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"""Summary Operations."""
# pylint: disable=wildcard-import,protected-access
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import gen_summary_ops
from tensorflow.python.ops.gen_summary_ops import *
def _Collect(val, collections, default_collections):
if collections is None:
collections = default_collections
for key in collections:
ops.add_to_collection(key, val)
def histogram_summary(tag, values, collections=None, name=None):
"""Outputs a `Summary` protocol buffer with a histogram.
The generated
[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
This op reports an `OutOfRange` error if any value is not finite.
Args:
tag: A `string` `Tensor`. 0-D. Tag to use for the summary value.
values: A `float32` `Tensor`. Any shape. Values to use to build the
histogram.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
with ops.op_scope([tag, values], name, "HistogramSummary") as scope:
val = gen_summary_ops._histogram_summary(
tag=tag, values=values, name=scope)
_Collect(val, collections, [ops.GraphKeys.SUMMARIES])
return val
def image_summary(tag, tensor, max_images=None, collections=None, name=None):
"""Outputs a `Summary` protocol buffer with images.
The summary has up to `max_images` summary values containing images. The
images are built from `tensor` which must be 4-D with shape `[batch_size,
height, width, channels]` and where `channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
The images have the same number of channels as the input tensor. Their values
are normalized, one image at a time, to fit in the range `[0, 255]`. The
op uses two different normalization algorithms:
* If the input values are all positive, they are rescaled so the largest one
is 255.
* If any input value is negative, the values are shifted so input value 0.0
is at 127. They are then rescaled so that either the smallest value is 0,
or the largest one is 255.
The `tag` argument is a scalar `Tensor` of type `string`. It is used to
build the `tag` of the summary values:
* If `max_images` is 1, the summary value tag is '*tag*/image'.
* If `max_images` is greater than 1, the summary value tags are
generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
Args:
tag: A scalar `Tensor` of type `string`. Used to build the `tag`
of the summary values.
tensor: A 4-D `float32` `Tensor` of shape `[batch_size, height, width,
channels]` where `channels` is 1, 3, or 4.
max_images: Max number of batch elements to generate images for.
collections: Optional list of ops.GraphKeys. The collections to add the
summary to. Defaults to [ops.GraphKeys.SUMMARIES]
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
with ops.op_scope([tag, tensor], name, "ImageSummary") as scope:
val = gen_summary_ops._image_summary(
tag=tag, tensor=tensor, max_images=max_images, name=scope)
_Collect(val, collections, [ops.GraphKeys.SUMMARIES])
return val
def merge_summary(inputs, collections=None, name=None):
"""Merges summaries.
This op creates a
[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input
summaries.
When the Op is run, it reports an `InvalidArgument` error if multiple values
in the summaries to merge use the same tag.
Args:
inputs: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer resulting from the merging.
"""
with ops.op_scope(inputs, name, "MergeSummary") as scope:
val = gen_summary_ops._merge_summary(inputs=inputs, name=name)
_Collect(val, collections, [])
return val
def merge_all_summaries(key=ops.GraphKeys.SUMMARIES):
"""Merges all summaries collected in the default graph.
Args:
key: `GraphKey` used to collect the summaries. Defaults to
`GraphKeys.SUMMARIES`.
Returns:
If no summaries were collected, returns None. Otherwise returns a scalar
`Tensor` of type`string` containing the serialized `Summary` protocol
buffer resulting from the merging.
"""
summary_ops = ops.get_collection(key)
if not summary_ops:
return None
else:
return merge_summary(summary_ops)
def scalar_summary(tags, values, collections=None, name=None):
"""Outputs a `Summary` protocol buffer with scalar values.
The input `tags` and `values` must have the same shape. The generated
summary has a summary value for each tag-value pair in `tags` and `values`.
Args:
tags: A 1-D `string` `Tensor`. Tags for the summaries.
values: A 1-D `float32` or `float64` Tensor. Values for the summaries.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
with ops.op_scope([tags, values], name, "ScalarSummary") as scope:
val = gen_summary_ops._scalar_summary(tags=tags, values=values, name=scope)
_Collect(val, collections, [ops.GraphKeys.SUMMARIES])
return val
ops.NoGradient("HistogramAccumulatorSummary")
ops.NoGradient("HistogramSummary")
ops.NoGradient("ImageSummary")
ops.NoGradient("MergeSummary")
ops.NoGradient("ScalarSummary")
@ops.RegisterShape("HistogramAccumulatorSummary")
@ops.RegisterShape("HistogramSummary")
@ops.RegisterShape("ImageSummary")
@ops.RegisterShape("MergeSummary")
@ops.RegisterShape("ScalarSummary")
def _ScalarShape(unused_op):
return [tensor_shape.scalar()]
|
