tf.summary.experimental.get

Experimental context manager for use when defining a custom summary op.

@tf_contextlib.contextmanager
tf.summary.experimental.summary_scope(
    name, default_name='summary', values=None
)

This behaves similarly to tf.name_scope, except that it returns a generated summary tag in addition to the scope name. The tag is structurally similar to the scope name - derived from the user-provided name, prefixed with enclosing name scopes if any - but we relax the constraint that it be uniquified, as well as the character set limitation (so the user-provided name can contain characters not legal for scope names; in the scope name these are removed).

This makes the summary tag more predictable and consistent for the user.

For example, to define a new summary op called my_op:

def my_op(name, my_value, step):
  with tf.summary.summary_scope(name, "MyOp", [my_value]) as (tag, scope):
    my_value = tf.convert_to_tensor(my_value)
    return tf.summary.write(tag, my_value, step=step)

Args
`name`	string name for the summary.
`default_name`	Optional; if provided, used as default name of the summary.
`values`	Optional; passed as `values` parameter to name_scope.

Yields
A tuple `(tag, scope)` as described above.

TensorFlow

tf.summary / experimental / experimental.get_step

Args

Yields

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.summary / experimental / experimental.get_step

Args

Yields