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Python gen_math_ops.cumsum方法代码示例

本文整理汇总了Python中tensorflow.python.ops.gen_math_ops.cumsum方法的典型用法代码示例。如果您正苦于以下问题:Python gen_math_ops.cumsum方法的具体用法?Python gen_math_ops.cumsum怎么用?Python gen_math_ops.cumsum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tensorflow.python.ops.gen_math_ops的用法示例。


在下文中一共展示了gen_math_ops.cumsum方法的3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: cumsum

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cumsum [as 别名]
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
  """Compute the cumulative sum of the tensor `x` along `axis`.

  By default, this op performs an inclusive cumsum, which means that the first
  element of the input is identical to the first element of the output:
  ```prettyprint
  tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c]
  ```

  By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
  instead:
  ```prettyprint
  tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b]
  ```

  By setting the `reverse` kwarg to `True`, the cumsum is performed in the
  opposite direction:
  ```prettyprint
  tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c]
  ```
  This is more efficient than using separate `tf.reverse` ops.

  The `reverse` and `exclusive` kwargs can also be combined:
  ```prettyprint
  tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0]
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
       `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
       `complex128`, `qint8`, `quint8`, `qint32`, `half`.
    axis: A `Tensor` of type `int32` (default: 0).
    exclusive: If `True`, perform exclusive cumsum.
    reverse: A `bool` (default: False).
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  with ops.name_scope(name, "Cumsum", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops.cumsum(
        x, axis, exclusive=exclusive, reverse=reverse, name=name) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:45,代码来源:math_ops.py

示例2: cumsum

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cumsum [as 别名]
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
  """Compute the cumulative sum of the tensor `x` along `axis`.

  By default, this op performs an inclusive cumsum, which means that the first
  element of the input is identical to the first element of the output:
  ```prettyprint
  tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c]
  ```

  By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
  instead:
  ```prettyprint
  tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b]
  ```

  By setting the `reverse` kwarg to `True`, the cumsum is performed in the
  opposite direction:
  ```prettyprint
  tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c]
  ```
  This is more efficient than using separate `tf.reverse` ops.

  The `reverse` and `exclusive` kwargs can also be combined:
  ```prettyprint
  tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0]
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
       `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
       `complex128`, `qint8`, `quint8`, `qint32`, `half`.
       axis: A `Tensor` of type `int32` (default: 0).
       reverse: A `bool` (default: False).
       name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  with ops.name_scope(name, "Cumsum", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops.cumsum(
        x, axis, exclusive=exclusive, reverse=reverse, name=name) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:44,代码来源:math_ops.py

示例3: cumsum

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cumsum [as 别名]
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
  """Compute the cumulative sum of the tensor `x` along `axis`.

  By default, this op performs an inclusive cumsum, which means that the first
  element of the input is identical to the first element of the output:

  ```python
  tf.cumsum([a, b, c])  # [a, a + b, a + b + c]
  ```

  By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
  instead:

  ```python
  tf.cumsum([a, b, c], exclusive=True)  # [0, a, a + b]
  ```

  By setting the `reverse` kwarg to `True`, the cumsum is performed in the
  opposite direction:

  ```python
  tf.cumsum([a, b, c], reverse=True)  # [a + b + c, b + c, c]
  ```

  This is more efficient than using separate `tf.reverse` ops.

  The `reverse` and `exclusive` kwargs can also be combined:

  ```python
  tf.cumsum([a, b, c], exclusive=True, reverse=True)  # [b + c, c, 0]
  ```

  Args:
    x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
       `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
       `complex128`, `qint8`, `quint8`, `qint32`, `half`.
    axis: A `Tensor` of type `int32` (default: 0). Must be in the range
      `[-rank(x), rank(x))`.
    exclusive: If `True`, perform exclusive cumsum.
    reverse: A `bool` (default: False).
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `x`.
  """
  with ops.name_scope(name, "Cumsum", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops.cumsum(
        x, axis, exclusive=exclusive, reverse=reverse, name=name) 
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:51,代码来源:math_ops.py


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