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 image(name, data, step=None, max_outputs=3, description=None):
"""Write an image summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A `Tensor` representing pixel data with shape `[k, h, w, c]`,
where `k` is the number of images, `h` and `w` are the height and
width of the images, and `c` is the number of channels, which
should be 1, 2, 3, or 4 (grayscale, grayscale with alpha, RGB, RGBA).
Any of the dimensions may be statically unknown (i.e., `None`).
Floating point data will be clipped to the range [0,1).
step: Explicit `int64`-castable monotonic step value for this summary. If
omitted, this defaults to `tf.summary.experimental.get_step()`, which must
not be None.
max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
many images will be emitted at each step. When more than
`max_outputs` many images are provided, the first `max_outputs` many
images will be used and the rest silently discarded.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
Returns:
True on success, or false if no summary was emitted because no default
summary writer was available.
Raises:
ValueError: if a default writer exists, but no step was provided and
`tf.summary.experimental.get_step()` is None.
"""
summary_metadata = metadata.create_summary_metadata(
display_name=None, description=description)
# TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
summary_scope = (getattr(tf.summary.experimental, 'summary_scope', None)
or tf.summary.summary_scope)
with summary_scope(name, 'image_summary', values=[data, max_outputs,
step]) as (tag, _):
tf.debugging.assert_rank(data, 4)
tf.debugging.assert_non_negative(max_outputs)
images = tf.image.convert_image_dtype(data, tf.uint8, saturate=True)
limited_images = images[:max_outputs]
encoded_images = tf.map_fn(tf.image.encode_png,
limited_images,
dtype=tf.string,
name='encode_each_image')
# Workaround for map_fn returning float dtype for an empty elems input.
encoded_images = tf.cond(
tf.shape(input=encoded_images)[0] > 0, lambda: encoded_images,
lambda: tf.constant([], tf.string))
image_shape = tf.shape(input=images)
dimensions = tf.stack([
tf.as_string(image_shape[2], name='width'),
tf.as_string(image_shape[1], name='height')
],
name='dimensions')
tensor = tf.concat([dimensions, encoded_images], axis=0)
return tf.summary.write(tag=tag,
tensor=tensor,
step=step,
metadata=summary_metadata)
def lazy_tensor():
tf.debugging.assert_rank(data, 4)
tf.debugging.assert_non_negative(max_outputs)
images = tf.image.convert_image_dtype(data,
tf.uint8,
saturate=True)
limited_images = images[:max_outputs]
encoded_images = tf.map_fn(
tf.image.encode_png,
limited_images,
dtype=tf.string,
name="encode_each_image",
)
# Workaround for map_fn returning float dtype for an empty elems input.
encoded_images = tf.cond(
tf.shape(input=encoded_images)[0] > 0,
lambda: encoded_images,
lambda: tf.constant([], tf.string),
)
image_shape = tf.shape(input=images)
dimensions = tf.stack(
[
tf.as_string(image_shape[2], name="width"),
tf.as_string(image_shape[1], name="height"),
],
name="dimensions",
)
return tf.concat([dimensions, encoded_images], axis=0)
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_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]))
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]))
