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| ``mx.nd.Pooling.v1`` |
| ======================================== |
| |
| Description |
| ---------------------- |
| |
| This operator is DEPRECATED. |
| Perform pooling on the input. |
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| The shapes for 2-D pooling is |
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| - **data**: *(batch_size, channel, height, width)* |
| - **out**: *(batch_size, num_filter, out_height, out_width)*, with:: |
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| out_height = f(height, kernel[0], pad[0], stride[0]) |
| out_width = f(width, kernel[1], pad[1], stride[1]) |
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| The definition of *f* depends on ``pooling_convention``, which has two options: |
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| - **valid** (default):: |
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| f(x, k, p, s) = floor((x+2*p-k)/s)+1 |
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| - **full**, which is compatible with Caffe:: |
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| f(x, k, p, s) = ceil((x+2*p-k)/s)+1 |
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| But ``global_pool`` is set to be true, then do a global pooling, namely reset |
| ``kernel=(height, width)``. |
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| Three pooling options are supported by ``pool_type``: |
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| - **avg**: average pooling |
| - **max**: max pooling |
| - **sum**: sum pooling |
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| 1-D pooling is special case of 2-D pooling with *weight=1* and |
| *kernel[1]=1*. |
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| For 3-D pooling, an additional *depth* dimension is added before |
| *height*. Namely the input data will have shape *(batch_size, channel, depth, |
| height, width)*. |
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| Arguments |
| ------------------ |
| |
| +----------------------------------------+------------------------------------------------------------+ |
| | Argument | Description | |
| +========================================+============================================================+ |
| | ``data`` | NDArray-or-Symbol. | |
| | | | |
| | | Input data to the pooling operator. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``kernel`` | Shape(tuple), optional, default=[]. | |
| | | | |
| | | pooling kernel size: (y, x) or (d, y, x) | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``pool.type`` | {'avg', 'max', 'sum'},optional, default='max'. | |
| | | | |
| | | Pooling type to be applied. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``global.pool`` | boolean, optional, default=0. | |
| | | | |
| | | Ignore kernel size, do global pooling based on current | |
| | | input feature | |
| | | map. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``pooling.convention`` | {'full', 'valid'},optional, default='valid'. | |
| | | | |
| | | Pooling convention to be applied. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``stride`` | Shape(tuple), optional, default=[]. | |
| | | | |
| | | stride: for pooling (y, x) or (d, y, x) | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``pad`` | Shape(tuple), optional, default=[]. | |
| | | | |
| | | pad for pooling: (y, x) or (d, y, x) | |
| +----------------------------------------+------------------------------------------------------------+ |
| |
| Value |
| ---------- |
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| ``out`` The result mx.ndarray |
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| Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/pooling_v1.cc#L104 |
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