tensorflow API:tf.map_fn
tf.map_fn(
fn,
elems,
dtype=None,
parallel_iterations=10,
back_prop=True,
swap_memory=False,
infer_shape=True,
name=None
)
作用:map on the list of tensors unpacked from elems
on dimension 0.
引數:
fn
: The callable to be performed. It accepts one argument, which will have the same (possibly nested) structure as elems
dtype
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 applied to fn
dtype
: (optional) The output type(s) of fn
. If fn
returns a structure of Tensors differing from the structure of elems
, then dtype
is not optional and must have the same structure as the output of fn
.
parallel_iterations
: (optional) The number of iterations allowed to run in parallel.
back_prop
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.
官網例子:
1.
elems = np.array([1, 2, 3, 4, 5, 6])
squares = map_fn(lambda x: x * x, elems)
# squares == [1, 4, 9, 16, 25, 36]
elems = (np.array([1, 2, 3]), np.array([-1, 1, -1]))
alternate = map_fn(lambda x: x[0] * x[1], elems, dtype=tf.int64)
# alternate == [-1, 2, -3]
elems = np.array([1, 2, 3])
alternates = map_fn(lambda x: (x, -x), elems, dtype=(tf.int64, tf.int64))
# alternates[0] == [1, 2, 3]
# alternates[1] == [-1, -2, -3]