tf.Operation

参考 tf.Operation - 云+社区 - 腾讯云

Class `Operation`

Represents a graph node that performs computation on tensors.

Aliases:

Class tf.compat.v1.Oeration
Class tf.compat.v2.Operation

An Operation is a node in a TensorFlow Graph that takes zero or more Tensor objects as input, and produces zero or more Tensor objects as output. Objects of type Operation are created by calling a Python op constructor (such as tf.matmul) or tf.Graph.create_op.For example c = tf.matmul(a, b) creates an Operation of type "MatMul" that takes tensors a and b as input, and produces c as output.After the graph has been launched in a session, an Operation can be executed by passing it to tf.Session.run. op.run() is a shortcut for calling tf.compat.v1.get_default_session().run(op).

`init`

View source

__init__(node_def,g,inputs=None,output_types=None,control_inputs=None,input_types=None,original_op=None,op_def=None
)

Creates an Operation.

NOTE: This constructor validates the name of the Operation (passed as node_def.name). Valid Operation names match the following regular expression:

[A-Za-z0-9.][A-Za-z0-9_.\\-/]*

Args:

node_def: node_def_pb2.NodeDef. NodeDef for the Operation. Used for attributes of node_def_pb2.NodeDef, typically name, op, and device. The input attribute is irrelevant here as it will be computed when generating the model.
g: Graph. The parent graph.
inputs: list of Tensor objects. The inputs to this Operation.
output_types: list of DType objects. List of the types of the Tensors computed by this operation. The length of this list indicates the number of output endpoints of the Operation.
control_inputs: list of operations or tensors from which to have a control dependency.
input_types: List of DType objects representing the types of the tensors accepted by the Operation. By default uses [x.dtype.base_dtype for x in inputs]. Operations that expect reference-typed inputs must specify these explicitly.
original_op: Optional. Used to associate the new Operation with an existing Operation (for example, a replica with the op that was replicated).
op_def: Optional. The op_def_pb2.OpDef proto that describes the op type that this Operation represents.

Raises:

TypeError: if control inputs are not Operations or Tensors, or if node_def is not a NodeDef, or if g is not a Graph, or if inputs are not tensors, or if inputs and input_types are incompatible.
ValueError: if the node_def name is not valid.

Properties

`control_inputs`

The Operation objects on which this op has a control dependency.

Before this op is executed, TensorFlow will ensure that the operations in self.control_inputs have finished executing. This mechanism can be used to run ops sequentially for performance reasons, or to ensure that the side effects of an op are observed in the correct order.

Returns:

A list of Operation objects.

`device`

The name of the device to which this op has been assigned, if any.

Returns:

The string name of the device to which this op has been assigned, or an empty string if it has not been assigned to a device.

`graph`

The Graph that contains this operation.

`inputs`

The list of Tensor objects representing the data inputs of this op.

`name`

The full name of this operation.

`node_def`

Returns the NodeDef representation of this operation.

Returns:

A NodeDef protocol buffer.

`op_def`

Returns the OpDef proto that represents the type of this op.

Returns:

An OpDef protocol buffer.

`outputs`

The list of Tensor objects representing the outputs of this op.

`traceback`

Returns the call stack from when this operation was constructed.

`1traceback_with_start_lines`

Same as traceback but includes start line of function definition.

Returns:

A list of 5-tuples (filename, lineno, name, code, func_start_lineno).

`type`

The type of the op (e.g. "MatMul").

2、Methods

`1、colocation_groups`

View source

colocation_groups()

Returns the list of colocation groups of the op.

`2、get_attr`

View source

get_attr(name)

Returns the value of the attr of this op with the given name.

Args:

name: The name of the attr to fetch.

Returns:

The value of the attr, as a Python object.

Raises:

ValueError: If this op does not have an attr with the given name.

`3、run`

View source

run(feed_dict=None,session=None
)

Runs this operation in a Session.

Calling this method will execute all preceding operations that produce the inputs needed for this operation.

N.B. Before invoking Operation.run(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly.

Args:

feed_dict: A dictionary that maps Tensor objects to feed values. See tf.Session.run for a description of the valid feed values.
session: (Optional.) The Session to be used to run to this operation. If none, the default session will be used.

`4、values`

View source

values()

DEPRECATED: Use outputs.

tf.Operation相关推荐

python tensorflow tf.Session().run()函数（运行操作并评估“fetches”中的张量）
参考文章:TensorFlow-sess.run() 当我们构建完图(可能是我们pre_process后生成的图片?NoNoNo,它只是指tensorflow框架的一种设计理念--计算流图)后,需要在 ...
TensorFlow 学习（七） — 常用函数 api、tf.nn、tf.keras
0. 四则运算平方:tf.square(),开方:tf.sqrt() tf.add().tf.sub().tf.mul().tf.div().tf.mod().tf.abs().tf.neg() 1 ...
批标准化 tf.keras.layers.BatchNormalization 中的trainable参数与training参数比较
巨坑提醒:tf.keras与tensorflow混用,trainable=False根本不起作用.正文不用看了. 摘要: 在tensorflow中,training参数和trainable参数是两个不 ...
批标准化 tf.keras.layers.BatchNormalization 参数解析与应用分析
Table of Contents 函数调用设置training=None时可能存在的问题 :tf.keras.backend.learning_phase()的特点批标准化函数产生的变量是可训练 ...
tf.reset_default_graph()
tf.reset_default_graph函数用于清除默认图形堆栈并重置全局默认图形. 注意:默认图形是当前线程的一个属性.该tf.reset_default_graph函数只适用于当前线程.当一个 ...
Dive into TensorFlow - 解析 TF 核心抽象 op 算子
TF 计算图从逻辑层来讲,由 op 与 tensor 构成.op 是项点代表计算单元,tensor 是边代表 op 之间流动的数据内容,两者配合以数据流图的形式来表达计算图.那么 op 对应的物理层实 ...
对于operation操作的理解
#如果存在函数复用的情况比如add用了好几次那么后面的operation名字是什么样子的呢 import tensorflow as tf def calu(a,b):out = tf.add(a ...
TensorFlow算子融合
TensorFlow算子融合 • TensorFlow的特点: o 真正的可移植性 o 引入各种计算设备的支持,包括CPU,GPU,以及能够很好的运行在各种系统的移动端 o 多语言支持 o 支持C++ ...
TensorFlow之会话
TensorFlow之会话 1.1 会话一个运行TensorFlow operation的类.会话包含以下两种开启方式 tf.Session:用于完整的程序当中 [如在pycharm中写代码 ...

tf.Operation

参考 tf.Operation - 云+社区 - 腾讯云

Class `Operation`

`init`

Properties

`control_inputs`

`device`

`graph`

`inputs`

`name`

`node_def`

`op_def`

`outputs`

`traceback`

`1traceback_with_start_lines`

`type`

2、Methods

`1、colocation_groups`

`2、get_attr`

`3、run`

`4、values`

tf.Operation相关推荐

最新文章

热门文章

tf.Operation

参考 tf.Operation - 云+社区 - 腾讯云

Class Operation

__init__

Properties

control_inputs

device

graph

inputs

name

node_def

op_def

outputs

traceback

1traceback_with_start_lines

type

2、Methods

1、colocation_groups

2、get_attr

3、run

4、values

tf.Operation相关推荐

最新文章

热门文章

Class `Operation`

`init`

`control_inputs`

`device`

`graph`

`inputs`

`name`

`node_def`

`op_def`

`outputs`

`traceback`

`1traceback_with_start_lines`

`type`

`1、colocation_groups`

`2、get_attr`

`3、run`

`4、values`