorted(... | python | def _find_matching_instance(cache_key):
"""Find a running TensorBoard instance compatible with the cache key.
Returns:
A `TensorBoardInfo` object, or `None` if none matches the cache key.
"""
infos = get_all()
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31,963 | tensorflow/tensorboard | tensorboard/manager.py | _maybe_read_file | def _maybe_read_file(filename):
"""Read the given file, if it exists.
Args:
filename: A path to a file.
Returns:
A string containing the file contents, or `None` if the file does
not exist.
"""
try:
with open(filename) as infile:
return infile.read()
except IOError as e:
if e.err... | python | def _maybe_read_file(filename):
"""Read the given file, if it exists.
Args:
filename: A path to a file.
Returns:
A string containing the file contents, or `None` if the file does
not exist.
"""
try:
with open(filename) as infile:
return infile.read()
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31,964 | tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | process_raw_trace | def process_raw_trace(raw_trace):
"""Processes raw trace data and returns the UI data."""
trace = trace_events_pb2.Trace()
trace.ParseFromString(raw_trace)
return ''.join(trace_events_json.TraceEventsJsonStream(trace)) | python | def process_raw_trace(raw_trace):
"""Processes raw trace data and returns the UI data."""
trace = trace_events_pb2.Trace()
trace.ParseFromString(raw_trace)
return ''.join(trace_events_json.TraceEventsJsonStream(trace)) | [
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31,965 | tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.is_active | def is_active(self):
"""Whether this plugin is active and has any profile data to show.
Detecting profile data is expensive, so this process runs asynchronously
and the value reported by this method is the cached value and may be stale.
Returns:
Whether any run has profile data.
"""
# If... | python | def is_active(self):
"""Whether this plugin is active and has any profile data to show.
Detecting profile data is expensive, so this process runs asynchronously
and the value reported by this method is the cached value and may be stale.
Returns:
Whether any run has profile data.
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31,966 | tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin._run_dir | def _run_dir(self, run):
"""Helper that maps a frontend run name to a profile "run" directory.
The frontend run name consists of the TensorBoard run name (aka the relative
path from the logdir root to the directory containing the data) path-joined
to the Profile plugin's "run" concept (which is a subdi... | python | def _run_dir(self, run):
"""Helper that maps a frontend run name to a profile "run" directory.
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path from the logdir root to the directory containing the data) path-joined
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31,967 | tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.generate_run_to_tools | def generate_run_to_tools(self):
"""Generator for pairs of "run name" and a list of tools for that run.
The "run name" here is a "frontend run name" - see _run_dir() for the
definition of a "frontend run name" and how it maps to a directory of
profile data for a specific profile "run". The profile plug... | python | def generate_run_to_tools(self):
"""Generator for pairs of "run name" and a list of tools for that run.
The "run name" here is a "frontend run name" - see _run_dir() for the
definition of a "frontend run name" and how it maps to a directory of
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31,968 | tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.host_impl | def host_impl(self, run, tool):
"""Returns available hosts for the run and tool in the log directory.
In the plugin log directory, each directory contains profile data for a
single run (identified by the directory name), and files in the run
directory contains data for different tools and hosts. The fi... | python | def host_impl(self, run, tool):
"""Returns available hosts for the run and tool in the log directory.
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31,969 | tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.data_impl | def data_impl(self, request):
"""Retrieves and processes the tool data for a run and a host.
Args:
request: XMLHttpRequest
Returns:
A string that can be served to the frontend tool or None if tool,
run or host is invalid.
"""
run = request.args.get('run')
tool = request.arg... | python | def data_impl(self, request):
"""Retrieves and processes the tool data for a run and a host.
Args:
request: XMLHttpRequest
Returns:
A string that can be served to the frontend tool or None if tool,
run or host is invalid.
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31,970 | tensorflow/tensorboard | tensorboard/plugins/scalar/scalars_demo.py | run | def run(logdir, run_name,
initial_temperature, ambient_temperature, heat_coefficient):
"""Run a temperature simulation.
This will simulate an object at temperature `initial_temperature`
sitting at rest in a large room at temperature `ambient_temperature`.
The object has some intrinsic `heat_coefficient... | python | def run(logdir, run_name,
initial_temperature, ambient_temperature, heat_coefficient):
"""Run a temperature simulation.
This will simulate an object at temperature `initial_temperature`
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31,971 | tensorflow/tensorboard | tensorboard/backend/json_util.py | Cleanse | def Cleanse(obj, encoding='utf-8'):
"""Makes Python object appropriate for JSON serialization.
- Replaces instances of Infinity/-Infinity/NaN with strings.
- Turns byte strings into unicode strings.
- Turns sets into sorted lists.
- Turns tuples into lists.
Args:
obj: Python data structure.
encodi... | python | def Cleanse(obj, encoding='utf-8'):
"""Makes Python object appropriate for JSON serialization.
- Replaces instances of Infinity/-Infinity/NaN with strings.
- Turns byte strings into unicode strings.
- Turns sets into sorted lists.
- Turns tuples into lists.
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31,972 | tensorflow/tensorboard | tensorboard/plugins/text/summary.py | op | def op(name,
data,
display_name=None,
description=None,
collections=None):
"""Create a legacy text summary op.
Text data summarized via this plugin will be visible in the Text Dashboard
in TensorBoard. The standard TensorBoard Text Dashboard will render markdown
in the strings, and ... | python | def op(name,
data,
display_name=None,
description=None,
collections=None):
"""Create a legacy text summary op.
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31,973 | tensorflow/tensorboard | tensorboard/plugins/text/summary.py | pb | def pb(name, data, display_name=None, description=None):
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name: A name for the generated node. Will also serve as a series name in
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data: A Python bytestring (of type bytes), or Unicode string. Or a numpy
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"""Create a legacy text summary protobuf.
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31,974 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | _GeneratorFromPath | def _GeneratorFromPath(path):
"""Create an event generator for file or directory at given path string."""
if not path:
raise ValueError('path must be a valid string')
if io_wrapper.IsTensorFlowEventsFile(path):
return event_file_loader.EventFileLoader(path)
else:
return directory_watcher.DirectoryWa... | python | def _GeneratorFromPath(path):
"""Create an event generator for file or directory at given path string."""
if not path:
raise ValueError('path must be a valid string')
if io_wrapper.IsTensorFlowEventsFile(path):
return event_file_loader.EventFileLoader(path)
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31,975 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | _ParseFileVersion | def _ParseFileVersion(file_version):
"""Convert the string file_version in event.proto into a float.
Args:
file_version: String file_version from event.proto
Returns:
Version number as a float.
"""
tokens = file_version.split('brain.Event:')
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... | python | def _ParseFileVersion(file_version):
"""Convert the string file_version in event.proto into a float.
Args:
file_version: String file_version from event.proto
Returns:
Version number as a float.
"""
tokens = file_version.split('brain.Event:')
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31,976 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.Reload | def Reload(self):
"""Loads all events added since the last call to `Reload`.
If `Reload` was never called, loads all events in the file.
Returns:
The `EventAccumulator`.
"""
with self._generator_mutex:
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"""Loads all events added since the last call to `Reload`.
If `Reload` was never called, loads all events in the file.
Returns:
The `EventAccumulator`.
"""
with self._generator_mutex:
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31,977 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.RetrievePluginAsset | def RetrievePluginAsset(self, plugin_name, asset_name):
"""Return the contents of a given plugin asset.
Args:
plugin_name: The string name of a plugin.
asset_name: The string name of an asset.
Returns:
The string contents of the plugin asset.
Raises:
KeyError: If the asset is ... | python | def RetrievePluginAsset(self, plugin_name, asset_name):
"""Return the contents of a given plugin asset.
Args:
plugin_name: The string name of a plugin.
asset_name: The string name of an asset.
Returns:
The string contents of the plugin asset.
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31,978 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.FirstEventTimestamp | def FirstEventTimestamp(self):
"""Returns the timestamp in seconds of the first event.
If the first event has been loaded (either by this method or by `Reload`,
this returns immediately. Otherwise, it will load in the first event. Note
that this means that calling `Reload` will cause this to block unti... | python | def FirstEventTimestamp(self):
"""Returns the timestamp in seconds of the first event.
If the first event has been loaded (either by this method or by `Reload`,
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31,979 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.Graph | def Graph(self):
"""Return the graph definition, if there is one.
If the graph is stored directly, return that. If no graph is stored
directly but a metagraph is stored containing a graph, return that.
Raises:
ValueError: If there is no graph for this run.
Returns:
The `graph_def` pr... | python | def Graph(self):
"""Return the graph definition, if there is one.
If the graph is stored directly, return that. If no graph is stored
directly but a metagraph is stored containing a graph, return that.
Raises:
ValueError: If there is no graph for this run.
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31,980 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.MetaGraph | def MetaGraph(self):
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Raises:
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Returns:
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"""
if self._meta_graph is None:
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meta_g... | python | def MetaGraph(self):
"""Return the metagraph definition, if there is one.
Raises:
ValueError: If there is no metagraph for this run.
Returns:
The `meta_graph_def` proto.
"""
if self._meta_graph is None:
raise ValueError('There is no metagraph in this EventAccumulator')
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31,981 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._CheckForRestartAndMaybePurge | def _CheckForRestartAndMaybePurge(self, event):
"""Check and discard expired events using SessionLog.START.
Check for a SessionLog.START event and purge all previously seen events
with larger steps, because they are out of date. Because of supervisor
threading, it is possible that this logic will cause... | python | def _CheckForRestartAndMaybePurge(self, event):
"""Check and discard expired events using SessionLog.START.
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31,982 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessHistogram | def _ProcessHistogram(self, tag, wall_time, step, histo):
"""Processes a proto histogram by adding it to accumulated state."""
histo = self._ConvertHistogramProtoToTuple(histo)
histo_ev = HistogramEvent(wall_time, step, histo)
self.histograms.AddItem(tag, histo_ev)
self.compressed_histograms.AddItem... | python | def _ProcessHistogram(self, tag, wall_time, step, histo):
"""Processes a proto histogram by adding it to accumulated state."""
histo = self._ConvertHistogramProtoToTuple(histo)
histo_ev = HistogramEvent(wall_time, step, histo)
self.histograms.AddItem(tag, histo_ev)
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31,983 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._CompressHistogram | def _CompressHistogram(self, histo_ev):
"""Callback for _ProcessHistogram."""
return CompressedHistogramEvent(
histo_ev.wall_time,
histo_ev.step,
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"""Callback for _ProcessHistogram."""
return CompressedHistogramEvent(
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31,984 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessImage | def _ProcessImage(self, tag, wall_time, step, image):
"""Processes an image by adding it to accumulated state."""
event = ImageEvent(wall_time=wall_time,
step=step,
encoded_image_string=image.encoded_image_string,
width=image.width,
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"""Processes an image by adding it to accumulated state."""
event = ImageEvent(wall_time=wall_time,
step=step,
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width=image.width,
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31,985 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessAudio | def _ProcessAudio(self, tag, wall_time, step, audio):
"""Processes a audio by adding it to accumulated state."""
event = AudioEvent(wall_time=wall_time,
step=step,
encoded_audio_string=audio.encoded_audio_string,
content_type=audio.content_typ... | python | def _ProcessAudio(self, tag, wall_time, step, audio):
"""Processes a audio by adding it to accumulated state."""
event = AudioEvent(wall_time=wall_time,
step=step,
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31,986 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessScalar | def _ProcessScalar(self, tag, wall_time, step, scalar):
"""Processes a simple value by adding it to accumulated state."""
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self.scalars.AddItem(tag, sv) | python | def _ProcessScalar(self, tag, wall_time, step, scalar):
"""Processes a simple value by adding it to accumulated state."""
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31,987 | tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_loader.py | RawEventFileLoader.Load | def Load(self):
"""Loads all new events from disk as raw serialized proto bytestrings.
Calling Load multiple times in a row will not 'drop' events as long as the
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Yields:
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"""
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"""Loads all new events from disk as raw serialized proto bytestrings.
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Yields:
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31,989 | tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_server_lib.py | DebuggerDataStreamHandler._parse_session_run_index | def _parse_session_run_index(self, event):
"""Parses the session_run_index value from the event proto.
Args:
event: The event with metadata that contains the session_run_index.
Returns:
The int session_run_index value. Or
constants.SENTINEL_FOR_UNDETERMINED_STEP if it could not be determ... | python | def _parse_session_run_index(self, event):
"""Parses the session_run_index value from the event proto.
Args:
event: The event with metadata that contains the session_run_index.
Returns:
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31,990 | tensorflow/tensorboard | tensorboard/plugins/image/images_plugin.py | ImagesPlugin._serve_image_metadata | def _serve_image_metadata(self, request):
"""Given a tag and list of runs, serve a list of metadata for images.
Note that the images themselves are not sent; instead, we respond with URLs
to the images. The frontend should treat these URLs as opaque and should not
try to parse information about them or... | python | def _serve_image_metadata(self, request):
"""Given a tag and list of runs, serve a list of metadata for images.
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31,991 | tensorflow/tensorboard | tensorboard/plugins/image/images_plugin.py | ImagesPlugin._serve_individual_image | def _serve_individual_image(self, request):
"""Serves an individual image."""
run = request.args.get('run')
tag = request.args.get('tag')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
data = self._get_individual_image(run, tag, index, sample)
image_type =... | python | def _serve_individual_image(self, request):
"""Serves an individual image."""
run = request.args.get('run')
tag = request.args.get('tag')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
data = self._get_individual_image(run, tag, index, sample)
image_type =... | [
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31,992 | tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curve_demo.py | run_all | def run_all(logdir, steps, thresholds, verbose=False):
"""Generate PR curve summaries.
Arguments:
logdir: The directory into which to store all the runs' data.
steps: The number of steps to run for.
verbose: Whether to print the names of runs into stdout during execution.
thresholds: The number of ... | python | def run_all(logdir, steps, thresholds, verbose=False):
"""Generate PR curve summaries.
Arguments:
logdir: The directory into which to store all the runs' data.
steps: The number of steps to run for.
verbose: Whether to print the names of runs into stdout during execution.
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31,993 | tensorflow/tensorboard | tensorboard/plugins/image/summary_v2.py | image | def image(name,
data,
step=None,
max_outputs=3,
description=None):
"""Write an image 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` representing pixe... | python | def image(name,
data,
step=None,
max_outputs=3,
description=None):
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name: A name for this summary. The summary tag used for TensorBoard will
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31,994 | tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_examples | def set_examples(self, examples):
"""Sets the examples to be displayed in WIT.
Args:
examples: List of example protos.
Returns:
self, in order to enabled method chaining.
"""
self.store('examples', examples)
if len(examples) > 0:
self.store('are_sequence_examples',
... | python | def set_examples(self, examples):
"""Sets the examples to be displayed in WIT.
Args:
examples: List of example protos.
Returns:
self, in order to enabled method chaining.
"""
self.store('examples', examples)
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31,995 | tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_estimator_and_feature_spec | def set_estimator_and_feature_spec(self, estimator, feature_spec):
"""Sets the model for inference as a TF Estimator.
Instead of using TF Serving to host a model for WIT to query, WIT can
directly use a TF Estimator object as the model to query. In order to
accomplish this, a feature_spec must also be ... | python | def set_estimator_and_feature_spec(self, estimator, feature_spec):
"""Sets the model for inference as a TF Estimator.
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31,996 | tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_compare_estimator_and_feature_spec | def set_compare_estimator_and_feature_spec(self, estimator, feature_spec):
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If you wish to compare the results of two models in WIT, use this method
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31,997 | tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_custom_predict_fn | def set_custom_predict_fn(self, predict_fn):
"""Sets a custom function for inference.
Instead of using TF Serving to host a model for WIT to query, WIT can
directly use a custom function as the model to query. In this case, the
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- For clas... | python | def set_custom_predict_fn(self, predict_fn):
"""Sets a custom function for inference.
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31,998 | tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_compare_custom_predict_fn | def set_compare_custom_predict_fn(self, predict_fn):
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31,999 | tensorflow/tensorboard | tensorboard/plugins/beholder/visualizer.py | Visualizer._sections_to_variance_sections | def _sections_to_variance_sections(self, sections_over_time):
'''Computes the variance of corresponding sections over time.
Returns:
a list of np arrays.
'''
variance_sections = []
for i in range(len(sections_over_time[0])):
time_sections = [sections[i] for sections in sections_over_ti... | python | def _sections_to_variance_sections(self, sections_over_time):
'''Computes the variance of corresponding sections over time.
Returns:
a list of np arrays.
'''
variance_sections = []
for i in range(len(sections_over_time[0])):
time_sections = [sections[i] for sections in sections_over_ti... | [
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Returns:
a list of np arrays. | [
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