def text(name, tensor, collections=None):
"""Summarizes textual data.
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 will automatically organize 1d and 2d tensors into tables.
If a tensor with more than 2 dimensions is provided, a 2d subarray will be
displayed along with a warning message. (Note that this behavior is not
intrinsic to the text summary api, but rather to the default TensorBoard text
plugin.)
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: a string-type Tensor to summarize.
collections: Optional list of ops.GraphKeys. The collections to add the
summary to. Defaults to [_ops.GraphKeys.SUMMARIES]
Returns:
A TensorSummary op that is configured so that TensorBoard will recognize
that it contains textual data. The TensorSummary is a scalar `Tensor` of
type `string` which contains `Summary` protobufs.
Raises:
ValueError: If tensor has the wrong type.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`. To migrate
to TF2, please use `tf.summary.text` instead. Please check
[Migrating tf.summary usage to
TF 2.0](https://www.tensorflow.org/tensorboard/migrate#in_tf_1x) for concrete
steps for migration.
#### How to Map Arguments
| TF1 Arg Name | TF2 Arg Name | Note |
| :------------ | :-------------- | :------------------------------------- |
| `name` | `name` | - |
| `tensor` | `data` | - |
| - | `step` | Explicit int64-castable monotonic step |
: : : value. If omitted, this defaults to :
: : : `tf.summary.experimental.get_step()`. :
| `collections` | Not Supported | - |
| - | `description` | Optional long-form `str` description |
: : : for the summary. Markdown is supported.:
: : : Defaults to empty. :
@end_compatibility
"""
if tensor.dtype != _dtypes.string:
raise ValueError('Expected tensor %s to have dtype string, got %s' %
(tensor.name, tensor.dtype))
summary_metadata = _SummaryMetadata(
plugin_data=_SummaryMetadata.PluginData(plugin_name='text'))
t_summary = tensor_summary(name=name,
tensor=tensor,
summary_metadata=summary_metadata,
collections=collections)
return t_summary
def tensor_summary(name,
tensor,
summary_description=None,
collections=None,
summary_metadata=None,
family=None,
display_name=None):
"""Outputs a `Summary` protocol buffer with a serialized tensor.proto.
Args:
name: A name for the generated node. If display_name is not set, it will
also serve as the tag name in TensorBoard. (In that case, the tag
name will inherit tf name scopes.)
tensor: A tensor of any type and shape to serialize.
summary_description: A long description of the summary sequence. Markdown
is supported.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
summary_metadata: Optional SummaryMetadata proto (which describes which
plugins may use the summary value).
family: Optional; if provided, used as the prefix of the summary tag,
which controls the name used for display on TensorBoard when
display_name is not set.
display_name: A string used to name this data in TensorBoard. If this is
not set, then the node name will be used instead.
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
if summary_metadata is None:
summary_metadata = _SummaryMetadata()
if summary_description is not None:
summary_metadata.summary_description = summary_description
if display_name is not None:
summary_metadata.display_name = display_name
serialized_summary_metadata = summary_metadata.SerializeToString()
if _summary_op_util.skip_summary():
return _constant_op.constant('')
with _summary_op_util.summary_scope(
name, family, values=[tensor]) as (tag, scope):
val = _gen_logging_ops.tensor_summary_v2(
tensor=tensor,
tag=tag,
name=scope,
serialized_summary_metadata=serialized_summary_metadata)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def text(name, tensor, collections=None):
"""Summarizes textual data.
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 will automatically organize 1d and 2d tensors into tables.
If a tensor with more than 2 dimensions is provided, a 2d subarray will be
displayed along with a warning message. (Note that this behavior is not
intrinsic to the text summary api, but rather to the default TensorBoard text
plugin.)
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: a string-type Tensor to summarize.
collections: Optional list of ops.GraphKeys. The collections to add the
summary to. Defaults to [_ops.GraphKeys.SUMMARIES]
Returns:
A TensorSummary op that is configured so that TensorBoard will recognize
that it contains textual data. The TensorSummary is a scalar `Tensor` of
type `string` which contains `Summary` protobufs.
Raises:
ValueError: If tensor has the wrong type.
"""
if tensor.dtype != _dtypes.string:
raise ValueError('Expected tensor %s to have dtype string, got %s' %
(tensor.name, tensor.dtype))
summary_metadata = _SummaryMetadata(
plugin_data=_SummaryMetadata.PluginData(plugin_name='text'))
t_summary = tensor_summary(
name=name,
tensor=tensor,
summary_metadata=summary_metadata,
collections=collections)
return t_summary
def text(name, tensor, collections=None):
"""Summarizes textual data.
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 will automatically organize 1d and 2d tensors into tables.
If a tensor with more than 2 dimensions is provided, a 2d subarray will be
displayed along with a warning message. (Note that this behavior is not
intrinsic to the text summary api, but rather to the default TensorBoard text
plugin.)
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: a string-type Tensor to summarize.
collections: Optional list of ops.GraphKeys. The collections to add the
summary to. Defaults to [_ops.GraphKeys.SUMMARIES]
Returns:
A TensorSummary op that is configured so that TensorBoard will recognize
that it contains textual data. The TensorSummary is a scalar `Tensor` of
type `string` which contains `Summary` protobufs.
Raises:
ValueError: If tensor has the wrong type.
@compatibility(TF2)
For compatibility purposes, when invoked in TF2 where the outermost context is
eager mode, this API will check if there is a suitable TF2 summary writer
context available, and if so will forward this call to that writer instead. A
"suitable" writer context means that the writer is set as the default writer,
and there is an associated non-empty value for `step` (see
`tf.summary.SummaryWriter.as_default`, `tf.summary.experimental.set_step` or
alternatively `tf.compat.v1.train.create_global_step`). For the forwarded
call, the arguments here will be passed to the TF2 implementation of
`tf.summary.text`, and the return value will be an empty bytestring tensor, to
avoid duplicate summary writing. This forwarding is best-effort and not all
arguments will be preserved.
To migrate to TF2, please use `tf.summary.text` instead. Please check
[Migrating tf.summary usage to
TF 2.0](https://www.tensorflow.org/tensorboard/migrate#in_tf_1x) for concrete
steps for migration.
#### How to Map Arguments
| TF1 Arg Name | TF2 Arg Name | Note |
| :------------ | :-------------- | :------------------------------------- |
| `name` | `name` | - |
| `tensor` | `data` | - |
| - | `step` | Explicit int64-castable monotonic step |
: : : value. If omitted, this defaults to :
: : : `tf.summary.experimental.get_step()`. :
| `collections` | Not Supported | - |
| - | `description` | Optional long-form `str` description |
: : : for the summary. Markdown is supported.:
: : : Defaults to empty. :
@end_compatibility
"""
if tensor.dtype != _dtypes.string:
raise ValueError('Expected tensor %s to have dtype string, got %s' %
(tensor.name, tensor.dtype))
# Special case: invoke v2 op for TF2 users who have a v2 writer.
if _should_invoke_v2_op():
# `skip_summary` check for v1 op case is done in `tensor_summary`.
if _distribute_summary_op_util.skip_summary():
return _constant_op.constant('')
# Defer the import to happen inside the symbol to prevent breakage due to
# missing dependency.
from tensorboard.summary.v2 import text as text_v2 # pylint: disable=g-import-not-at-top
text_v2(name=name, data=tensor, step=_get_step_for_v2())
# Return an empty Tensor, which will be acceptable as an input to the
# `tf.compat.v1.summary.merge()` API.
return _constant_op.constant(b'')
# Fall back to legacy v1 text implementation.
summary_metadata = _SummaryMetadata(
plugin_data=_SummaryMetadata.PluginData(plugin_name='text'))
t_summary = tensor_summary(name=name,
tensor=tensor,
summary_metadata=summary_metadata,
collections=collections)
return t_summary
