Logging to tensorboard without tensorflow operations. Uses manually generated summaries instead of summary ops

tensorboard_logging.py

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

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

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, images, step):
        """Logs a list of images."""

        im_summaries = []
        for nr, img in enumerate(images):
            # Write the image to a string
            s = StringIO()
            plt.imsave(s, img, format='png')

            # Create an Image object
            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
                                       height=img.shape[0],
                                       width=img.shape[1])
            # Create a Summary value
            im_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, nr),
                                                 image=img_sum))

        # Create and write Summary
        summary = tf.Summary(value=im_summaries)
        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()

Thanks!

It works, thx!

Great code, thanks

excellent work, and I have to use BytesIO instead of StringIO for python 3.5.5, matplotlib 2.2.2

Cool! Thanks

@gyglim Maybe it is worth to add flush method? I think for newcomers to tensorboard it would be a good hint that they should consider flushing.

Thanks a lot, perfect!

Thanks a lot Michael, this helped me today :).
BTW: I had to use a Bytes-like object (BytesIO) instead of a StringIO for it to work (TF 1.13, Matplotlib 3.0.3).

Cheers !

Are you sure this works for multiple scalars? Is flush needed? I see points showing up in tboard but they do not seem to update often enough?

Also anyone know what to put in a step to get the scalars to look similar to the keras callback scalars?

text summary can be a new feature

Anyone knows how to construct text summary?

What does 'License: Copyleft' mean?
Can you provide more detailed license information, such as GPL, LGPL or MPL?

Thanks!!

Added BSD License 2.0

@gyglim Thanks!! :)

To add text summary

from tensorboard.plugins.text import metadata

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_text(self, tag, value, step):
	        """Log string or 2D string tables. """
	        summary_metadata = metadata.create_summary_metadata(
	                display_name="text",
	                description="Text Summary")
	        summary_metadata = tf.SummaryMetadata.FromString(
	                summary_metadata.SerializeToString())
	        tensor = tf.make_tensor_proto(value, dtype=tf.string)
	        summary = tf.Summary(value=[tf.Summary.Value(
	            tag=tag,
	            metadata=summary_metadata,
	            tensor=tensor
	            )])
	        self.writer.add_summary(summary, step)
	        self.writer.flush()

Example usage:

logger = Logger('/tmp/test')
logger.log_text(tag="string", value="test", step=0)
logger.log_text(tag="table", value=[["r0c0", "r0c1"], ["r1c0", "r1c1"]], step=0)

Tensorboard:

I get some incompatibility errors when running on tensorflow 2.0. Any plans to update this -very useful- code?

Wonderful solution:) Why flush is only in log_histogram?

Very minor tweaks for xrz000's text summary for TF2 Compatibility:

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.compat.v1.summary.FileWriter(log_dir)

def log_text(self, tag, value, step):
    """Log string or 2D string tables. """
    summary_metadata = metadata.create_summary_metadata(
        display_name="text",
        description="Text Summary")
    summary_metadata = tf.compat.v1.SummaryMetadata.FromString(
        summary_metadata.SerializeToString())
    tensor = tf.make_tensor_proto(value, dtype=tf.string)
    summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(
                tag=tag,
                metadata=summary_metadata,
                tensor=tensor
            )])
    self.writer.add_summary(summary, step)
    self.writer.flush()

Example Usage:

with tf.compat.v1.Graph().as_default():
    logger.log_text(tag="string", value="test", step=0)
    logger.log_text(tag="table", value=[["r0c0", "r0c1"], ["r1c0", "r1c1"]], step=0)

在张力流 2.0 上运行时，我遇到一些不兼容的错误。有什么计划来更新这个 - 非常有用的代码？

Hello, has your problem been solved? Which version should be configured to be compatible

tested it and every things worked great with tf 2.4

thank you very much!My bro! you are so nice!

…

------------------ 原始邮件 ------------------ 发件人: "gyglim" ***@***.***>; 发送时间: 2021年10月14日(星期四) 下午4:08 ***@***.***>; ***@***.******@***.***>; 主题: Re: gyglim/tensorboard_logging.py @Kisameee commented on this gist. tested it and every things worked great with tf 2.4 — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android.

tested it and every things worked great with tf 2.4

In tf 2.3, it fails with AttributeError: module 'tensorboard.summary._tf.summary' has no attribute 'FileWriter'

Anyone can tell me how can I use summary add_graph to show modal structure.