本文整理汇总了Python中tensorflow.python.framework.ops.add_to_collections方法的典型用法代码示例。如果您正苦于以下问题:Python ops.add_to_collections方法的具体用法?Python ops.add_to_collections怎么用?Python ops.add_to_collections使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.framework.ops
的用法示例。
在下文中一共展示了ops.add_to_collections方法的14个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: collect_named_outputs
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def collect_named_outputs(collections, alias, outputs):
"""Add `Tensor` outputs tagged with alias to collections.
It is useful to collect end-points or tags for summaries. Example of usage:
logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
assert 'inception_v3/logits' in logits.aliases
Args:
collections: A collection or list of collections. If None skip collection.
alias: String to append to the list of aliases of outputs, for example,
'inception_v3/conv1'.
outputs: Tensor, an output tensor to collect
Returns:
The outputs Tensor to allow inline call.
"""
if collections:
append_tensor_alias(outputs, alias)
ops.add_to_collections(collections, outputs)
return outputs
示例2: collect_named_outputs
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def collect_named_outputs(collections, alias, outputs):
"""Add `Tensor` outputs tagged with alias to collections.
It is useful to collect end-points or tags for summaries. Example of usage:
logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
assert 'inception_v3/logits' in logits.aliases
Args:
collections: A collection or list of collections. If None skip collection.
alias: String to append to the list of aliases of outputs, for example,
'inception_v3/conv1'.
outputs: Tensor, an output tensor to collect
Returns:
The outputs Tensor to allow inline call.
"""
append_tensor_alias(outputs, alias)
if collections:
ops.add_to_collections(collections, outputs)
return outputs
示例3: collect_named_outputs
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def collect_named_outputs(collections, alias, outputs):
"""Add `Tensor` outputs tagged with alias to collections.
It is useful to collect end-points or tags for summaries. Example of usage:
logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
assert logits.alias == 'inception_v3/logits'
Args:
collections: A collection or list of collections. If None skip collection.
alias: String, alias to name the outputs, ex. 'inception_v3/conv1'
outputs: Tensor, an output tensor to collect
Returns:
The outputs Tensor to allow inline call.
"""
# Remove ending '/' if present.
if alias[-1] == '/':
alias = alias[:-1]
outputs.alias = alias
if collections:
ops.add_to_collections(collections, outputs)
return outputs
示例4: _apply_activation
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def _apply_activation(y, activation_fn, output_collections):
if activation_fn is not None:
y = activation_fn(y)
ops.add_to_collections(
list(output_collections or []) + [ops.GraphKeys.ACTIVATIONS], y)
return y
示例5: _apply_activation
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def _apply_activation(y, activation_fn, output_collections):
if activation_fn is not None:
y = activation_fn(y)
ops.add_to_collections(list(output_collections or []) +
[ops.GraphKeys.ACTIVATIONS], y)
return y
示例6: _register_variable_read
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def _register_variable_read(read, collections, trainable):
"""Helper function to put a read from a variable in the collections."""
if collections is None:
collections = [ops.GraphKeys.GLOBAL_VARIABLES]
if (trainable and ops.GraphKeys.TRAINABLE_VARIABLES
not in collections):
collections = (list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES])
ops.add_to_collections(collections, read)
示例7: _register_variable_read
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def _register_variable_read(read, collections, trainable):
"""Helper function to put a read from a variable in the collections."""
if collections is None:
collections = []
if (trainable and
ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES not in collections):
collections = (list(collections) +
[ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES])
ops.add_to_collections(collections, read)
示例8: _count_condition
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def _count_condition(values, weights=None, metrics_collections=None,
updates_collections=None):
"""Sums the weights of cases where the given values are True.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
values: A `bool` `Tensor` of arbitrary size.
weights: An optional `Tensor` whose shape is broadcastable to `values`.
metrics_collections: An optional list of collections that the metric
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
Returns:
value_tensor: A tensor representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `weights` is not `None` and its shape doesn't match `values`,
or if either `metrics_collections` or `updates_collections` are not a list
or tuple.
"""
check_ops.assert_type(values, dtypes.bool)
count = _create_local('count', shape=[])
values = math_ops.to_float(values)
if weights is not None:
weights = math_ops.to_float(weights)
values = math_ops.mul(values, weights)
value_tensor = array_ops.identity(count)
update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))
if metrics_collections:
ops.add_to_collections(metrics_collections, value_tensor)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return value_tensor, update_op
示例9: _count_condition
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def _count_condition(values, weights=None, metrics_collections=None,
updates_collections=None):
"""Sums the weights of cases where the given values are True.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
values: A `bool` `Tensor` of arbitrary size.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`values`, and must be broadcastable to `values` (i.e., all dimensions must
be either `1`, or the same as the corresponding `values` dimension).
metrics_collections: An optional list of collections that the metric
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
Returns:
value_tensor: A `Tensor` representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `weights` is not `None` and its shape doesn't match `values`,
or if either `metrics_collections` or `updates_collections` are not a list
or tuple.
"""
check_ops.assert_type(values, dtypes.bool)
count = _create_local('count', shape=[])
values = math_ops.to_float(values)
if weights is not None:
with ops.control_dependencies((
check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
weights = math_ops.to_float(weights)
values = math_ops.multiply(values, weights)
value_tensor = array_ops.identity(count)
update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))
if metrics_collections:
ops.add_to_collections(metrics_collections, value_tensor)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return value_tensor, update_op
示例10: _count_condition
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def _count_condition(values, weights=None, metrics_collections=None,
updates_collections=None):
"""Sums the weights of cases where the given values are True.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
values: A `bool` `Tensor` of arbitrary size.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`values`, and must be broadcastable to `values` (i.e., all dimensions
must be either `1`, or the same as the corresponding `values`
dimension).
metrics_collections: An optional list of collections that the metric
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
Returns:
value_tensor: A `Tensor` representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `weights` is not `None` and its shape doesn't match `values`,
or if either `metrics_collections` or `updates_collections` are not a list
or tuple.
"""
check_ops.assert_type(values, dtypes.bool)
count = _create_local('count', shape=[])
values = math_ops.to_float(values)
if weights is not None:
weights = math_ops.to_float(weights)
with ops.control_dependencies((_assert_weights_rank(weights, values),)):
values = math_ops.multiply(values, weights)
value_tensor = array_ops.identity(count)
update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))
if metrics_collections:
ops.add_to_collections(metrics_collections, value_tensor)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return value_tensor, update_op
示例11: false_negatives_at_thresholds
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def false_negatives_at_thresholds(labels, predictions, thresholds, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes false negatives at provided threshold values.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
thresholds: A python list or tuple of float thresholds in `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that `false_negatives`
should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
false_negatives: A float `Tensor` of shape `[len(thresholds)]`.
update_op: An operation that updates the `false_negatives` variable and
returns its current value.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(name, 'false_negatives',
(predictions, labels, weights)):
values, update_ops = _confusion_matrix_at_thresholds(
labels, predictions, thresholds, weights=weights, includes=('fn',))
if metrics_collections:
ops.add_to_collections(metrics_collections, values['fn'])
if updates_collections:
ops.add_to_collections(updates_collections, update_ops['fn'])
return values['fn'], update_ops['fn']
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:48,代码来源:metrics_impl.py
示例12: false_positives_at_thresholds
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def false_positives_at_thresholds(labels, predictions, thresholds, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes false positives at provided threshold values.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
thresholds: A python list or tuple of float thresholds in `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that `false_positives`
should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
false_positives: A float `Tensor` of shape `[len(thresholds)]`.
update_op: An operation that updates the `false_positives` variable and
returns its current value.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(name, 'false_positives',
(predictions, labels, weights)):
values, update_ops = _confusion_matrix_at_thresholds(
labels, predictions, thresholds, weights=weights, includes=('fp',))
if metrics_collections:
ops.add_to_collections(metrics_collections, values['fp'])
if updates_collections:
ops.add_to_collections(updates_collections, update_ops['fp'])
return values['fp'], update_ops['fp']
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:48,代码来源:metrics_impl.py
示例13: true_negatives_at_thresholds
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def true_negatives_at_thresholds(labels, predictions, thresholds, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes true negatives at provided threshold values.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
thresholds: A python list or tuple of float thresholds in `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that `true_negatives`
should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
true_negatives: A float `Tensor` of shape `[len(thresholds)]`.
update_op: An operation that updates the `true_negatives` variable and
returns its current value.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(name, 'true_negatives',
(predictions, labels, weights)):
values, update_ops = _confusion_matrix_at_thresholds(
labels, predictions, thresholds, weights=weights, includes=('tn',))
if metrics_collections:
ops.add_to_collections(metrics_collections, values['tn'])
if updates_collections:
ops.add_to_collections(updates_collections, update_ops['tn'])
return values['tn'], update_ops['tn']
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:48,代码来源:metrics_impl.py
示例14: true_positives_at_thresholds
# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import add_to_collections [as 别名]
def true_positives_at_thresholds(labels, predictions, thresholds, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes true positives at provided threshold values.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
thresholds: A python list or tuple of float thresholds in `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that `true_positives`
should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
true_positives: A float `Tensor` of shape `[len(thresholds)]`.
update_op: An operation that updates the `true_positives` variable and
returns its current value.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(name, 'true_positives',
(predictions, labels, weights)):
values, update_ops = _confusion_matrix_at_thresholds(
labels, predictions, thresholds, weights=weights, includes=('tp',))
if metrics_collections:
ops.add_to_collections(metrics_collections, values['tp'])
if updates_collections:
ops.add_to_collections(updates_collections, update_ops['tp'])
return values['tp'], update_ops['tp']
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:48,代码来源:metrics_impl.py