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Logging to tensorboard without tensorflow operations. Uses manually generated summaries instead of summary ops
"""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.
tag : basestring
Name of the scalar
step : int
training iteration
summary = tf.Summary(value=[tf.Summary.Value(tag=tag,
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(),
# Create a Summary value
im_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, nr),
# 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(
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)])
self.writer.add_summary(summary, step)
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Zlo7 commented Oct 11, 2020

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(
        description="Text Summary")
    summary_metadata = tf.compat.v1.SummaryMetadata.FromString(
    tensor = tf.make_tensor_proto(value, dtype=tf.string)
    summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(
    self.writer.add_summary(summary, step)

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)

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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

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tested it and every things worked great with tf 2.4

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hanchengyu3 commented Oct 14, 2021 via email

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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'

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

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