from chainer.functions.array import reshape
from chainer.functions.math import minmax
from chainer.utils import type_check
[docs]def maxout(x, pool_size, axis=1):
"""Maxout activation function.
It accepts an input tensor ``x``, reshapes the ``axis`` dimension
(say the size being ``M * pool_size``) into two dimensions
``(M, pool_size)``, and takes maximum along the ``axis`` dimension.
Args:
x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`):
Input variable. A :math:`n`-dimensional (:math:`n \\ge` ``axis``)
float array. In general, its first dimension is assumed to be the
*minibatch dimension*. The other dimensions are treated as one
concatenated dimension.
pool_size (int):
The size used for downsampling of pooling layer.
axis (int):
The ``axis`` dimension to be reshaped. The size of ``axis``
dimension should be ``M * pool_size``.
Returns:
~chainer.Variable:
Output variable. The shape of the output is same as ``x`` except
that ``axis`` dimension is transformed from ``M * pool_size`` to
``M``.
.. seealso:: :class:`~chainer.links.Maxout`
.. admonition:: Example
Typically, ``x`` is the output of a linear layer or a convolution
layer. The following is the example where we use :func:`maxout` in
combination with a Linear link.
>>> in_size, out_size, pool_size = 10, 10, 10
>>> bias = np.arange(out_size * pool_size).astype('f')
>>> l = L.Linear(in_size, out_size * pool_size, initial_bias=bias)
>>> x = np.zeros((1, in_size), 'f') # prepare data
>>> x = l(x)
>>> y = F.maxout(x, pool_size)
>>> x.shape
(1, 100)
>>> y.shape
(1, 10)
>>> x.reshape((out_size, pool_size)).data
array([[ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],
[ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.],
[ 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.],
[ 30., 31., 32., 33., 34., 35., 36., 37., 38., 39.],
[ 40., 41., 42., 43., 44., 45., 46., 47., 48., 49.],
[ 50., 51., 52., 53., 54., 55., 56., 57., 58., 59.],
[ 60., 61., 62., 63., 64., 65., 66., 67., 68., 69.],
[ 70., 71., 72., 73., 74., 75., 76., 77., 78., 79.],
[ 80., 81., 82., 83., 84., 85., 86., 87., 88., 89.],
[ 90., 91., 92., 93., 94., 95., 96., 97., 98., 99.]], \
dtype=float32)
>>> y.data
array([[ 9., 19., 29., 39., 49., 59., 69., 79., 89., 99.]], \
dtype=float32)
"""
if pool_size <= 0:
raise ValueError('pool_size must be a positive integer.')
x_shape = x.shape
if x_shape[axis] % pool_size != 0:
expect = 'x.shape[axis] % pool_size == 0'
actual = 'x.shape[axis]={}, pool_size={}'.format(
x_shape[axis], pool_size)
msg = 'axis dimension must be divided by pool_size'
raise type_check.InvalidType(expect, actual, msg)
shape = (x_shape[:axis] +
(x_shape[axis] // pool_size, pool_size) +
x_shape[axis + 1:])
x = reshape.reshape(x, shape)
return minmax.max(x, axis=axis + 1)