Python tensorflow.HistogramProto() Examples
The following are 30
code examples of tensorflow.HistogramProto().
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Example #1
| Source File: tb.py From Jacinle with MIT License | 6 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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)
Example #2
| Source File: utilities.py From Transferable-Adversarial-Training with MIT License | 6 votes |
|
def log_histogram(self, tag, values, step = None, bins = 1000):
if not step:
step = self.step
values = np.array(values)
counts, bin_edges = np.histogram(values, bins=bins)
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))
for edge in bin_edges:
hist.bucket_limit.append(edge)
for c in counts:
hist.bucket.append(c)
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
self.writer.add_summary(summary, step)
self.writer.flush()
Example #3
| Source File: logger.py From rl with MIT License | 6 votes |
|
def log_histogram(self, tag, values, bins=1000):
""" https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514 """
values = np.array(values)
counts, bin_edges = np.histogram(values, bins=bins)
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))
bin_edges = bin_edges[1:]
for edge in bin_edges:
hist.bucket_limit.append(edge)
for c in counts:
hist.bucket.append(c)
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
self.writer.add_summary(summary, self._hparams.global_step)
self.writer.flush()
Example #4
| Source File: generate_testdata.py From deep_image_model with Apache License 2.0 | 6 votes |
|
def _MakeHistogram(values):
"""Convert values into a histogram proto using logic from histogram.cc."""
limits = _MakeHistogramBuckets()
counts = [0] * len(limits)
for v in values:
idx = bisect.bisect_left(limits, v)
counts[idx] += 1
limit_counts = [(limits[i], counts[i]) for i in xrange(len(limits))
if counts[i]]
bucket_limit = [lc[0] for lc in limit_counts]
bucket = [lc[1] for lc in limit_counts]
sum_sq = sum(v * v for v in values)
return tf.HistogramProto(min=min(values),
max=max(values),
num=len(values),
sum=sum(values),
sum_squares=sum_sq,
bucket_limit=bucket_limit,
bucket=bucket)
Example #5
| Source File: event_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
|
def AddHistogram(self,
tag,
wall_time=0,
step=0,
hmin=1,
hmax=2,
hnum=3,
hsum=4,
hsum_squares=5,
hbucket_limit=None,
hbucket=None):
histo = tf.HistogramProto(min=hmin,
max=hmax,
num=hnum,
sum=hsum,
sum_squares=hsum_squares,
bucket_limit=hbucket_limit,
bucket=hbucket)
event = tf.summary.Event(
wall_time=wall_time,
step=step,
summary=tf.Summary(value=[tf.Summary.Value(
tag=tag, histo=histo)]))
self.AddEvent(event)
Example #6
| Source File: utilities.py From Separate_to_Adapt with MIT License | 6 votes |
|
def log_histogram(self, tag, values, step = None, bins = 1000):
if not step:
step = self.step
values = np.array(values)
counts, bin_edges = np.histogram(values, bins=bins)
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))
for edge in bin_edges:
hist.bucket_limit.append(edge)
for c in counts:
hist.bucket.append(c)
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
self.writer.add_summary(summary, step)
self.writer.flush()
Example #7
| Source File: logger.py From DivMatch with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #8
| Source File: logger.py From SalsaNext with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #9
| Source File: utilities.py From Transferable-Adversarial-Training with MIT License | 5 votes |
|
def log_bar(self, tag, values, xs = None, step = None):
if not step:
step = self.step
values = np.asarray(values).flatten()
if not xs:
axises = list(range(len(values)))
else:
axises = xs
hist = tf.HistogramProto()
hist.min = float(min(axises))
hist.max = float(max(axises))
hist.num = sum(values)
hist.sum = sum([y * x for (x, y) in zip(axises, values)])
hist.sum_squares = sum([y * (x ** 2) for (x, y) in zip(axises, values)])
for edge in axises:
hist.bucket_limit.append(edge - 1e-10)
hist.bucket_limit.append(edge + 1e-10)
for c in values:
hist.bucket.append(0)
hist.bucket.append(c)
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
self.writer.add_summary(summary, self.step)
self.writer.flush()
Example #10
| Source File: logger.py From EXTD_Pytorch with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #11
| Source File: logger.py From AUNets with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #12
| Source File: logging.py From aster.pytorch with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #13
| Source File: logger.py From pytorch-tutorial with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #14
| Source File: logger.py From CMCS-Temporal-Action-Localization with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #15
| Source File: logger.py From causal-infogan with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #16
| Source File: utilities.py From Separate_to_Adapt with MIT License | 5 votes |
|
def log_bar(self, tag, values, xs = None, step = None):
if not step:
step = self.step
values = np.asarray(values).flatten()
if not xs:
axises = list(range(len(values)))
else:
axises = xs
hist = tf.HistogramProto()
hist.min = float(min(axises))
hist.max = float(max(axises))
hist.num = sum(values)
hist.sum = sum([y * x for (x, y) in zip(axises, values)])
hist.sum_squares = sum([y * (x ** 2) for (x, y) in zip(axises, values)])
for edge in axises:
hist.bucket_limit.append(edge - 1e-10)
hist.bucket_limit.append(edge + 1e-10)
for c in values:
hist.bucket.append(0)
hist.bucket.append(c)
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
self.writer.add_summary(summary, self.step)
self.writer.flush()
Example #17
| Source File: logger.py From road_building_extraction with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #18
| Source File: logger.py From One-Shot-Object-Detection with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #19
| Source File: logger.py From bonnetal with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #20
| Source File: tensorboardlogger.py From PAST-ReID with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #21
| Source File: tensorboard_logging.py From TensorSwarm with MIT License | 5 votes |
|
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()
Example #22
| Source File: logger.py From MVCNN-PyTorch with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #23
| Source File: logger.py From bottom-up-features with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #24
| Source File: logger.py From CIOD with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #25
| Source File: logger.py From RL-Surgical-Gesture-Segmentation with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #26
| Source File: logger.py From DeepRecommender with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #27
| Source File: logger.py From AutoDL-Projects with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
if not self.use_tf: raise ValueError('Do not have tensorflow')
import tensorflow as tf
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #28
| Source File: logger.py From high-res-stereo with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #29
| Source File: logger.py From tatk with Apache License 2.0 | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
Example #30
| Source File: tblogger.py From SmoothParticleNets with MIT License | 5 votes |
|
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the 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))
# 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()
