def testGetSetStep(self):
try:
self.assertIsNone(summary_ops.get_step())
summary_ops.set_step(1)
# Use assertAllEqual instead of assertEqual since it works in a defun.
self.assertAllEqual(1, summary_ops.get_step())
summary_ops.set_step(constant_op.constant(2))
self.assertAllEqual(2, summary_ops.get_step())
finally:
# Reset to default state for other tests.
summary_ops.set_step(None)
def testGetSetStep(self):
try:
self.assertIsNone(summary_ops.get_step())
summary_ops.set_step(1)
# Use assertAllEqual instead of assertEqual since it works in a defun.
self.assertAllEqual(1, summary_ops.get_step())
summary_ops.set_step(constant_op.constant(2))
self.assertAllEqual(2, summary_ops.get_step())
finally:
# Reset to default state for other tests.
summary_ops.set_step(None)
def testGetSetStep_variable_fromFunction(self):
with context.eager_mode():
try:
@def_function.function
def set_step(step):
summary_ops.set_step(step)
return summary_ops.get_step()
@def_function.function
def get_and_increment():
summary_ops.get_step().assign_add(1)
return summary_ops.get_step()
mystep = variables.Variable(0)
self.assertAllEqual(0, set_step(mystep))
self.assertAllEqual(0, summary_ops.get_step().read_value())
self.assertAllEqual(1, get_and_increment())
self.assertAllEqual(2, get_and_increment())
# Check that set_step() properly maintains reference to variable.
del mystep
self.assertAllEqual(3, get_and_increment())
finally:
# Reset to default state for other tests.
summary_ops.set_step(None)
def _get_step_for_v2():
"""Get step for v2 summary invocation in v1.
In order to invoke v2 op in `tf.compat.v1.summary`, global step needs to be
set for the v2 summary writer.
Returns:
The step set by `tf.summary.experimental.set_step` or
`tf.compat.v1.train.create_global_step`, or None is no step has been
set.
"""
step = _summary_ops_v2.get_step()
if step is not None:
return step
return _training_util.get_global_step()
def testGetSetStep_variable(self):
with context.eager_mode():
try:
mystep = variables.Variable(0)
summary_ops.set_step(mystep)
self.assertAllEqual(0, summary_ops.get_step().read_value())
mystep.assign_add(1)
self.assertAllEqual(1, summary_ops.get_step().read_value())
# Check that set_step() properly maintains reference to variable.
del mystep
self.assertAllEqual(1, summary_ops.get_step().read_value())
summary_ops.get_step().assign_add(1)
self.assertAllEqual(2, summary_ops.get_step().read_value())
finally:
# Reset to default state for other tests.
summary_ops.set_step(None)
def testGetSetStep_variable(self):
with context.eager_mode():
try:
mystep = variables.Variable(0)
summary_ops.set_step(mystep)
self.assertAllEqual(0, summary_ops.get_step().read_value())
mystep.assign_add(1)
self.assertAllEqual(1, summary_ops.get_step().read_value())
# Check that set_step() properly maintains reference to variable.
del mystep
self.assertAllEqual(1, summary_ops.get_step().read_value())
summary_ops.get_step().assign_add(1)
self.assertAllEqual(2, summary_ops.get_step().read_value())
finally:
# Reset to default state for other tests.
summary_ops.set_step(None)
def testGetSetStep_variable_fromFunction(self):
with context.eager_mode():
try:
@def_function.function
def set_step(step):
summary_ops.set_step(step)
return summary_ops.get_step()
@def_function.function
def get_and_increment():
summary_ops.get_step().assign_add(1)
return summary_ops.get_step()
mystep = variables.Variable(0)
self.assertAllEqual(0, set_step(mystep))
self.assertAllEqual(0, summary_ops.get_step().read_value())
self.assertAllEqual(1, get_and_increment())
self.assertAllEqual(2, get_and_increment())
# Check that set_step() properly maintains reference to variable.
del mystep
self.assertAllEqual(3, get_and_increment())
finally:
# Reset to default state for other tests.
summary_ops.set_step(None)
def get_and_increment():
summary_ops.get_step().assign_add(1)
return summary_ops.get_step()
def set_step(step):
summary_ops.set_step(step)
return summary_ops.get_step()
def get_and_increment(): summary_ops.get_step().assign_add(1) return summary_ops.get_step()
def set_step(step): summary_ops.set_step(step) return summary_ops.get_step()
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 _ops.executing_eagerly_outside_functions():
# Apart from an existing writer, users need to call
# `tf.summary.experimental.set_step` in order to invoke v2 API here.
if _summary_ops_v2.get_step(
) is not None and _summary_ops_v2.has_default_writer():
# Defer the import to happen inside the symbol to prevent breakage due to
# missing dependency.
# pylint: disable=g-import-not-at-top
from tensorboard.summary.v2 import scalar as scalar_v2
# TODO(b/210992280): Handle the family argument.
scalar_v2(name, data=tensor)
# 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
