TensorFlow 2 version
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View source on GitHub
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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)).
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exclusive
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If True, perform exclusive cumsum.
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reverse
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A bool (default: False).
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name
|
A name for the operation (optional). |
Returns | |
|---|---|
A Tensor. Has the same type as x.
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TensorFlow 2 version
View source on GitHub