chainer.functions.maxout

chainer.functions.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.

Parameters:

• x (Variable or numpy.ndarray or cupy.ndarray) – Input variable. A \(n\)-dimensional (\(n \ge\) 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:

Output variable. The shape of the output is same as x except that axis dimension is transformed from M * pool_size to M.

Return type:

Variable

See also

Maxout

Example

Typically, x is the output of a linear layer or a convolution layer. The following is the example where we use 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)