tf.summary.histogram

Write a histogram summary.

tf.summary.histogram(
    name, data, step=None, buckets=None, description=None
)

See also tf.summary.scalar, tf.summary.SummaryWriter.

Writes a histogram to the current default summary writer, for later analysis in TensorBoard's 'Histograms' and 'Distributions' dashboards (data written using this API will appear in both places). Like tf.summary.scalar points, each histogram is associated with a step and a name. All the histograms with the same name constitute a time series of histograms.

The histogram is calculated over all the elements of the given Tensor without regard to its shape or rank.

This example writes 2 histograms:

w = tf.summary.create_file_writer('test/logs')
with w.as_default():
    tf.summary.histogram("activations", tf.random.uniform([100, 50]), step=0)
    tf.summary.histogram("initial_weights", tf.random.normal([1000]), step=0)

A common use case is to examine the changing activation patterns (or lack thereof) at specific layers in a neural network, over time.

w = tf.summary.create_file_writer('test/logs')
with w.as_default():
for step in range(100):
    # Generate fake "activations".
    activations = [
        tf.random.normal([1000], mean=step, stddev=1),
        tf.random.normal([1000], mean=step, stddev=10),
        tf.random.normal([1000], mean=step, stddev=100),
    ]

    tf.summary.histogram("layer1/activate", activations[0], step=step)
    tf.summary.histogram("layer2/activate", activations[1], step=step)
    tf.summary.histogram("layer3/activate", activations[2], step=step)

name A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes.
data A Tensor of any shape. The histogram is computed over its elements, which must be castable to float64.
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.
buckets Optional positive int. The output will have this many buckets, except in two edge cases. If there is no data, then there are no buckets. If there is data but all points have the same value, then all buckets' left and right endpoints are the same and only the last bucket has nonzero count.
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 emitted 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.