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import tensorflow as tf
from io import BytesIO
from scipy.misc import imsave
import numpy as np
class TensorboardLogger(object):
"""Logging in tensorboard without tensorflow ops."""
def __init__(self, log_dir=None, writer=None):
if writer is None:
assert log_dir is not None, 'Must provide a logdir or a summary writer'
self.writer = tf.summary.FileWriter(log_dir)
self.writer = writer
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 = BytesIO()
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, min=None, max=None, density=False):
"""Logs the histogram of a list/vector of values."""
values = np.array(values)
# Fill fields of histogram proto
hist = tf.HistogramProto()
hist.min = float(np.min(values)) if min is None else float(min)
hist.max = float(np.max(values)) if min is None else float(max)
hist.num = int(
hist.sum = float(np.sum(values))
hist.sum_squares = float(np.sum(values**2))
# Create histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins, range=(hist.min, hist.max), density=density)
# 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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