本文整理匯總了Python中tensorflow.python.ops.gen_math_ops.cumsum方法的典型用法代碼示例。如果您正苦於以下問題：Python gen_math_ops.cumsum方法的具體用法？Python gen_math_ops.cumsum怎麽用？Python gen_math_ops.cumsum使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類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個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: cumsum
# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import cumsum [as 別名]
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) 示例2: cumsum
# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import cumsum [as 別名]
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) 示例3: cumsum
# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import cumsum [as 別名]
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) 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:51，代碼來源:math_ops.py