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tf.summary.scalar

Write a scalar summary.

Used in the notebooks

Used in the guide Used in the tutorials

See also tf.summary.image, tf.summary.histogram, tf.summary.SummaryWriter.

Writes simple numeric values for later analysis in TensorBoard. Writes go to the current default summary writer. Each summary point is associated with an integral step value. This enables the incremental logging of time series data. A common usage of this API is to log loss during training to produce a loss curve.

For example:

test_summary_writer = tf.summary.create_file_writer('test/logdir')
with test_summary_writer.as_default():
    tf.summary.scalar('loss', 0.345, step=1)
    tf.summary.scalar('loss', 0.234, step=2)
    tf.summary.scalar('loss', 0.123, step=3)

Multiple independent time series may be logged by giving each series a unique name value.

See Get started with TensorBoard for more examples of effective usage of tf.summary.scalar.

In general, this API expects that data points are logged iwth a monotonically increasing step value. Duplicate points for a single step or points logged out of order by step are not guaranteed to display as desired in TensorBoard.

name A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes.
data A real numeric scalar value, convertible to a float32 Tensor.
step Explicit int64-castable monotonic step value for this summary. If omitted, this defaults to tf.summary.experimental.get_step(), which must not be None.
description Optional long-form description for this summary, as a constant str. Markdown is supported. Defaults to empty.

True on success, or false if no summary was written because no default summary writer was available.

ValueError if a default writer exists, but no step was provided and tf.summary.experimental.get_step() is None.