Class: DNN::Layers::Conv2D

Inherits:
HasParamLayer show all
Includes:
Initializers, Conv2DModule
Defined in:
lib/dnn/core/layers.rb

Instance Attribute Summary collapse

Attributes inherited from HasParamLayer

#grads, #params

Class Method Summary collapse

Instance Method Summary collapse

Methods inherited from HasParamLayer

#update

Methods inherited from Layer

#built?, #prev_layer

Constructor Details

#initialize(num_filters, filter_size, weight_initializer: nil, bias_initializer: nil, strides: 1, padding: false, weight_decay: 0) ⇒ Conv2D

Returns a new instance of Conv2D.



231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
 
# File 'lib/dnn/core/layers.rb', line 231

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

Instance Attribute Details

#filter_size ⇒ Object (readonly)

Returns the value of attribute filter_size.



227
228
229
 
# File 'lib/dnn/core/layers.rb', line 227

def filter_size
  @filter_size
end

#num_filters ⇒ Object (readonly)

Returns the value of attribute num_filters.



226
227
228
 
# File 'lib/dnn/core/layers.rb', line 226

def num_filters
  @num_filters
end

#strides ⇒ Object (readonly)

Returns the value of attribute strides.



228
229
230
 
# File 'lib/dnn/core/layers.rb', line 228

def strides
  @strides
end

#weight_decay ⇒ Object (readonly)

Returns the value of attribute weight_decay.



229
230
231
 
# File 'lib/dnn/core/layers.rb', line 229

def weight_decay
  @weight_decay
end

Class Method Details

.load_hash(hash) ⇒ Object 



247
248
249
250
251
252
253
254
 
# File 'lib/dnn/core/layers.rb', line 247

def self.load_hash(hash)
  Conv2D.new(hash[:num_filters], hash[:filter_size],
             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 



275
276
277
278
279
280
281
282
283
284
285
286
 
# File 'lib/dnn/core/layers.rb', line 275

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_size, *@filter_size, @strides)
  @padding ? back_padding(dx, @pad) : dx
end

#build(model) ⇒ Object 



256
257
258
259
260
261
262
263
264
265
 
# File 'lib/dnn/core/layers.rb', line 256

def build(model)
  super
  prev_h, prev_w = prev_layer.shape[0..1]
  @out_size = out_size(prev_h, prev_w, *@filter_size, @strides)
  out_w, out_h = @out_size
  if @padding
    @pad = [prev_h - out_h, prev_w - out_w]
    @out_size = [prev_h, prev_w]
  end
end

#forward(x) ⇒ Object 



267
268
269
270
271
272
273
 
# File 'lib/dnn/core/layers.rb', line 267

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

#shape ⇒ Object 



288
289
290
 
# File 'lib/dnn/core/layers.rb', line 288

def shape
  [*@out_size, @num_filters]
end

#to_hash ⇒ Object 



292
293
294
295
296
297
298
299
300
 
# File 'lib/dnn/core/layers.rb', line 292

def to_hash
  super({num_filters: @num_filters,
         filter_size: @filter_size,
         weight_initializer: @weight_initializer.to_hash,
         bias_initializer: @bias_initializer.to_hash,
         strides: @strides,
         padding: @padding,
         weight_decay: @weight_decay})
end