tf.math.cumsum
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Compute the cumulative sum of the tensor x
along axis
.
tf.math.cumsum(
x, axis=0, exclusive=False, reverse=False, name=None
)
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:
tf.cumsum([a, b, c]) # [a, a + b, a + b + c]
By setting the exclusive
kwarg to True
, an exclusive cumsum is performed
instead:
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:
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:
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 .
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.math.cumsum\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/math/cumsum) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.1.0/tensorflow/python/ops/math_ops.py#L3286-L3335) |\n\nCompute the cumulative sum of the tensor `x` along `axis`.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.cumsum`](/api_docs/python/tf/math/cumsum)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.cumsum`](/api_docs/python/tf/math/cumsum), [`tf.compat.v1.math.cumsum`](/api_docs/python/tf/math/cumsum)\n\n\u003cbr /\u003e\n\n tf.math.cumsum(\n x, axis=0, exclusive=False, reverse=False, name=None\n )\n\nBy default, this op performs an inclusive cumsum, which means that the first\nelement of the input is identical to the first element of the output: \n\n tf.cumsum([a, b, c]) # [a, a + b, a + b + c]\n\nBy setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed\ninstead: \n\n tf.cumsum([a, b, c], exclusive=True) # [0, a, a + b]\n\nBy setting the `reverse` kwarg to `True`, the cumsum is performed in the\nopposite direction: \n\n tf.cumsum([a, b, c], reverse=True) # [a + b + c, b + c, c]\n\nThis is more efficient than using separate [`tf.reverse`](../../tf/reverse) ops.\n\nThe `reverse` and `exclusive` kwargs can also be combined: \n\n tf.cumsum([a, b, c], exclusive=True, reverse=True) # [b + c, c, 0]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `x` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`. |\n| `axis` | A `Tensor` of type `int32` (default: 0). Must be in the range `[-rank(x), rank(x))`. |\n| `exclusive` | If `True`, perform exclusive cumsum. |\n| `reverse` | A `bool` (default: False). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `x`. ||\n\n\u003cbr /\u003e"]]