Create a custom Tensorflow histogram summary

There are a couple of SO answers on creating a custom scalar summary in TF (here and here), but I can’t find anything on creating a custom histogram summary. The documentation seems to be very lacking for custom summaries. I have a numpy array of that I’d like to make a summary of – any ideas on how?

(tf.Summary.Value has a histo field that I tried using, but it requires a tensorflow::HistogramProto; there’s no documentation on that class either, so I’m at a loss on how to make it. I’ve tried creating a minimal failing example below).

import tensorflow as tf
import numpy as np
sess = tf.Session()
means_placeholder = tf.placeholder(tf.float32)
tf.summary.histogram('means', means_placeholder)
summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter('./summaries')
means = np.random.random(10)    
writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='means', histo=means)]))


This piece of code works:

import tensorflow as tf

import numpy as np

def log_histogram(writer, tag, values, step, bins=1000):
    # Convert to a numpy array
    values = np.array(values)

    # Create histogram using numpy
    counts, bin_edges = np.histogram(values, bins=bins)

    # Fill fields of histogram proto
    hist = tf.HistogramProto()
    hist.min = float(np.min(values))
    hist.max = float(np.max(values))
    hist.num = int(
    hist.sum = float(np.sum(values))
    hist.sum_squares = float(np.sum(values**2))

    # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
    # See
    # Thus, we drop the start of the first bin
    bin_edges = bin_edges[1:]

    # Add bin edges and counts
    for edge in bin_edges:
    for c in counts:

    # Create and write Summary
    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
    writer.add_summary(summary, step)

sess = tf.Session()
placeholder = tf.placeholder(tf.float32)

tf.summary.histogram('N(0,1)', placeholder)
summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter('./summaries')

mu, sigma = 0, 0.1 # mean and standard deviation
s = np.random.normal(mu, 1, 10000)
log_histogram(writer, 'N(0,1)', s, 1, bins=100)

The source.

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