def op(name, data, display_name=None, description=None, collections=None):
"""Create a legacy scalar summary op.
Arguments:
name: A unique name for the generated summary node.
data: A real numeric rank-0 `Tensor`. Must have `dtype` castable
to `float32`.
display_name: Optional name for this summary in TensorBoard, as a
constant `str`. Defaults to `name`.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
collections: Optional list of graph collections keys. The new
summary op is added to these collections. Defaults to
`[Graph Keys.SUMMARIES]`.
Returns:
A TensorFlow summary op.
"""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if display_name is None:
display_name = name
summary_metadata = metadata.create_summary_metadata(
display_name=display_name, description=description)
with tf.name_scope(name):
with tf.control_dependencies([tf.assert_scalar(data)]):
return tf.summary.tensor_summary(
name="scalar_summary",
tensor=tf.cast(data, tf.float32),
collections=collections,
summary_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`.
"""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if bucket_count is None:
bucket_count = summary_v2.DEFAULT_BUCKET_COUNT
with tf.name_scope('buckets', values=[data, bucket_count]), \
tf.control_dependencies([tf.assert_scalar(bucket_count),
tf.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)
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 _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`.
"""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if bucket_count is None:
bucket_count = summary_v2.DEFAULT_BUCKET_COUNT
with tf.name_scope('buckets', values=[data, bucket_count]), \
tf.control_dependencies([tf.assert_scalar(bucket_count),
tf.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(): #这个函数将输入的值封装到bucket里面。以后需要该这个函数
bucket_num = tf.minimum(bucket_count, data.size()[0])
left_edges = tf.linspace(0, bucket_num - 1, bucket_num)
right_edges = tf.linspace(1, bucket_num, bucket_num)
return tf.transpose(a=tf.stack(
[left_edges, right_edges,
data[0:bucket_num]])) #stack是拼装矩阵。这里将这三个矩阵顺次拼装
def when_singular():
bucket_num = tf.minimum(bucket_count, data.size()[0])
left_edges = tf.linspace(0, bucket_num - 1, bucket_num)
right_edges = tf.linspace(1, bucket_num, bucket_num)
return tf.transpose(
a=tf.stack([left_edges, right_edges, data[0:bucket_num]]))
return tf.cond(is_singular, when_singular, when_nonsingular)
return tf.cond(is_empty, when_empty, when_nonempty)