- jax.ops.segment_prod(data, segment_ids, num_segments=None, indices_are_sorted=False, unique_indices=False, bucket_size=None, mode=None)[source]#
Computes the product within segments of an array.
Similar to TensorFlow’s segment_prod
Any) – an array with the values to be reduced.
Any) – an array with integer dtype that indicates the segments of data (along its leading axis) to be reduced. Values can be repeated and need not be sorted. Values outside of the range [0, num_segments) are dropped and do not contribute to the result.
int]) – optional, an int with nonnegative value indicating the number of segments. The default is set to be the minimum number of segments that would support all indices in
segment_ids, calculated as
max(segment_ids) + 1. Since num_segments determines the size of the output, a static value must be provided to use
segment_prodin a JIT-compiled function.
bool) – whether
segment_idsis known to be sorted.
bool) – whether segment_ids is known to be free of duplicates.
int]) – size of bucket to group indices into.
segment_prodis performed on each bucket separately to improve numerical stability of addition. Default
Nonemeans no bucketing.
GatherScatterMode]) – a
jax.lax.GatherScatterModevalue describing how out-of-bounds indices should be handled. By default, values outside of the range [0, num_segments) are dropped and do not contribute to the sum.
- Return type:
An array with shape
(num_segments,) + data.shape[1:]representing the segment products.
Simple 1D segment product:
>>> data = jnp.arange(6) >>> segment_ids = jnp.array([0, 0, 1, 1, 2, 2]) >>> segment_prod(data, segment_ids) Array([ 0, 6, 20], dtype=int32)
Using JIT requires static num_segments:
>>> from jax import jit >>> jit(segment_prod, static_argnums=2)(data, segment_ids, 3) Array([ 0, 6, 20], dtype=int32)