tf_map

`tf.map_fn()`

Description

Thin wrapper around tf.map_fn() with the following differences:

accepts purrr style ~ lambda syntax to define function fn.
The order of elems and fn is switched to make it more pipe %>% friendly and consistent with R mappers lapply() and purrr::map().

Usage

 
tf_map( 
  elems, 
  fn, 
  dtype = NULL, 
  parallel_iterations = NULL, 
  back_prop = TRUE, 
  swap_memory = FALSE, 
  infer_shape = TRUE, 
  name = NULL 
)

Arguments

Arguments	Description
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`.
fn	An R function, specified using `purrr` style ~ syntax, a character string, a python function (or more generally, any python object with a `__call__` method) or anything coercible via `as.function()`. The function will be be called with one argument, which will have the same (possibly nested) structure as `elems`. Its output must return the same structure as `dtype` if one is provided, otherwise it must return the same structure as `elems`.
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. When graph building, the default value is 10. While executing eagerly, the default value is set to 1.
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.

Value

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, from first to last.