def histogram_continuous(name,
data,
bucket_min=None,
bucket_max=None,
bucket_count=DEFAULT_BUCKET_COUNT,
step=None,
description=None):
"""histogram for continuous data .
Args:
name (str): name for this summary
data (Tensor): A `Tensor` of any shape.
bucket_min (float|None): represent bucket min value,
if None value of tf.reduce_min(data) will be used
bucket_max (float|None): represent bucket max value,
if None value tf.reduce_max(data) will be used
bucket_count (int): positive `int`. The output will have this many buckets.
step (None|tf.Variable): step value for this summary. this defaults to
`tf.summary.experimental.get_step()`
description (str): Optional long-form description for this summary
"""
summary_metadata = metadata.create_summary_metadata(
display_name=None, description=description)
summary_scope = (getattr(tf.summary.experimental, 'summary_scope', None)
or tf.summary.summary_scope)
with summary_scope(
name,
'histogram_summary',
values=[data, bucket_min, bucket_max, bucket_count,
step]) as (tag, _):
with tf.name_scope('buckets'):
data = tf.cast(tf.reshape(data, shape=[-1]), tf.float64)
if bucket_min is None:
bucket_min = tf.reduce_min(data)
if bucket_max is None:
bucket_max = tf.reduce_min(data)
range_ = bucket_max - bucket_min
bucket_width = range_ / tf.cast(bucket_count, tf.float64)
offsets = data - bucket_min
bucket_indices = tf.cast(tf.floor(offsets / bucket_width),
dtype=tf.int32)
clamped_indices = tf.clip_by_value(bucket_indices, 0,
bucket_count - 1)
one_hots = tf.one_hot(clamped_indices, depth=bucket_count)
bucket_counts = tf.cast(tf.reduce_sum(input_tensor=one_hots,
axis=0),
dtype=tf.float64)
edges = tf.linspace(bucket_min, bucket_max, bucket_count + 1)
edges = tf.concat([edges[:-1], [bucket_max]], 0)
edges = tf.cast(edges, tf.float64)
left_edges = edges[:-1]
right_edges = edges[1:]
tensor = tf.transpose(
a=tf.stack([left_edges, right_edges, bucket_counts]))
return tf.summary.write(tag=tag,
tensor=tensor,
step=step,
metadata=summary_metadata)
def when_nonempty():
min_ = tf.reduce_min(input_tensor=data)
max_ = tf.reduce_max(input_tensor=data)
range_ = max_ - min_
is_singular = tf.equal(range_, 0)
def when_nonsingular():
bucket_width = range_ / tf.cast(bucket_count, tf.float64)
offsets = data - min_
bucket_indices = tf.cast(tf.floor(offsets / bucket_width),
dtype=tf.int32)
clamped_indices = tf.minimum(bucket_indices, bucket_count - 1)
one_hots = tf.one_hot(clamped_indices, depth=bucket_count)
bucket_counts = tf.cast(tf.reduce_sum(input_tensor=one_hots,
axis=0),
dtype=tf.float64)
edges = tf.linspace(min_, max_, bucket_count + 1)
# Ensure edges[-1] == max_, which TF's linspace implementation does not
# do, leaving it subject to the whim of floating point rounding error.
edges = tf.concat([edges[:-1], [max_]], 0)
left_edges = edges[:-1]
right_edges = edges[1:]
return tf.transpose(
a=tf.stack([left_edges, right_edges, bucket_counts]))
def when_singular():
center = min_
bucket_starts = tf.stack([center - 0.5])
bucket_ends = tf.stack([center + 0.5])
bucket_counts = tf.stack(
[tf.cast(tf.size(input=data), tf.float64)])
return tf.transpose(
a=tf.stack([bucket_starts, bucket_ends, bucket_counts]))
return tf.cond(is_singular, when_singular, when_nonsingular)
def when_nonempty():
min_ = tf.reduce_min(input_tensor=data)
max_ = tf.reduce_max(input_tensor=data)
range_ = max_ - min_
has_single_value = tf.equal(range_, 0)
def when_multiple_values():
"""When input data contains multiple values."""
bucket_width = range_ / tf.cast(bucket_count, tf.float64)
offsets = data - min_
bucket_indices = tf.cast(tf.floor(offsets / bucket_width),
dtype=tf.int32)
clamped_indices = tf.minimum(bucket_indices, bucket_count - 1)
# Use float64 instead of float32 to avoid accumulating floating point error
# later in tf.reduce_sum when summing more than 2^24 individual `1.0` values.
# See https://github.com/tensorflow/tensorflow/issues/51419 for details.
one_hots = tf.one_hot(clamped_indices,
depth=bucket_count,
dtype=tf.float64)
bucket_counts = tf.cast(
tf.reduce_sum(input_tensor=one_hots, axis=0),
dtype=tf.float64,
)
edges = tf.linspace(min_, max_, bucket_count + 1)
# Ensure edges[-1] == max_, which TF's linspace implementation does not
# do, leaving it subject to the whim of floating point rounding error.
edges = tf.concat([edges[:-1], [max_]], 0)
left_edges = edges[:-1]
right_edges = edges[1:]
return tf.transpose(
a=tf.stack([left_edges, right_edges, bucket_counts]))
def when_single_value():
"""When input data contains a single unique value."""
# Left and right edges are the same for single value input.
edges = tf.fill([bucket_count], max_)
# Bucket counts are 0 except the last bucket (if bucket_count > 0),
# which is `data_size`. Ensure that the resulting counts vector has
# length `bucket_count` always, including the bucket_count==0 case.
zeroes = tf.fill([bucket_count], 0)
bucket_counts = tf.cast(
tf.concat([zeroes[:-1], [data_size]], 0)[:bucket_count],
dtype=tf.float64,
)
return tf.transpose(a=tf.stack([edges, edges, bucket_counts]))
return tf.cond(has_single_value, when_single_value,
when_multiple_values)
def when_nonempty():
min_ = tf.reduce_min(input_tensor=data)
max_ = tf.reduce_max(input_tensor=data)
range_ = max_ - min_
is_singular = tf.equal(range_, 0)
def when_nonsingular():
bucket_width = range_ / tf.cast(bucket_count, tf.float64)
offsets = data - min_
bucket_indices = tf.cast(tf.floor(offsets / bucket_width),
dtype=tf.int32)
clamped_indices = tf.minimum(bucket_indices, bucket_count - 1)
# Use float64 instead of float32 to avoid accumulating floating point error
# later in tf.reduce_sum when summing more than 2^24 individual `1.0` values.
# See https://github.com/tensorflow/tensorflow/issues/51419 for details.
one_hots = tf.one_hot(clamped_indices,
depth=bucket_count,
dtype=tf.float64)
bucket_counts = tf.cast(
tf.reduce_sum(input_tensor=one_hots, axis=0),
dtype=tf.float64,
)
edges = tf.linspace(min_, max_, bucket_count + 1)
# Ensure edges[-1] == max_, which TF's linspace implementation does not
# do, leaving it subject to the whim of floating point rounding error.
edges = tf.concat([edges[:-1], [max_]], 0)
left_edges = edges[:-1]
right_edges = edges[1:]
return tf.transpose(
a=tf.stack([left_edges, right_edges, bucket_counts]))
def when_singular():
center = min_
bucket_starts = tf.stack([center - 0.5])
bucket_ends = tf.stack([center + 0.5])
bucket_counts = tf.stack(
[tf.cast(tf.size(input=data), tf.float64)])
return tf.transpose(
a=tf.stack([bucket_starts, bucket_ends, bucket_counts]))
return tf.cond(is_singular, when_singular, when_nonsingular)
