tensorboard_logging.py

import tensorflow as tf
import numpy as np


class Logger(object):
  """Logging in tensorboard without tensorflow ops."""

  def __init__(self, log_dir, graph=None):
    """Creates a summary writer logging to log_dir."""
    self.writer = tf.summary.FileWriter(log_dir, graph=graph)


  def log_scalar(self, tag, value, step):
    summary = tf.Summary(value=[tf.Summary.Value(tag=tag,
                                                 simple_value=value)])
    self.writer.add_summary(summary, step)
    self.writer.flush()


  def log_histogram(self, tag, values, step, bins=1000):
    """Logs the histogram of a list/vector of values."""
    # 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(np.prod(values.shape))
    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 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
    # Thus, we drop the start of the first bin
    bin_edges = bin_edges[1:]

    # Add bin edges and counts
    for edge in bin_edges:
      hist.bucket_limit.append(edge)
    for c in counts:
      hist.bucket.append(c)

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