class_docstr: PoolLayer parameter object.
  This then is passed as an argument to all pooling kernels.
  
  op: max, avg, l2 pooling
  N: Number of images in mini-batch
  
  C: Number of input feature maps
  D: Depth  of input image
  H: Height of input image
  W: Width  of input image
  
  J: Size of feature map pooling window (maxout n_pieces)
  T: Depth  of pooling window
  R: Height of pooling window
  S: Width  of pooling window
  
  padding: amount of zero-padding around the given image or feature map edge
  strides: factor to step the window by in a given direction (overlap allowed)
  
  Leave spatial dimensions at 1 to allow feature map pooling in the fc layers.
class_docstr_tok: ["PoolLayer","parameter","object",".","This","then","is","passed","as","an","argument","to","all","pooling","kernels",".","op",":","max",",","avg",",","l2","pooling","N",":","Number","of","images","in","mini-batch","C",":","Number","of","input","feature","maps","D",":","Depth","of","input","image","H",":","Height","of","input","image","W",":","Width","of","input","image","J",":","Size","of","feature","map","pooling","window","(","maxout","n_pieces",")","T",":","Depth","of","pooling","window","R",":","Height","of","pooling","window","S",":","Width","of","pooling","window","padding",":","amount","of","zero-padding","around","the","given","image","or","feature","map","edge","strides",":","factor","to","step","the","window","by","in","a","given","direction","(","overlap","allowed",")","Leave","spatial","dimensions","at","1","to","allow","feature","map","pooling","in","the","fc","layers","."]
code: def __init__(
      self,
      lib,
      dtype,
      op,
      N,
      C,
      D=1,
      H=1,
      W=1,
      J=1,
      T=1,
      R=1,
      S=1,
      pad_c=0,
      pad_d=0,
      pad_h=0,
      pad_w=0,
      str_c=None,
      str_d=None,
      str_h=None,
      str_w=None,
  ):
      super(PoolLayerMKL, self).__init__(
          lib,
          dtype,
          op,
          N,
          C,
          D,
          H,
          W,
          J,
          T,
          R,
          S,
          pad_c,
          pad_d,
          pad_h,
          pad_w,
          str_c,
          str_d,
          str_h,
          str_w,
      )
      self.dnnPrimitives = np.zeros((1, 20), dtype=np.uint64)
      self.initOk_f = 0
      self.initOk_b = 0
      self.is_mklop = True
  
imports: from __future__ import division
  import numpy
  from ctypes import c_longlong, c_float
  from neon.backends.layer_cpu import ConvLayer, DeconvLayer, PoolLayer
label_desc: Use image node_id 1 to create a new PoolLayerMKL object from inherited base classes: PoolLayer with example: obj = PoolLayerMKL(lib, dtype, op, N, C, D, H, W, J, T, R, S, pad_c, pad_d, pad_h, pad_w, str_c, str_d, str_h, str_w)