chainer.dataset.concat_examples(batch, device=None, padding=None)[source]

Concatenates a list of examples into array(s).

This function converts an “array of tuples” into a “tuple of arrays”. Specifically, given a list of examples each of which consists of a list of elements, this function first makes an array by taking the element in the same position from each example and concatenates them along the newly-inserted first axis (called batch dimension) into one array. It repeats this for all positions and returns the resulting arrays.

The output type depends on the type of examples in batch. For instance, consider each example consists of two arrays (x, y). Then, this function concatenates x ‘s into one array, and y ‘s into another array, and returns a tuple of these two arrays. Another example: consider each example is a dictionary of two entries whose keys are 'x' and 'y', respectively, and values are arrays. Then, this function concatenates x ‘s into one array, and y ‘s into another array, and returns a dictionary with two entries x and y whose values are the concatenated arrays.

When the arrays to concatenate have different shapes, the behavior depends on the padding value. If padding is None (default), it raises an error. Otherwise, it builds an array of the minimum shape that the contents of all arrays can be substituted to. The padding value is then used to the extra elements of the resulting arrays.


>>> import numpy as np
>>> from chainer import dataset
>>> x = [([1, 2], 1),
...      ([3, 4], 2),
...      ([5, 6], 3)]
>>> dataset.concat_examples(x)
(array([[1, 2],
       [3, 4],
       [5, 6]]), array([1, 2, 3]))
>>> y = [(np.array([1, 2]), 0),
...      (np.array([3]), 1),
...      (np.array([]), 2)]
>>> dataset.concat_examples(y, padding=100)
(array([[  1,   2],
       [  3, 100],
       [100, 100]]), array([0, 1, 2]))
>>> z = [(np.array([1, 2]), np.array([0])),
...      (np.array([3]), np.array([])),
...      (np.array([]), np.array([2]))]
>>> dataset.concat_examples(z, padding=(100, 200))
(array([[  1,   2],
       [  3, 100],
       [100, 100]]), array([[  0],
       [  2]]))
>>> w = [{'feature': np.array([1, 2]), 'label': 0},
...      {'feature': np.array([3, 4]), 'label': 1},
...      {'feature': np.array([5, 6]), 'label': 2}]
>>> dataset.concat_examples(w)  # doctest: +SKIP
{'feature': array([[1, 2],
       [3, 4],
       [5, 6]]), 'label': array([0, 1, 2])}
  • batch (list) – A list of examples. This is typically given by a dataset iterator.

  • device (device specifier) – A device to which each array is sent. If it is omitted, all arrays are left in their original devices. See to_device() for more details.

  • padding – Scalar value for extra elements. If this is None (default), an error is raised on shape mismatch. Otherwise, an array of minimum dimensionalities that can accommodate all arrays is created, and elements outside of the examples are padded by this value.


Array, a tuple of arrays, or a dictionary of arrays. The type depends on the type of each example in the batch.