maxout¶
- paddle.nn.functional. maxout ( x, groups, axis=1, name=None ) [source]
-
maxout activation.
Assumed the input shape is (N, Ci, H, W). The output shape is (N, Co, H, W). Then Co = Ci/groups and the operator formula is as follows:
\[\begin{split}\begin{array}{l} &out_{si+j} = \max_{k} x_{gsi + sk + j} \\ &g = groups \\ &s = \frac{input.size}{num\_channels} \\ &0 \le i < \frac{num\_channels}{groups} \\ &0 \le j < s \\ &0 \le k < groups \end{array}\end{split}\]- Parameters
-
x (Tensor) – The input is 4-D Tensor with shape [N, C, H, W] or [N, H, W, C], the data type of input is float32 or float64.
groups (int, optional) – The groups number of maxout. groups specifies the index of channel dimension where maxout will be performed. This must be a factor of number of features. Default is 1.
axis (int, optional) – The axis along which to perform maxout calculations. It should be 1 when data format is NCHW, be -1 or 3 when data format is NHWC. If
axis< 0, it works the same way as \(axis + D\) , where D is the dimensions ofx.axisonly supports 1, 3 or -1. Default is 1.name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
- Returns
-
A Tensor with the same data type as
x.
Examples
import paddle import paddle.nn.functional as F x = paddle.rand([1, 2, 3, 4]) # [[[[0.5002636 0.22272532 0.17402348 0.2874594 ] # [0.95313174 0.6228939 0.7129065 0.7087491 ] # [0.02879342 0.88725346 0.61093384 0.38833922]] # [[0.5231306 0.03807496 0.91661984 0.15602879] # [0.666127 0.616567 0.30741522 0.24044901] # [0.7142536 0.7351477 0.31588817 0.23782359]]]] out = F.maxout(x, groups=2) # [[[[0.5231306 0.22272532 0.91661984 0.2874594 ] # [0.95313174 0.6228939 0.7129065 0.7087491 ] # [0.7142536 0.88725346 0.61093384 0.38833922]]]]