def histogram_raw(name, min, max, num, sum, sum_squares, bucket_limits,
bucket_counts):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with a histogram.
The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
min: A float or int min value
max: A float or int max value
num: Int number of values
sum: Float or int sum of all values
sum_squares: Float or int sum of squares for all values
bucket_limits: A numeric `Tensor` with upper value per bucket
bucket_counts: A numeric `Tensor` with number of values per bucket
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
hist = HistogramProto(
min=min,
max=max,
num=num,
sum=sum,
sum_squares=sum_squares,
bucket_limit=bucket_limits,
bucket=bucket_counts,
)
return Summary(value=[Summary.Value(tag=name, histo=hist)])
def make_histogram(values, bins, max_bins=None):
"""Convert values into a histogram proto using logic from histogram.cc."""
if values.size == 0:
raise ValueError("The input has no element.")
values = values.reshape(-1)
counts, limits = np.histogram(values, bins=bins)
num_bins = len(counts)
if max_bins is not None and num_bins > max_bins:
subsampling = num_bins // max_bins
subsampling_remainder = num_bins % subsampling
if subsampling_remainder != 0:
counts = np.pad(
counts,
pad_width=[[0, subsampling - subsampling_remainder]],
mode="constant",
constant_values=0,
)
counts = counts.reshape(-1, subsampling).sum(axis=-1)
new_limits = np.empty((counts.size + 1, ), limits.dtype)
new_limits[:-1] = limits[:-1:subsampling]
new_limits[-1] = limits[-1]
limits = new_limits
# Find the first and the last bin defining the support of the histogram:
cum_counts = np.cumsum(np.greater(counts, 0, dtype=np.int32))
start, end = np.searchsorted(cum_counts, [0, cum_counts[-1] - 1],
side="right")
start = int(start)
end = int(end) + 1
del cum_counts
# TensorBoard only includes the right bin limits. To still have the leftmost limit
# included, we include an empty bin left.
# If start == 0, we need to add an empty one left, otherwise we can just include the bin left to the
# first nonzero-count bin:
counts = (counts[start - 1:end]
if start > 0 else np.concatenate([[0], counts[:end]]))
limits = limits[start:end + 1]
if counts.size == 0 or limits.size == 0:
raise ValueError("The histogram is empty, please file a bug report.")
sum_sq = values.dot(values)
return HistogramProto(
min=values.min(),
max=values.max(),
num=len(values),
sum=values.sum(),
sum_squares=sum_sq,
bucket_limit=limits.tolist(),
bucket=counts.tolist(),
)
def inferred_histo(summary, samples=1000):
np.random.seed(
hash(summary.std + summary.mean + summary.min + summary.max))
samples = np.random.randn(samples) * summary.std + summary.mean
samples = np.clip(samples, a_min=summary.min, a_max=summary.max)
(hist, edges) = np.histogram(samples)
upper_edges = edges[1:]
r = HistogramProto(min=summary.min,
max=summary.max,
num=len(samples),
sum=samples.sum(),
sum_squares=(samples * samples).sum())
r.bucket_limit.extend(upper_edges)
r.bucket.extend(hist)
return r