tf.control_dependencies和tf.Graph.control

tf.control_dependencies和tf.Graph.control_dependencies

2018-03-11 699

用来控制计算流图的，给图中的某些计算指定顺序

control_dependencies(control_inputs)

Wrapper (封装)for `Graph.control_dependencies()` using the default graph.

See @{tf.Graph.control_dependencies}

for more details.

Args:

control_inputs: A list of `Operation` or `Tensor` objects which

must be executed or computed before running the operations

defined in the context. Can also be `None` to clear the control

dependencies.

Returns:

A context manager that specifies control dependencies for all

operations constructed within the context.

control_dependencies(self, control_inputs)

Returns a context manager that specifies control dependencies.

Use with the `with` keyword to specify that all operations constructed

within the context should have control dependencies on

`control_inputs`. For example:

```python

with g.control_dependencies([a, b, c]):

# `d` and `e` will only run after `a`, `b`, and `c` have executed.

d = ...

e = ...

```

Multiple calls to `control_dependencies()` can be nested, and in

that case a new `Operation` will have control dependencies on the union

of `control_inputs` from all active contexts.

```python

with g.control_dependencies([a, b]):

# Ops constructed here run after `a` and `b`.

with g.control_dependencies([c, d]):

# Ops constructed here run after `a`, `b`, `c`, and `d`.

```

You can pass None to clear the control dependencies:

```python

with g.control_dependencies([a, b]):

# Ops constructed here run after `a` and `b`.

with g.control_dependencies(None):

# Ops constructed here run normally, not waiting for either `a` or `b`.

with g.control_dependencies([c, d]):

# Ops constructed here run after `c` and `d`, also not waiting

# for either `a` or `b`.

```

*N.B.* The control dependencies context applies *only* to ops that

are constructed within the context. Merely using an op or tensor

in the context does not add a control dependency. The following

example illustrates this point:

```python

# WRONG

def my_func(pred, tensor):

t = tf.matmul(tensor, tensor)

with tf.control_dependencies([pred]):

# The matmul op is created outside the context, so no control

# dependency will be added.

return t

# RIGHT

def my_func(pred, tensor):

with tf.control_dependencies([pred]):

# The matmul op is created in the context, so a control dependency

# will be added.

return tf.matmul(tensor, tensor)

```

Args:

control_inputs: A list of `Operation` or `Tensor` objects which

must be executed or computed before running the operations

defined in the context. Can also be `None` to clear the control

dependencies.

Returns:

A context manager that specifies control dependencies for all

operations constructed within the context.

Raises:

TypeError: If `control_inputs` is not a list of `Operation` or

`Tensor` objects.

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原文链接：https://yq.aliyun.com/articles/532404

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tf.assert_positive

assert_positive(x, data=None, summarize=None, message=None, name=None) Assert the condition `x > 0` holds element-wise. Example of adding a dependency to an operation: ```python with tf.control_dependencies([tf.assert_positive(x)]): output = tf.reduce_sum(x) ``` Positive means, for every element `x[i]` of `x`, we have `x[i] > 0`. If `x` is empty this is trivially satisfied. Args: x: Numeric `Tensor`. data: The tensors to print out if the condition is False....

2018-03-12

627

tf.control_dependencies(self, control_inputs) arguments：control_inputs: A list of `Operation` or `Tensor` objects which must be executed or computed before running the operations defined in the context. （注意这里control_inputs是list） return： A context manager that specifies control dependencies for all operations constructed within the context.（返回所有在环境中的控制依赖的上下文管理器）该方法可以控制操作（op）执行的顺序，不能为tensor tf.identity(input, name=None) Args: input: A Tensor. name: A name for the operation (optional). Returns:A t...

2018-03-12

686

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