tf.math.cumsum  |  TensorFlow v2.16.1 (original) (raw)

tf.math.cumsum

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Compute the cumulative sum of the tensor x along axis.

View aliases

Main aliases

tf.cumsum

Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.cumsum, tf.compat.v1.math.cumsum

tf.math.cumsum(
    x, axis=0, exclusive=False, reverse=False, name=None
)

Used in the notebooks

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: For example:

# tf.cumsum([a, b, c]) # [a, a + b, a + b + c] x = tf.constant([2, 4, 6, 8]) tf.cumsum(x) <tf.Tensor: shape=(4,), dtype=int32, numpy=array([ 2, 6, 12, 20], dtype=int32)>

# using varying `axis` values y = tf.constant([[2, 4, 6, 8], [1,3,5,7]]) tf.cumsum(y, axis=0) <tf.Tensor: shape=(2, 4), dtype=int32, numpy= array([[ 2, 4, 6, 8], [ 3, 7, 11, 15]], dtype=int32)> tf.cumsum(y, axis=1) <tf.Tensor: shape=(2, 4), dtype=int32, numpy= array([[ 2, 6, 12, 20], [ 1, 4, 9, 16]], dtype=int32)>

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

# tf.cumsum([a, b, c], exclusive=True) => [0, a, a + b] x = tf.constant([2, 4, 6, 8]) tf.cumsum(x, exclusive=True) <tf.Tensor: shape=(4,), dtype=int32, numpy=array([ 0, 2, 6, 12], dtype=int32)>

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] x = tf.constant([2, 4, 6, 8]) tf.cumsum(x, reverse=True) <tf.Tensor: shape=(4,), dtype=int32, numpy=array([20, 18, 14, 8], dtype=int32)>

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] x = tf.constant([2, 4, 6, 8]) tf.cumsum(x, exclusive=True, reverse=True) <tf.Tensor: shape=(4,), dtype=int32, numpy=array([18, 14, 8, 0], dtype=int32)>