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tensorflow/tensorboard | tensorboard/backend/event_processing/reservoir.py | Reservoir.AddItem | def AddItem(self, key, item, f=lambda x: x):
"""Add a new item to the Reservoir with the given tag.
If the reservoir has not yet reached full size, the new item is guaranteed
to be added. If the reservoir is full, then behavior depends on the
always_keep_last boolean.
If always_keep_last was set t... | python | def AddItem(self, key, item, f=lambda x: x):
"""Add a new item to the Reservoir with the given tag.
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tensorflow/tensorboard | tensorboard/backend/event_processing/reservoir.py | Reservoir.FilterItems | def FilterItems(self, filterFn, key=None):
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tensorflow/tensorboard | tensorboard/backend/event_processing/reservoir.py | _ReservoirBucket.AddItem | def AddItem(self, item, f=lambda x: x):
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tensorflow/tensorboard | tensorboard/backend/event_processing/reservoir.py | _ReservoirBucket.FilterItems | def FilterItems(self, filterFn):
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tensorflow/tensorboard | tensorboard/util/tensor_util.py | _GetDenseDimensions | def _GetDenseDimensions(list_of_lists):
"""Returns the inferred dense dimensions of a list of lists."""
if not isinstance(list_of_lists, (list, tuple)):
return []
elif not list_of_lists:
return [0]
else:
return [len(list_of_lists)] + _GetDenseDimensions(list_of_lists[0]) | python | def _GetDenseDimensions(list_of_lists):
"""Returns the inferred dense dimensions of a list of lists."""
if not isinstance(list_of_lists, (list, tuple)):
return []
elif not list_of_lists:
return [0]
else:
return [len(list_of_lists)] + _GetDenseDimensions(list_of_lists[0]) | [
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tensorflow/tensorboard | tensorboard/util/tensor_util.py | make_tensor_proto | def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False):
"""Create a TensorProto.
Args:
values: Values to put in the TensorProto.
dtype: Optional tensor_pb2 DataType value.
shape: List of integers representing the dimensions of tensor.
verify_shape: B... | python | def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False):
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Args:
values: Values to put in the TensorProto.
dtype: Optional tensor_pb2 DataType value.
shape: List of integers representing the dimensions of tensor.
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tensorflow/tensorboard | tensorboard/util/tensor_util.py | make_ndarray | def make_ndarray(tensor):
"""Create a numpy ndarray from a tensor.
Create a numpy ndarray with the same shape and data as the tensor.
Args:
tensor: A TensorProto.
Returns:
A numpy array with the tensor contents.
Raises:
TypeError: if tensor has unsupported type.
"""
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"""Create a numpy ndarray from a tensor.
Create a numpy ndarray with the same shape and data as the tensor.
Args:
tensor: A TensorProto.
Returns:
A numpy array with the tensor contents.
Raises:
TypeError: if tensor has unsupported type.
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tensorflow/tensorboard | tensorboard/plugins/custom_scalar/summary.py | op | def op(scalars_layout, collections=None):
"""Creates a summary that contains a layout.
When users navigate to the custom scalars dashboard, they will see a layout
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Args:
scalars_layout: The scalars_layout_pb2.Layout proto that specifies the
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... | python | def op(scalars_layout, collections=None):
"""Creates a summary that contains a layout.
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scalars_layout: The scalars_layout_pb2.Layout proto that specifies the
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tensorflow/tensorboard | tensorboard/plugins/custom_scalar/summary.py | pb | def pb(scalars_layout):
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | Dimension.is_convertible_with | def is_convertible_with(self, other):
"""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.
Retur... | python | def is_convertible_with(self, other):
"""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.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | Dimension.merge_with | def merge_with(self, other):
"""Returns a Dimension that combines the information in `self` and `other`.
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```python
tf.Dimension(n) .merge_with(tf.Dimension(n)) == tf.Dimension(n)
tf.Dimension(n) .merge_with(tf.Dimension(None)) == tf.Di... | python | def merge_with(self, other):
"""Returns a Dimension that combines the information in `self` and `other`.
Dimensions are combined as follows:
```python
tf.Dimension(n) .merge_with(tf.Dimension(n)) == tf.Dimension(n)
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.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)
return self._ndims | python | 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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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.num_elements | 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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"""Returns the total number of elements, or none for incomplete shapes."""
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size = 1
for dim in self._dims:
size *= dim.value
return size
else:
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.merge_with | def merge_with(self, other):
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Retu... | python | def merge_with(self, other):
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.concatenate | 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
... | python | def concatenate(self, other):
"""Returns the concatenation of the dimension in `self` and `other`.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.assert_same_rank | 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
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"""
other = a... | python | def assert_same_rank(self, other):
"""Raises an exception if `self` and `other` do not have convertible ranks.
Args:
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.with_rank | def with_rank(self, rank):
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rank: An integer.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.with_rank_at_least | def with_rank_at_least(self, rank):
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Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at least the given
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ValueError: If `self` does... | python | def with_rank_at_least(self, rank):
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rank: An integer.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.with_rank_at_most | def with_rank_at_most(self, rank):
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rank: An integer.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.is_convertible_with | 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
... | python | def is_convertible_with(self, other):
"""Returns True iff `self` is convertible with `other`.
Two possibly-partially-defined shapes are convertible if there
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.most_specific_convertible_shape | 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... | python | def most_specific_convertible_shape(self, other):
"""Returns the most specific TensorShape convertible with `self` and `other`.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.is_fully_defined | 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
) | python | def is_fully_defined(self):
"""Returns True iff `self` is fully defined in every dimension."""
return self._dims is not None and all(
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.as_list | 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... | python | 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.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.as_proto | def as_proto(self):
"""Returns this shape as a `TensorShapeProto`."""
if self._dims is None:
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else:
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dim=[
tensor_shape_pb2.TensorShapeP... | python | def as_proto(self):
"""Returns this shape as a `TensorShapeProto`."""
if self._dims is None:
return tensor_shape_pb2.TensorShapeProto(unknown_rank=True)
else:
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/common_utils.py | convert_predict_response | def convert_predict_response(pred, serving_bundle):
"""Converts a PredictResponse to ClassificationResponse or RegressionResponse.
Args:
pred: PredictResponse to convert.
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/common_utils.py | convert_prediction_values | def convert_prediction_values(values, serving_bundle, model_spec=None):
"""Converts tensor values into ClassificationResponse or RegressionResponse.
Args:
values: For classification, a 2D list of numbers. The first dimension is for
each example being predicted. The second dimension are the probabilities
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tensorflow/tensorboard | tensorboard/backend/event_processing/plugin_event_accumulator.py | _GetPurgeMessage | def _GetPurgeMessage(most_recent_step, most_recent_wall_time, event_step,
event_wall_time, num_expired):
"""Return the string message associated with TensorBoard purges."""
return ('Detected out of order event.step likely caused by a TensorFlow '
'restart. Purging {} expired tensor ev... | python | def _GetPurgeMessage(most_recent_step, most_recent_wall_time, event_step,
event_wall_time, num_expired):
"""Return the string message associated with TensorBoard purges."""
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tensorflow/tensorboard | tensorboard/backend/event_processing/plugin_event_accumulator.py | EventAccumulator.PluginTagToContent | def PluginTagToContent(self, plugin_name):
"""Returns a dict mapping tags to content specific to that plugin.
Args:
plugin_name: The name of the plugin for which to fetch plugin-specific
content.
Raises:
KeyError: if the plugin name is not found.
Returns:
A dict mapping tags... | python | def PluginTagToContent(self, plugin_name):
"""Returns a dict mapping tags to content specific to that plugin.
Args:
plugin_name: The name of the plugin for which to fetch plugin-specific
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Raises:
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tensorflow/tensorboard | tensorboard/backend/event_processing/plugin_event_accumulator.py | EventAccumulator._ProcessEvent | def _ProcessEvent(self, event):
"""Called whenever an event is loaded."""
if self._first_event_timestamp is None:
self._first_event_timestamp = event.wall_time
if event.HasField('file_version'):
new_file_version = _ParseFileVersion(event.file_version)
if self.file_version and self.file_ve... | python | def _ProcessEvent(self, event):
"""Called whenever an event is loaded."""
if self._first_event_timestamp is None:
self._first_event_timestamp = event.wall_time
if event.HasField('file_version'):
new_file_version = _ParseFileVersion(event.file_version)
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tensorflow/tensorboard | tensorboard/backend/event_processing/plugin_event_accumulator.py | EventAccumulator.Tags | def Tags(self):
"""Return all tags found in the value stream.
Returns:
A `{tagType: ['list', 'of', 'tags']}` dictionary.
"""
return {
TENSORS: list(self.tensors_by_tag.keys()),
# Use a heuristic: if the metagraph is available, but
# graph is not, then we assume the metagra... | python | def Tags(self):
"""Return all tags found in the value stream.
Returns:
A `{tagType: ['list', 'of', 'tags']}` dictionary.
"""
return {
TENSORS: list(self.tensors_by_tag.keys()),
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tensorflow/tensorboard | tensorboard/backend/event_processing/plugin_event_accumulator.py | EventAccumulator._MaybePurgeOrphanedData | def _MaybePurgeOrphanedData(self, event):
"""Maybe purge orphaned data due to a TensorFlow crash.
When TensorFlow crashes at step T+O and restarts at step T, any events
written after step T are now "orphaned" and will be at best misleading if
they are included in TensorBoard.
This logic attempts t... | python | def _MaybePurgeOrphanedData(self, event):
"""Maybe purge orphaned data due to a TensorFlow crash.
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tensorflow/tensorboard | tensorboard/backend/event_processing/plugin_event_accumulator.py | EventAccumulator._CheckForOutOfOrderStepAndMaybePurge | def _CheckForOutOfOrderStepAndMaybePurge(self, event):
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"""Check for out-of-order event.step and discard expired events for tags.
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tensorflow/tensorboard | tensorboard/backend/event_processing/plugin_event_accumulator.py | EventAccumulator._Purge | def _Purge(self, event, by_tags):
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tensorflow/tensorboard | tensorboard/plugins/beholder/beholder_plugin_loader.py | BeholderPluginLoader.load | def load(self, context):
"""Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A BeholderPlugin instance or None if it couldn't be loaded.
"""
try:
# pylint: disable=g-import-not-at-top,unused-import
import tensorflow
except ImportError:
... | python | def load(self, context):
"""Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A BeholderPlugin instance or None if it couldn't be loaded.
"""
try:
# pylint: disable=g-import-not-at-top,unused-import
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tensorflow/tensorboard | tensorboard/plugins/graph/keras_util.py | _walk_layers | def _walk_layers(keras_layer):
"""Walks the nested keras layer configuration in preorder.
Args:
keras_layer: Keras configuration from model.to_json.
Yields:
A tuple of (name_scope, layer_config).
name_scope: a string representing a scope name, similar to that of tf.name_scope.
layer_config: a dic... | python | def _walk_layers(keras_layer):
"""Walks the nested keras layer configuration in preorder.
Args:
keras_layer: Keras configuration from model.to_json.
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tensorflow/tensorboard | tensorboard/plugins/graph/keras_util.py | _update_dicts | def _update_dicts(name_scope,
model_layer,
input_to_in_layer,
model_name_to_output,
prev_node_name):
"""Updates input_to_in_layer, model_name_to_output, and prev_node_name
based on the model_layer.
Args:
name_scope: a string representing... | python | def _update_dicts(name_scope,
model_layer,
input_to_in_layer,
model_name_to_output,
prev_node_name):
"""Updates input_to_in_layer, model_name_to_output, and prev_node_name
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tensorflow/tensorboard | tensorboard/plugins/graph/keras_util.py | keras_model_to_graph_def | def keras_model_to_graph_def(keras_layer):
"""Returns a GraphDef representation of the Keras model in a dict form.
Note that it only supports models that implemented to_json().
Args:
keras_layer: A dict from Keras model.to_json().
Returns:
A GraphDef representation of the layers in the model.
"""
... | python | def keras_model_to_graph_def(keras_layer):
"""Returns a GraphDef representation of the Keras model in a dict form.
Note that it only supports models that implemented to_json().
Args:
keras_layer: A dict from Keras model.to_json().
Returns:
A GraphDef representation of the layers in the model.
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_plugin_loader.py | HParamsPluginLoader.load | def load(self, context):
"""Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A HParamsPlugin instance or None if it couldn't be loaded.
"""
try:
# pylint: disable=g-import-not-at-top,unused-import
import tensorflow
except ImportError:
... | python | def load(self, context):
"""Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A HParamsPlugin instance or None if it couldn't be loaded.
"""
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_plugin.py | HParamsPlugin.is_active | def is_active(self):
"""Returns True if the hparams plugin is active.
The hparams plugin is active iff there is a tag with
the hparams plugin name as its plugin name and the scalars plugin is
registered and active.
"""
if not self._context.multiplexer:
return False
scalars_plugin = se... | python | def is_active(self):
"""Returns True if the hparams plugin is active.
The hparams plugin is active iff there is a tag with
the hparams plugin name as its plugin name and the scalars plugin is
registered and active.
"""
if not self._context.multiplexer:
return False
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tensorflow/tensorboard | tensorboard/plugin_util.py | markdown_to_safe_html | def markdown_to_safe_html(markdown_string):
"""Convert Markdown to HTML that's safe to splice into the DOM.
Arguments:
markdown_string: A Unicode string or UTF-8--encoded bytestring
containing Markdown source. Markdown tables are supported.
Returns:
A string containing safe HTML.
"""
warning =... | python | def markdown_to_safe_html(markdown_string):
"""Convert Markdown to HTML that's safe to splice into the DOM.
Arguments:
markdown_string: A Unicode string or UTF-8--encoded bytestring
containing Markdown source. Markdown tables are supported.
Returns:
A string containing safe HTML.
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | as_dtype | def as_dtype(type_value):
"""Converts the given `type_value` to a `DType`.
Args:
type_value: A value that can be converted to a `tf.DType` object. This may
currently be a `tf.DType` object, a [`DataType`
enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto),
... | python | def as_dtype(type_value):
"""Converts the given `type_value` to a `DType`.
Args:
type_value: A value that can be converted to a `tf.DType` object. This may
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.real_dtype | def real_dtype(self):
"""Returns the dtype correspond to this dtype's real part."""
base = self.base_dtype
if base == complex64:
return float32
elif base == complex128:
return float64
else:
return self | python | def real_dtype(self):
"""Returns the dtype correspond to this dtype's real part."""
base = self.base_dtype
if base == complex64:
return float32
elif base == complex128:
return float64
else:
return self | [
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.is_integer | def is_integer(self):
"""Returns whether this is a (non-quantized) integer type."""
return (
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and not self.is_quantized
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) | python | def is_integer(self):
"""Returns whether this is a (non-quantized) integer type."""
return (
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.is_floating | def is_floating(self):
"""Returns whether this is a (non-quantized, real) floating point type."""
return (
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"""Returns whether this is a (non-quantized, real) floating point type."""
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.min | def min(self):
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.limits | def limits(self, clip_negative=True):
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clip_negative : bool, optional
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.is_compatible_with | def is_compatible_with(self, other):
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```python
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tensorflow/tensorboard | tensorboard/plugins/debugger/interactive_debugger_plugin.py | InteractiveDebuggerPlugin.listen | def listen(self, grpc_port):
"""Start listening on the given gRPC port.
This method of an instance of InteractiveDebuggerPlugin can be invoked at
most once. This method is not thread safe.
Args:
grpc_port: port number to listen at.
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"""Start listening on the given gRPC port.
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tensorflow/tensorboard | tensorboard/plugins/debugger/interactive_debugger_plugin.py | InteractiveDebuggerPlugin.get_plugin_apps | def get_plugin_apps(self):
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This function also starts a debugger data server on separate thread if the
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Returns:
A mapping between routes and handlers (functions that respond to
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"""
return {
... | python | def get_plugin_apps(self):
"""Obtains a mapping between routes and handlers.
This function also starts a debugger data server on separate thread if the
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Returns:
A mapping between routes and handlers (functions that respond to
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin.is_active | def is_active(self):
"""The audio plugin is active iff any run has at least one relevant tag."""
if not self._multiplexer:
return False
return bool(self._multiplexer.PluginRunToTagToContent(metadata.PLUGIN_NAME)) | python | def is_active(self):
"""The audio plugin is active iff any run has at least one relevant tag."""
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._index_impl | def _index_impl(self):
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Result is a dictionary of the form
{
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._serve_audio_metadata | def _serve_audio_metadata(self, request):
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with URLs to the audio. The frontend should treat these URLs as
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... | python | def _serve_audio_metadata(self, request):
"""Given a tag and list of runs, serve a list of metadata for audio.
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._audio_response_for_run | def _audio_response_for_run(self, tensor_events, run, tag, sample):
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Args:
tensor_events: A list of image event_accumulator.TensorEvent objects.
run: The name of the run.
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._query_for_individual_audio | def _query_for_individual_audio(self, run, tag, sample, index):
"""Builds a URL for accessing the specified audio.
This should be kept in sync with _serve_audio_metadata. Note that the URL is
*not* guaranteed to always return the same audio, since audio may be
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"""Builds a URL for accessing the specified audio.
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tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._serve_individual_audio | def _serve_individual_audio(self, request):
"""Serve encoded audio data."""
tag = request.args.get('tag')
run = request.args.get('run')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
events = self._filter_by_sample(self._multiplexer.Tensors(run, tag), sample)
... | python | def _serve_individual_audio(self, request):
"""Serve encoded audio data."""
tag = request.args.get('tag')
run = request.args.get('run')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
events = self._filter_by_sample(self._multiplexer.Tensors(run, tag), sample)
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/app.py | _usage | def _usage(shorthelp):
"""Writes __main__'s docstring to stdout with some help text.
Args:
shorthelp: bool, if True, prints only flags from the main module,
rather than all flags.
"""
doc = _sys.modules['__main__'].__doc__
if not doc:
doc = '\nUSAGE: %s [flags]\n' % _sys.arg... | python | def _usage(shorthelp):
"""Writes __main__'s docstring to stdout with some help text.
Args:
shorthelp: bool, if True, prints only flags from the main module,
rather than all flags.
"""
doc = _sys.modules['__main__'].__doc__
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/app.py | run | def run(main=None, argv=None):
"""Runs the program with an optional 'main' function and 'argv' list."""
# Define help flags.
_define_help_flags()
# Parse known flags.
argv = flags.FLAGS(_sys.argv if argv is None else argv, known_only=True)
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# C... | python | def run(main=None, argv=None):
"""Runs the program with an optional 'main' function and 'argv' list."""
# Define help flags.
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argv = flags.FLAGS(_sys.argv if argv is None else argv, known_only=True)
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tensorflow/tensorboard | tensorboard/plugins/image/summary.py | op | def op(name,
images,
max_outputs=3,
display_name=None,
description=None,
collections=None):
"""Create a legacy image summary op for use in a TensorFlow graph.
Arguments:
name: A unique name for the generated summary node.
images: A `Tensor` representing pixel data with sh... | python | def op(name,
images,
max_outputs=3,
display_name=None,
description=None,
collections=None):
"""Create a legacy image summary op for use in a TensorFlow graph.
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name: A unique name for the generated summary node.
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tensorflow/tensorboard | tensorboard/plugins/image/summary.py | pb | def pb(name, images, max_outputs=3, display_name=None, description=None):
"""Create a legacy image summary protobuf.
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... | python | def pb(name, images, max_outputs=3, display_name=None, description=None):
"""Create a legacy image summary protobuf.
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tensorflow/tensorboard | tensorboard/backend/application.py | tensor_size_guidance_from_flags | def tensor_size_guidance_from_flags(flags):
"""Apply user per-summary size guidance overrides."""
tensor_size_guidance = dict(DEFAULT_TENSOR_SIZE_GUIDANCE)
if not flags or not flags.samples_per_plugin:
return tensor_size_guidance
for token in flags.samples_per_plugin.split(','):
k, v = token.strip().s... | python | def tensor_size_guidance_from_flags(flags):
"""Apply user per-summary size guidance overrides."""
tensor_size_guidance = dict(DEFAULT_TENSOR_SIZE_GUIDANCE)
if not flags or not flags.samples_per_plugin:
return tensor_size_guidance
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tensorflow/tensorboard | tensorboard/backend/application.py | standard_tensorboard_wsgi | def standard_tensorboard_wsgi(flags, plugin_loaders, assets_zip_provider):
"""Construct a TensorBoardWSGIApp with standard plugins and multiplexer.
Args:
flags: An argparse.Namespace containing TensorBoard CLI flags.
plugin_loaders: A list of TBLoader instances.
assets_zip_provider: See TBContext docum... | python | def standard_tensorboard_wsgi(flags, plugin_loaders, assets_zip_provider):
"""Construct a TensorBoardWSGIApp with standard plugins and multiplexer.
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flags: An argparse.Namespace containing TensorBoard CLI flags.
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tensorflow/tensorboard | tensorboard/backend/application.py | TensorBoardWSGIApp | def TensorBoardWSGIApp(logdir, plugins, multiplexer, reload_interval,
path_prefix='', reload_task='auto'):
"""Constructs the TensorBoard application.
Args:
logdir: the logdir spec that describes where data will be loaded.
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path_prefix='', reload_task='auto'):
"""Constructs the TensorBoard application.
Args:
logdir: the logdir spec that describes where data will be loaded.
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tensorflow/tensorboard | tensorboard/backend/application.py | parse_event_files_spec | def parse_event_files_spec(logdir):
"""Parses `logdir` into a map from paths to run group names.
The events files flag format is a comma-separated list of path specifications.
A path specification either looks like 'group_name:/path/to/directory' or
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"""Parses `logdir` into a map from paths to run group names.
The events files flag format is a comma-separated list of path specifications.
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tensorflow/tensorboard | tensorboard/backend/application.py | start_reloading_multiplexer | def start_reloading_multiplexer(multiplexer, path_to_run, load_interval,
reload_task):
"""Starts automatically reloading the given multiplexer.
If `load_interval` is positive, the thread will reload the multiplexer
by calling `ReloadMultiplexer` every `load_interval` seconds, star... | python | def start_reloading_multiplexer(multiplexer, path_to_run, load_interval,
reload_task):
"""Starts automatically reloading the given multiplexer.
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tensorflow/tensorboard | tensorboard/backend/application.py | get_database_info | def get_database_info(db_uri):
"""Returns TBContext fields relating to SQL database.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A tuple with the db_module and db_connection_provider TBContext fields. If
db_uri was empty, then (None, None) is returned.
Raise... | python | def get_database_info(db_uri):
"""Returns TBContext fields relating to SQL database.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A tuple with the db_module and db_connection_provider TBContext fields. If
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tensorflow/tensorboard | tensorboard/backend/application.py | create_sqlite_connection_provider | def create_sqlite_connection_provider(db_uri):
"""Returns function that returns SQLite Connection objects.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A function that returns a new PEP-249 DB Connection, which must be closed,
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Raises:
... | python | def create_sqlite_connection_provider(db_uri):
"""Returns function that returns SQLite Connection objects.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A function that returns a new PEP-249 DB Connection, which must be closed,
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tensorflow/tensorboard | tensorboard/backend/application.py | TensorBoardWSGI._serve_plugins_listing | def _serve_plugins_listing(self, request):
"""Serves an object mapping plugin name to whether it is enabled.
Args:
request: The werkzeug.Request object.
Returns:
A werkzeug.Response object.
"""
response = {}
for plugin in self._plugins:
start = time.time()
response[plug... | python | def _serve_plugins_listing(self, request):
"""Serves an object mapping plugin name to whether it is enabled.
Args:
request: The werkzeug.Request object.
Returns:
A werkzeug.Response object.
"""
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tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | parse_time_indices | def parse_time_indices(s):
"""Parse a string as time indices.
Args:
s: A valid slicing string for time indices. E.g., '-1', '[:]', ':', '2:10'
Returns:
A slice object.
Raises:
ValueError: If `s` does not represent valid time indices.
"""
if not s.startswith('['):
s = '[' + s + ']'
parse... | python | def parse_time_indices(s):
"""Parse a string as time indices.
Args:
s: A valid slicing string for time indices. E.g., '-1', '[:]', ':', '2:10'
Returns:
A slice object.
Raises:
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"""
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tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | process_buffers_for_display | def process_buffers_for_display(s, limit=40):
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tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | array_view | def array_view(array, slicing=None, mapping=None):
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Args:
array: The input array, as an numpy.ndarray.
slicing: Optional slicing string, e.g., "[:, 1:3, :]".
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`None` or case-insensitive `'None'`: Un... | python | def array_view(array, slicing=None, mapping=None):
"""View a slice or the entirety of an ndarray.
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array: The input array, as an numpy.ndarray.
slicing: Optional slicing string, e.g., "[:, 1:3, :]".
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tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | array_to_base64_png | def array_to_base64_png(array):
"""Convert an array into base64-enoded PNG image.
Args:
array: A 2D np.ndarray or nested list of items.
Returns:
A base64-encoded string the image. The image is grayscale if the array is
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3.... | python | def array_to_base64_png(array):
"""Convert an array into base64-enoded PNG image.
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array: A 2D np.ndarray or nested list of items.
Returns:
A base64-encoded string the image. The image is grayscale if the array is
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array: A 2D np.ndarray or nested list of items.
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tensorflow/tensorboard | tensorboard/plugins/graph/graph_util.py | _safe_copy_proto_list_values | def _safe_copy_proto_list_values(dst_proto_list, src_proto_list, get_key):
"""Safely merge values from `src_proto_list` into `dst_proto_list`.
Each element in `dst_proto_list` must be mapped by `get_key` to a key
value that is unique within that list; likewise for `src_proto_list`.
If an element of `src_proto_... | python | def _safe_copy_proto_list_values(dst_proto_list, src_proto_list, get_key):
"""Safely merge values from `src_proto_list` into `dst_proto_list`.
Each element in `dst_proto_list` must be mapped by `get_key` to a key
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tensorflow/tensorboard | tensorboard/plugins/graph/graph_util.py | combine_graph_defs | def combine_graph_defs(to_proto, from_proto):
"""Combines two GraphDefs by adding nodes from from_proto into to_proto.
All GraphDefs are expected to be of TensorBoard's.
It assumes node names are unique across GraphDefs if contents differ. The
names can be the same if the NodeDef content are exactly the same.
... | python | def combine_graph_defs(to_proto, from_proto):
"""Combines two GraphDefs by adding nodes from from_proto into to_proto.
All GraphDefs are expected to be of TensorBoard's.
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names can be the same if the NodeDef content are exactly the same.
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tensorflow/tensorboard | tensorboard/plugins/scalar/summary_v2.py | scalar | def scalar(name, data, step=None, description=None):
"""Write a scalar summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
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"""Write a scalar summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
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tensorflow/tensorboard | tensorboard/plugins/scalar/summary_v2.py | scalar_pb | def scalar_pb(tag, data, description=None):
"""Create a scalar summary_pb2.Summary protobuf.
Arguments:
tag: String tag for the summary.
data: A 0-dimensional `np.array` or a compatible python number type.
description: Optional long-form description for this summary, as a
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"""Create a scalar summary_pb2.Summary protobuf.
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tag: String tag for the summary.
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_demo.py | dump_data | def dump_data(logdir):
"""Dumps plugin data to the log directory."""
# Create a tfevents file in the logdir so it is detected as a run.
write_empty_event_file(logdir)
plugin_logdir = plugin_asset_util.PluginDirectory(
logdir, profile_plugin.ProfilePlugin.plugin_name)
_maybe_create_directory(plugin_logd... | python | def dump_data(logdir):
"""Dumps plugin data to the log directory."""
# Create a tfevents file in the logdir so it is detected as a run.
write_empty_event_file(logdir)
plugin_logdir = plugin_asset_util.PluginDirectory(
logdir, profile_plugin.ProfilePlugin.plugin_name)
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tensorflow/tensorboard | tensorboard/plugins/debugger/health_pill_calc.py | calc_health_pill | def calc_health_pill(tensor):
"""Calculate health pill of a tensor.
Args:
tensor: An instance of `np.array` (for initialized tensors) or
`tensorflow.python.debug.lib.debug_data.InconvertibleTensorProto`
(for unininitialized tensors).
Returns:
If `tensor` is an initialized tensor of numeric o... | python | def calc_health_pill(tensor):
"""Calculate health pill of a tensor.
Args:
tensor: An instance of `np.array` (for initialized tensors) or
`tensorflow.python.debug.lib.debug_data.InconvertibleTensorProto`
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tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._get_config | def _get_config(self):
'''Reads the config file from disk or creates a new one.'''
filename = '{}/{}'.format(self.PLUGIN_LOGDIR, CONFIG_FILENAME)
modified_time = os.path.getmtime(filename)
if modified_time != self.config_last_modified_time:
config = read_pickle(filename, default=self.previous_con... | python | def _get_config(self):
'''Reads the config file from disk or creates a new one.'''
filename = '{}/{}'.format(self.PLUGIN_LOGDIR, CONFIG_FILENAME)
modified_time = os.path.getmtime(filename)
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tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._write_summary | def _write_summary(self, session, frame):
'''Writes the frame to disk as a tensor summary.'''
summary = session.run(self.summary_op, feed_dict={
self.frame_placeholder: frame
})
path = '{}/{}'.format(self.PLUGIN_LOGDIR, SUMMARY_FILENAME)
write_file(summary, path) | python | def _write_summary(self, session, frame):
'''Writes the frame to disk as a tensor summary.'''
summary = session.run(self.summary_op, feed_dict={
self.frame_placeholder: frame
})
path = '{}/{}'.format(self.PLUGIN_LOGDIR, SUMMARY_FILENAME)
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tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._enough_time_has_passed | def _enough_time_has_passed(self, FPS):
'''For limiting how often frames are computed.'''
if FPS == 0:
return False
else:
earliest_time = self.last_update_time + (1.0 / FPS)
return time.time() >= earliest_time | python | def _enough_time_has_passed(self, FPS):
'''For limiting how often frames are computed.'''
if FPS == 0:
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tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._update_recording | def _update_recording(self, frame, config):
'''Adds a frame to the current video output.'''
# pylint: disable=redefined-variable-type
should_record = config['is_recording']
if should_record:
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'''Adds a frame to the current video output.'''
# pylint: disable=redefined-variable-type
should_record = config['is_recording']
if should_record:
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tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder.update | def update(self, session, arrays=None, frame=None):
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Args:
arrays: a list of np arrays. Use the "custom" option in the client.
frame: a 2D np array. This way the plugin can be used for video of any
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arrays: a list of np arrays. Use the "custom" option in the client.
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tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder.gradient_helper | def gradient_helper(optimizer, loss, var_list=None):
'''A helper to get the gradients out at each step.
Args:
optimizer: the optimizer op.
loss: the op that computes your loss value.
Returns: the gradient tensors and the train_step op.
'''
if var_list is None:
var_list = tf.compa... | python | def gradient_helper(optimizer, loss, var_list=None):
'''A helper to get the gradients out at each step.
Args:
optimizer: the optimizer op.
loss: the op that computes your loss value.
Returns: the gradient tensors and the train_step op.
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_key_func | def _create_key_func(extractor, none_is_largest):
"""Returns a key_func to be used in list.sort().
Returns a key_func to be used in list.sort() that sorts session groups
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_extractors | def _create_extractors(col_params):
"""Creates extractors to extract properties corresponding to 'col_params'.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
Returns:
A list of extractor functions. The ith element in the
returned list extracts the column corresponding to the i... | python | def _create_extractors(col_params):
"""Creates extractors to extract properties corresponding to 'col_params'.
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col_params: List of ListSessionGroupsRequest.ColParam protobufs.
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A list of extractor functions. The ith element in the
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_metric_extractor | def _create_metric_extractor(metric_name):
"""Returns function that extracts a metric from a session group or a session.
Args:
metric_name: tensorboard.hparams.MetricName protobuffer. Identifies the
metric to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.Session... | python | def _create_metric_extractor(metric_name):
"""Returns function that extracts a metric from a session group or a session.
Args:
metric_name: tensorboard.hparams.MetricName protobuffer. Identifies the
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _find_metric_value | def _find_metric_value(session_or_group, metric_name):
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_hparam_extractor | def _create_hparam_extractor(hparam_name):
"""Returns an extractor function that extracts an hparam from a session group.
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hparam_name: str. Identies the hparam to extract from the session group.
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"""Returns an extractor function that extracts an hparam from a session group.
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hparam_name: str. Identies the hparam to extract from the session group.
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_filters | def _create_filters(col_params, extractors):
"""Creates filters for the given col_params.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
extractors: list of extractor functions of the same length as col_params.
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"""Creates filters for the given col_params.
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_filter | def _create_filter(col_param, extractor):
"""Creates a filter for the given col_param and extractor.
Args:
col_param: A tensorboard.hparams.ColParams object identifying the column
and describing the filter to apply.
extractor: A function that extract the column value identified by
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"""Creates a filter for the given col_param and extractor.
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col_param: A tensorboard.hparams.ColParams object identifying the column
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_regexp_filter | def _create_regexp_filter(regex):
"""Returns a boolean function that filters strings based on a regular exp.
Args:
regex: A string describing the regexp to use.
Returns:
A function taking a string and returns True if any of its substrings
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"""
# Warning: Note that python's regex lib... | python | def _create_regexp_filter(regex):
"""Returns a boolean function that filters strings based on a regular exp.
Args:
regex: A string describing the regexp to use.
Returns:
A function taking a string and returns True if any of its substrings
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_interval_filter | def _create_interval_filter(interval):
"""Returns a function that checkes whether a number belongs to an interval.
Args:
interval: A tensorboard.hparams.Interval protobuf describing the interval.
Returns:
A function taking a number (a float or an object of a type in
six.integer_types) that returns Tr... | python | def _create_interval_filter(interval):
"""Returns a function that checkes whether a number belongs to an interval.
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interval: A tensorboard.hparams.Interval protobuf describing the interval.
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _value_to_python | def _value_to_python(value):
"""Converts a google.protobuf.Value to a native Python object."""
assert isinstance(value, struct_pb2.Value)
field = value.WhichOneof('kind')
if field == 'number_value':
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return value.string_value
elif field == 'boo... | python | def _value_to_python(value):
"""Converts a google.protobuf.Value to a native Python object."""
assert isinstance(value, struct_pb2.Value)
field = value.WhichOneof('kind')
if field == 'number_value':
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _set_avg_session_metrics | def _set_avg_session_metrics(session_group):
"""Sets the metrics for the group to be the average of its sessions.
The resulting session group metrics consist of the union of metrics across
the group's sessions. The value of each session group metric is the average
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _set_median_session_metrics | def _set_median_session_metrics(session_group, aggregation_metric):
"""Sets the metrics for session_group to those of its "median session".
The median session is the session in session_group with the median value
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _set_extremum_session_metrics | def _set_extremum_session_metrics(session_group, aggregation_metric,
extremum_fn):
"""Sets the metrics for session_group to those of its "extremum session".
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"""Sets the metrics for session_group to those of its "extremum session".
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _measurements | def _measurements(session_group, metric_name):
"""A generator for the values of the metric across the sessions in the group.
Args:
session_group: A SessionGroup protobuffer.
metric_name: A MetricName protobuffer.
Yields:
The next metric value wrapped in a _Measurement instance.
"""
for session_in... | python | def _measurements(session_group, metric_name):
"""A generator for the values of the metric across the sessions in the group.
Args:
session_group: A SessionGroup protobuffer.
metric_name: A MetricName protobuffer.
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler.run | def run(self):
"""Handles the request specified on construction.
Returns:
A ListSessionGroupsResponse object.
"""
session_groups = self._build_session_groups()
session_groups = self._filter(session_groups)
self._sort(session_groups)
return self._create_response(session_groups) | python | def run(self):
"""Handles the request specified on construction.
Returns:
A ListSessionGroupsResponse object.
"""
session_groups = self._build_session_groups()
session_groups = self._filter(session_groups)
self._sort(session_groups)
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._build_session_groups | def _build_session_groups(self):
"""Returns a list of SessionGroups protobuffers from the summary data."""
# Algorithm: We keep a dict 'groups_by_name' mapping a SessionGroup name
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._add_session | def _add_session(self, session, start_info, groups_by_name):
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Called by _build_session_groups when we encounter a new session. Creates
the Session protobuffer and adds it to the relevant group in the
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"""Adds a new Session protobuffer to the 'groups_by_name' dictionary.
Called by _build_session_groups when we encounter a new session. Creates
the Session protobuffer and adds it to the relevant group in the
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"=... | Adds a new Session protobuffer to the 'groups_by_name' dictionary.
Called by _build_session_groups when we encounter a new session. Creates
the Session protobuffer and adds it to the relevant group in the
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] | 8e5f497b48e40f2a774f85416b8a35ac0693c35e | https://github.com/tensorflow/tensorboard/blob/8e5f497b48e40f2a774f85416b8a35ac0693c35e/tensorboard/plugins/hparams/list_session_groups.py#L98-L130 | train |
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