Class: DNN::Layers::Conv2D

Inherits:
HasParamLayer show all
Includes:
Initializers, Convert
Defined in:
lib/dnn/core/layers.rb

Instance Attribute Summary

Attributes inherited from HasParamLayer

#grads, #params

Class Method Summary collapse

Instance Method Summary collapse

Methods inherited from HasParamLayer

#update

Methods inherited from Layer

#builded?, #prev_layer

Constructor Details

#initialize(num_filters, filter_width, filter_height, weight_initializer: nil, bias_initializer: nil, strides: [1, 1], padding: false, weight_decay: 0) ⇒ Conv2D

Returns a new instance of Conv2D.



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# File 'lib/dnn/core/layers.rb', line 225

def initialize(num_filters, filter_width, filter_height,
               weight_initializer: nil,
               bias_initializer: nil,
               strides: [1, 1],
               padding: false,
               weight_decay: 0)
  super()
  @num_filters = num_filters
  @filter_width = filter_width
  @filter_height = filter_height
  @weight_initializer = (weight_initializer || RandomNormal.new)
  @bias_initializer = (bias_initializer || Zeros.new)
  @strides = strides
  @padding = padding
  @weight_decay = weight_decay
end

Class Method Details

.load_hash(hash) ⇒ Object 



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# File 'lib/dnn/core/layers.rb', line 242

def self.load_hash(hash)
  Conv2D.new(hash[:num_filters], hash[:filter_width], hash[:filter_height],
             weight_initializer: Util.load_hash(hash[:weight_initializer]),
             bias_initializer: Util.load_hash(hash[:bias_initializer]),
             strides: hash[:strides],
             padding: hash[:padding],
             weight_decay: hash[:weight_decay])
end

Instance Method Details

#backward(dout) ⇒ Object 



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# File 'lib/dnn/core/layers.rb', line 270

def backward(dout)
  dout = dout.reshape(dout.shape[0..2].reduce(:*), dout.shape[3])
  @grads[:weight] = @col.transpose.dot(dout)
  if @weight_decay > 0
    dridge = @weight_decay * @params[:weight]
    @grads[:weight] += dridge
  end
  @grads[:bias] = dout.sum(0)
  dcol = dout.dot(@params[:weight].transpose)
  dx = col2im(dcol, @x_shape, @out_width, @out_height, @filter_width, @filter_height, @strides)
  @padding ? back_padding(dx, @pad) : dx
end

#build(model) ⇒ Object 



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# File 'lib/dnn/core/layers.rb', line 251

def build(model)
  super
  prev_width, prev_height = prev_layer.shape[0..1]
  @out_width, @out_height = out_size(prev_width, prev_height, @filter_width, @filter_height, @strides)
  if @padding
    @pad = [prev_width - @out_width, prev_height - @out_height]
    @out_width = prev_width
    @out_height = prev_height
  end
end

#forward(x) ⇒ Object 



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# File 'lib/dnn/core/layers.rb', line 262

def forward(x)
  x = padding(x, @pad) if @padding
  @x_shape = x.shape
  @col = im2col(x, @out_width, @out_height, @filter_width, @filter_height, @strides)
  out = @col.dot(@params[:weight])
  out.reshape(x.shape[0], @out_width, @out_height, out.shape[3])
end

#shape ⇒ Object 



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# File 'lib/dnn/core/layers.rb', line 283

def shape
  [@out_width, @out_height, @num_filters]
end

#to_hash ⇒ Object 



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# File 'lib/dnn/core/layers.rb', line 287

def to_hash
  {
    name: self.class.name,
    num_filters: @num_filters,
    filter_width: @filter_width,
    filter_height: @filter_height,
    weight_initializer: @weight_initializer.to_hash,
    bias_initializer: @bias_initializer.to_hash,
    strides: @strides,
    padding: @padding,
    weight_decay: @weight_decay,
  }
end