A simple python snippet for logging with tensorboard

tensorboard_logging.py

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: Copyleft
"""
__author__ = "Jeasine Ma"

import tensorflow as tf
from PIL import Image
import numpy as np
from io import BytesIO


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

    def __init__(self, log_dir):
        """Creates a summary writer logging to log_dir."""
        self.writer = tf.summary.FileWriter(log_dir)

    def log_scalar(self, tag, value, step):
        """Log a scalar variable.

        Parameter
        ----------
        tag : basestring
            Name of the scalar
        value
        step : int
            training iteration
        """
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag,
                                                     simple_value=value)])
        self.writer.add_summary(summary, step)

    def log_images(self, tag, image, step):
        """Logs a list of images."""

        height, width, channel = image.shape
        image = Image.fromarray(image)
        output = BytesIO()
        image.save(output, format='PNG')
        image_string = output.getvalue()
        output.close()

        # Create an Image object
        img_sum = tf.Summary.Image(height=height,
                         width=width,
                         colorspace=channel,
                         encoded_image_string=image_string)
        # Create a Summary value
        im_summary = tf.Summary.Value(tag='%s' % (tag), image=img_sum)

        # Create and write Summary
        summary = tf.Summary(value=[im_summary])
        self.writer.add_summary(summary, step)

    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()