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Logging to tensorboard with manually generated summaries (not relying on summary ops)
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"""Simple example on how to log scalars and images to tensorboard without tensor ops. | |
License: Copyleft | |
""" | |
__author__ = "Michael Gygli, Tao He" | |
import tensorflow as tf | |
try: | |
from StringIO import StringIO | |
except: | |
from io 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, *args, **kwargs): | |
"""Creates a summary writer logging to log_dir.""" | |
self.writer = tf.summary.FileWriter(log_dir, *args, **kwargs) | |
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) | |
def flush(self): | |
self.writer.flush() |
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