tf.scan( fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None )
Defined in tensorflow/python/ops/functional_ops.py
.
See the guide: Higher Order Functions > Higher Order Operators
scan on the list of tensors unpacked from elems
on dimension 0.
The simplest version of scan
repeatedly applies the callable fn
to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems
on dimension 0. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invocation of fn. If initializer
is None, elems
must contain at least one element, and its first element is used as the initializer.
Suppose that elems
is unpacked into values
, a list of tensors. The shape of the result tensor is [len(values)] + fn(initializer, values[0]).shape
.
This method also allows multi-arity elems
and accumulator. If elems
is a (possibly nested) list or tuple of tensors, then each of these tensors must have a matching first (unpack) dimension. The second argument of fn
must match the structure of elems
.
If no initializer
is provided, the output structure and dtypes of fn
are assumed to be the same as its input; and in this case, the first argument of fn
must match the structure of elems
.
If an initializer
is provided, then the output of fn
must have the same structure as initializer
; and the first argument of fn
must match this structure.
For example, if elems
is (t1, [t2, t3])
and initializer
is [i1, i2]
then an appropriate signature for fn
in python2
is: fn = lambda (acc_p1, acc_p2), (t1, [t2, t3]):
and fn
must return a list, [acc_n1, acc_n2]
. An alternative correct signature for fn
, and the one that works in python3
, is: fn = lambda a, t:
, where a
and t
correspond to the input tuples.
fn
: The callable to be performed. It accepts two arguments. The first will have the same structure as initializer
if one is provided, otherwise it will have the same structure as elems
. The second will have the same (possibly nested) structure as elems
. Its output must have the same structure as initializer
if one is provided, otherwise it must have the same structure as elems
.elems
: A tensor or (possibly nested) sequence of tensors, each of which will be unpacked along their first dimension. The nested sequence of the resulting slices will be the first argument to fn
.initializer
: (optional) A tensor or (possibly nested) sequence of tensors, initial value for the accumulator, and the expected output type of fn
.parallel_iterations
: (optional) The number of iterations allowed to run in parallel.back_prop
: (optional) True enables support for back propagation.swap_memory
: (optional) True enables GPU-CPU memory swapping.infer_shape
: (optional) False disables tests for consistent output shapes.name
: (optional) Name prefix for the returned tensors.A tensor or (possibly nested) sequence of tensors. Each tensor packs the results of applying fn
to tensors unpacked from elems
along the first dimension, and the previous accumulator value(s), from first to last.
TypeError
: if fn
is not callable or the structure of the output of fn
and initializer
do not match.ValueError
: if the lengths of the output of fn
and initializer
do not match.Examples:
elems = np.array([1, 2, 3, 4, 5, 6]) sum = scan(lambda a, x: a + x, elems) # sum == [1, 3, 6, 10, 15, 21] elems = np.array([1, 2, 3, 4, 5, 6]) initializer = np.array(0) sum_one = scan( lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer) # sum_one == [1, 2, 3, 4, 5, 6] elems = np.array([1, 0, 0, 0, 0, 0]) initializer = (np.array(0), np.array(1)) fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer) # fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/scan