# tf.contrib.framework.nest.map_structure_up_to

tf.contrib.framework.nest.map_structure_up_to(
shallow_tree,
func,
*inputs
)


Defined in tensorflow/python/util/nest.py.

Applies a function or op to a number of partially flattened inputs.

The inputs are flattened up to shallow_tree before being mapped.

Use Case:

Sometimes we wish to apply a function to a partially flattened sequence (for example when the function itself takes sequence inputs). We achieve this by specifying a shallow structure, shallow_tree we wish to flatten up to.

The inputs, can be thought of as having the same structure as shallow_tree, but with leaf nodes that are themselves tree structures.

This function therefore will return something with the same base structure as shallow_tree.

Examples:

ab_tuple = collections.namedtuple("ab_tuple", "a, b")
inp_val = ab_tuple(a=2, b=3)
out = map_structure_up_to(inp_val, lambda val, ops: (val + ops.add) * ops.mul,
inp_val, inp_ops)

# Output is: ab_tuple(a=6, b=15)

data_list = [[2, 4, 6, 8], [[1, 3, 5, 7, 9], [3, 5, 7]]]
name_list = ['evens', ['odds', 'primes']]
out = map_structure_up_to(
name_list,
lambda name, sec: "first_{}_{}".format(len(sec), name),
name_list, data_list)

# Output is: ['first_4_evens', ['first_5_odds', 'first_3_primes']]


#### Args:

• shallow_tree: a shallow tree, common to all the inputs.
• func: callable which will be applied to each input individually.
• *inputs: arbitrarily nested combination of objects that are compatible with shallow_tree. The function func is applied to corresponding partially flattened elements of each input, so the function must support arity of len(inputs).

#### Raises:

• TypeError: If shallow_tree is a sequence but input_tree is not.
• TypeError: If the sequence types of shallow_tree are different from input_tree.
• ValueError: If the sequence lengths of shallow_tree are different from input_tree.

#### Returns:

result of repeatedly applying func, with same structure as shallow_tree.