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map on the list of tensors unpacked from elems on dimension 0.

tf.map_fn(    fn,    elems,    dtype=None,    parallel_iterations=None,    back_prop=True,    swap_memory=False,    infer_shape=True,    name=None)

The simplest version of map_fn repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems. dtype is the data type of the return value of fn. Users must provide dtype if it is different from the data type of elems.

Suppose that elems is unpacked into values, a list of tensors. The shape of the result tensor is [values.shape[0]] + fn(values[0]).shape.

This method also allows multi-arity elems and output of fn. If elems is a (possibly nested) list or tuple of tensors, then each of these tensors must have a matching first (unpack) dimension. The signature of fn may match the structure of elems. That is, if elems is (t1, [t2, t3, [t4, t5]]), then an appropriate signature for fn is: fn = lambda (t1, [t2, t3, [t4, t5]]):.

Furthermore, fn may emit a different structure than its input. For example, fn may look like: fn = lambda t1: return (t1 + 1, t1 - 1). In this case, the dtype parameter is not optional: dtype must be a type or (possibly nested) tuple of types matching the output of fn.

To apply a functional operation to the nonzero elements of a SparseTensor one of the following methods is recommended. First, if the function is expressible as TensorFlow ops, use

  result = SparseTensor(input.indices, fn(input.values), input.dense_shape)

If, however, the function is not expressible as a TensorFlow op, then use

result = SparseTensor(  input.indices, map_fn(fn, input.values), input.dense_shape)

instead.

When executing eagerly, map_fn does not execute in parallel even if parallel_iterations is set to a value > 1. You can still get the performance benefits of running a function in parallel by using the tf.contrib.eager.defun decorator,

# Assume the function being used in map_fn is fn.# To ensure map_fn calls fn in parallel, use the defun decorator.@tf.contrib.eager.defundef func(tensor):  return tf.map_fn(fn, tensor)

Note that if you use the defun decorator, any non-TensorFlow Python code that you may have written in your function won't get executed. See tf.contrib.eager.defun for more details. The recommendation would be to debug without defun but switch to defun to get performance benefits of running map_fn in parallel.

Args:

• fn: The callable to be performed. It accepts one argument, which will have the same (possibly nested) structure as elems. Its output must have the same structure as 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. 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.

Returns:

• 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.

Raises:

• TypeError: if fn is not callable or the structure of the output of fn and dtype do not match, or if elems is a SparseTensor.
• ValueError: if the lengths of the output of fn and dtype do not match.

Examples:

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]

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