def scalar_summary(tags, values, collections=None, name=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with scalar values.
This ops is deprecated. Please switch to tf.summary.scalar.
For an explanation of why this op was deprecated, and information on how to
migrate, look
['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py)
The input `tags` and `values` must have the same shape. The generated
summary has a summary value for each tag-value pair in `tags` and `values`.
Args:
tags: A `string` `Tensor`. Tags for the summaries.
values: A real numeric Tensor. Values for the summaries.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
with ops.name_scope(name, "ScalarSummary", [tags, values]) as scope:
val = gen_logging_ops.scalar_summary(tags=tags,
values=values,
name=scope)
_Collect(val, collections, [ops.GraphKeys.SUMMARIES])
return val
def scalar(name, tensor, collections=None, family=None):
"""Outputs a `Summary` protocol buffer containing a single scalar value.
The generated Summary has a Tensor.proto containing the input Tensor.
Args:
name: A name for the generated node. Will also serve as the series name in
TensorBoard.
tensor: A real numeric Tensor containing a single value.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
family: Optional; if provided, used as the prefix of the summary tag name,
which controls the tab name used for display on Tensorboard.
Returns:
A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
"""
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.scalar_summary(tags=tag,
values=tensor,
name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def scalar_summary(tags, values, collections=None, name=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with scalar values.
This ops is deprecated. Please switch to tf.summary.scalar.
For an explanation of why this op was deprecated, and information on how to
migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py)
The input `tags` and `values` must have the same shape. The generated
summary has a summary value for each tag-value pair in `tags` and `values`.
Args:
tags: A `string` `Tensor`. Tags for the summaries.
values: A real numeric Tensor. Values for the summaries.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
with ops.name_scope(name, "ScalarSummary", [tags, values]) as scope:
val = gen_logging_ops.scalar_summary(tags=tags, values=values, name=scope)
_Collect(val, collections, [ops.GraphKeys.SUMMARIES])
return val
def scalar(name, tensor, collections=None, family=None):
"""Outputs a `Summary` protocol buffer containing a single scalar value.
The generated Summary has a Tensor.proto containing the input Tensor.
Args:
name: A name for the generated node. Will also serve as the series name in
TensorBoard.
tensor: A real numeric Tensor containing a single value.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
family: Optional; if provided, used as the prefix of the summary tag name,
which controls the tab name used for display on Tensorboard.
Returns:
A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
"""
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.scalar_summary(tags=tag, values=tensor, name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def scalar(name, tensor, collections=None, family=None):
"""Outputs a `Summary` protocol buffer containing a single scalar value.
The generated Summary has a Tensor.proto containing the input Tensor.
Args:
name: A name for the generated node. Will also serve as the series name in
TensorBoard.
tensor: A real numeric Tensor containing a single value.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
family: Optional; if provided, used as the prefix of the summary tag name,
which controls the tab name used for display on Tensorboard.
Returns:
A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`. To migrate
to TF2, please use `tf.summary.scalar` 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. `tf.summary.scalar` can also log training metrics in
Keras, you can check [Logging training metrics in
Keras](https://www.tensorflow.org/tensorboard/scalars_and_keras) for detials.
#### 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 | - |
| `family` | Removed | Please use `tf.name_scope` instead to |
: : : manage summary name prefix. :
| - | `description` | Optional long-form `str` description |
: : : for the summary. Markdown is supported.:
: : : Defaults to empty. :
@end_compatibility
"""
if _distribute_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.scalar_summary(tags=tag,
values=tensor,
name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def scalar(name, tensor, collections=None, family=None):
"""Outputs a `Summary` protocol buffer containing a single scalar value.
The generated Summary has a Tensor.proto containing the input Tensor.
Args:
name: A name for the generated node. Will also serve as the series name in
TensorBoard.
tensor: A real numeric Tensor containing a single value.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
family: Optional; if provided, used as the prefix of the summary tag name,
which controls the tab name used for display on Tensorboard.
Returns:
A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or 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`, or alternatively
`tf.summary.experimental.set_step`). For the forwarded call, the arguments
here will be passed to the TF2 implementation of `tf.summary.scalar`, 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.scalar` 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. `tf.summary.scalar` can also log training metrics in
Keras, you can check [Logging training metrics in
Keras](https://www.tensorflow.org/tensorboard/scalars_and_keras) for detials.
#### 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 | - |
| `family` | Removed | Please use `tf.name_scope` instead to |
: : : manage summary name prefix. :
| - | `description` | Optional long-form `str` description |
: : : for the summary. Markdown is supported.:
: : : Defaults to empty. :
@end_compatibility
"""
# Special case: invoke v2 op for TF2 users who have a v2 writer.
if _should_invoke_v2_op():
# Defer the import to happen inside the symbol to prevent breakage due to
# missing dependency.
from tensorboard.summary.v2 import scalar as scalar_v2 # pylint: disable=g-import-not-at-top
with _compat_summary_scope(name, family) as tag:
scalar_v2(name=tag, 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 scalar implementation.
if _distribute_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.scalar_summary(tags=tag, values=tensor, name=scope)
_summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
return val
def _scalar(name, tensor):
if not tf.get_variable_scope().reuse and not tf.sg_get_context().reuse:
val = gen_logging_ops.scalar_summary(name, tensor)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, val)
