Class: DNN::Layers::Pool2D

Inherits:
Layer
  • Object
show all
Includes:
Conv2DUtils
Defined in:
lib/dnn/core/cnn_layers.rb

Overview

Super class of all pooling2D class.

Direct Known Subclasses

AvgPool2D, MaxPool2D

Instance Attribute Summary collapse

Attributes inherited from Layer

#input_shape, #name

Instance Method Summary collapse

Methods inherited from Layer

#backward, #built?, #call, call, #forward, from_hash

Constructor Details

#initialize(pool_size, strides: nil, padding: false) ⇒ Pool2D

Returns a new instance of Pool2D.

Parameters:

  • pool_size (Array | Integer) —

    Pooling size. Pooling size is of the form [height, width].

  • strides (Array | Integer | NilClass) (defaults to: nil) —

    Stride length. Stride length is of the form [height, width]. If you set nil, treat pool_size as strides.

  • padding (Array | Boolean) (defaults to: false) —

    Padding size or whether to padding. Padding size is of the form [height, width].



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# File 'lib/dnn/core/cnn_layers.rb', line 304

def initialize(pool_size, strides: nil, padding: false)
  super()
  @pool_size = pool_size.is_a?(Integer) ? [pool_size, pool_size] : pool_size
  @strides = if strides
               strides.is_a?(Integer) ? [strides, strides] : strides
             else
               @pool_size.clone
             end
  @padding = padding.is_a?(Integer) ? [padding, padding] : padding
end

Instance Attribute Details

#padding ⇒ Object (readonly)

Returns the value of attribute padding.



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# File 'lib/dnn/core/cnn_layers.rb', line 298

def padding
  @padding
end

#pool_size ⇒ Object (readonly)

Returns the value of attribute pool_size.



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# File 'lib/dnn/core/cnn_layers.rb', line 296

def pool_size
  @pool_size
end

#strides ⇒ Object (readonly)

Returns the value of attribute strides.



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# File 'lib/dnn/core/cnn_layers.rb', line 297

def strides
  @strides
end

Instance Method Details

#build(input_shape) ⇒ Object 



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# File 'lib/dnn/core/cnn_layers.rb', line 315

def build(input_shape)
  unless input_shape.length == 3
    raise DNN_ShapeError, "Input shape is #{input_shape}. But input shape must be 3 dimensional."
  end
  super
  prev_h, prev_w = input_shape[0..1]
  @num_channel = input_shape[2]
  @pad_size = if @padding == true
                calc_conv2d_padding_size(prev_h, prev_w, *@pool_size, @strides)
              elsif @padding.is_a?(Array)
                @padding
              else
                [0, 0]
              end
  @out_size = calc_conv2d_out_size(prev_h, prev_w, *@pool_size, *@pad_size, @strides)
end

#load_hash(hash) ⇒ Object 



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# File 'lib/dnn/core/cnn_layers.rb', line 342

def load_hash(hash)
  initialize(hash[:pool_size], strides: hash[:strides], padding: hash[:padding])
end

#output_shape ⇒ Object 



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# File 'lib/dnn/core/cnn_layers.rb', line 332

def output_shape
  [*@out_size, @num_channel]
end

#to_hash ⇒ Object 



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# File 'lib/dnn/core/cnn_layers.rb', line 336

def to_hash
  super(pool_size: @pool_size,
        strides: @strides,
        padding: @padding)
end