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 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 scalar(name, data, step=None, description=None):
"""Write a scalar 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 real numeric scalar value, convertible to a `float32` Tensor.
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.
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 written 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)
with tf.summary.summary_scope(
name, 'scalar_summary', values=[data, step]) as (tag, _):
tf.debugging.assert_scalar(data)
return tf.summary.write(tag=tag,
tensor=tf.cast(data, tf.float32),
step=step,
metadata=summary_metadata)
def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets.
Arguments:
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
Returns:
A `Tensor` of shape `[k, 3]` and type `float64`. The `i`th row is
a triple `[left_edge, right_edge, count]` for a single bucket.
The value of `k` is either `bucket_count` or `1` or `0`.
"""
if bucket_count is None:
bucket_count = DEFAULT_BUCKET_COUNT
with tf.name_scope('buckets'):
tf.debugging.assert_scalar(bucket_count)
tf.debugging.assert_type(bucket_count, tf.int32)
data = tf.reshape(data, shape=[-1]) # flatten
data = tf.cast(data, tf.float64)
is_empty = tf.equal(tf.size(input=data), 0)
def when_empty():
return tf.constant([], shape=(0, 3), dtype=tf.float64)
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)
return tf.cond(is_empty, when_empty, when_nonempty)
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]))
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 histogram_discrete(name,
data,
bucket_min,
bucket_max,
step=None,
description=None):
"""histogram for discrete data.
Args:
name (str): name for this summary
data (Tensor): A `Tensor` integers of any shape.
bucket_min (int): represent bucket min value
bucket_max (int): represent bucket max value
bucket count is calculate as `bucket_max - bucket_min + 1`
and 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,
step]) as (tag, _):
with tf.name_scope('buckets'):
bucket_count = bucket_max - bucket_min + 1
data = data - bucket_min
one_hots = tf.one_hot(tf.reshape(data, shape=[-1]),
depth=bucket_count)
bucket_counts = tf.cast(
tf.reduce_sum(input_tensor=one_hots, axis=0), tf.float64)
edge = tf.cast(tf.range(bucket_count), tf.float64)
# histogram can not draw when left_edge == right_edge
left_edge = edge - 1e-12
right_edge = edge + 1e-12
tensor = tf.transpose(
a=tf.stack([left_edge, right_edge, 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 scalar(name, data, step, description=None):
"""Write a scalar 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 real numeric scalar value, convertible to a `float32` Tensor.
step: Required `int64`-castable monotonic step value.
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 written because no default
summary writer was available.
"""
summary_metadata = metadata.create_summary_metadata(
display_name=None, description=description)
with tf.summary.summary_scope(name, 'scalar_summary',
values=[data, step]) as (tag, _):
tf.debugging.assert_scalar(data)
return tf.summary.write(tag=tag,
tensor=tf.cast(data, tf.float32),
step=step,
metadata=summary_metadata)
def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets.
Arguments:
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional non-negative `int` or scalar `int32` `Tensor`,
defaults to 30.
Returns:
A `Tensor` of shape `[k, 3]` and type `float64`. The `i`th row is
a triple `[left_edge, right_edge, count]` for a single bucket.
The value of `k` is either `bucket_count` or `0` (when input data
is empty).
"""
if bucket_count is None:
bucket_count = DEFAULT_BUCKET_COUNT
with tf.name_scope("buckets"):
tf.debugging.assert_scalar(bucket_count)
tf.debugging.assert_type(bucket_count, tf.int32)
# Treat a negative bucket count as zero.
bucket_count = tf.math.maximum(0, bucket_count)
data = tf.reshape(data, shape=[-1]) # flatten
data = tf.cast(data, tf.float64)
data_size = tf.size(input=data)
is_empty = tf.logical_or(tf.equal(data_size, 0),
tf.less_equal(bucket_count, 0))
def when_empty():
"""When input data is empty or bucket_count is zero.
1. If bucket_count is specified as zero, an empty tensor of shape
(0, 3) will be returned.
2. If the input data is empty, a tensor of shape (bucket_count, 3)
of all zero values will be returned.
"""
return tf.zeros((bucket_count, 3), dtype=tf.float64)
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)
return tf.cond(is_empty, when_empty, when_nonempty)
def scalar(name, data, step=None, description=None):
"""Write a scalar summary.
See also `tf.summary.image`, `tf.summary.histogram`, `tf.summary.SummaryWriter`.
Writes simple numeric values for later analysis in TensorBoard. Writes go to
the current default summary writer. Each summary point is associated with an
integral `step` value. This enables the incremental logging of time series
data. A common usage of this API is to log loss during training to produce
a loss curve.
For example:
```python
test_summary_writer = tf.summary.create_file_writer('test/logdir')
with test_summary_writer.as_default():
tf.summary.scalar('loss', 0.345, step=1)
tf.summary.scalar('loss', 0.234, step=2)
tf.summary.scalar('loss', 0.123, step=3)
```
Multiple independent time series may be logged by giving each series a unique
`name` value.
See [Get started with TensorBoard](https://www.tensorflow.org/tensorboard/get_started)
for more examples of effective usage of `tf.summary.scalar`.
In general, this API expects that data points are logged iwth a monotonically
increasing step value. Duplicate points for a single step or points logged out
of order by step are not guaranteed to display as desired in TensorBoard.
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 real numeric scalar value, convertible to a `float32` Tensor.
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.
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 written 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, "scalar_summary", values=[data,
step]) as (tag, _):
tf.debugging.assert_scalar(data)
return tf.summary.write(
tag=tag,
tensor=tf.cast(data, tf.float32),
step=step,
metadata=summary_metadata,
)
