[딥러닝 필기] week7. CNN Basics: CNN Structure

Convolution Layer's Output Size

• Output Size = (Intput Size + Padding Size * 2 - Convolution Size ) / Stride + 1

def calculate_convolution(input_size, input_chanel,
                          output_chanel, output_size=None,
                          conv_size=3, padding=1, stride =1,
                          print_input=True
                          ):
  if output_size == None:
    output_size = (input_size + padding*2 - conv_size)/stride + 1
    output_size = int(output_size) if output_size % 1 == 0 else False  
  if print_input:
    print("[%sX%sX%s] Input"%(input_size,input_size,input_chanel))
  print("[%sX%sX%s] CONV: %s %sX%s filters at stride %s, pad %s"%(
      output_size,output_size,output_chanel,output_chanel,
      conv_size,conv_size,stride,padding),
      end="; " 
  )
  print("params:(%s*%s*%s)*%s"%(input_chanel,conv_size,conv_size,output_chanel))
  return output_size, output_chanel

calculate_convolution(input_size=29,input_chanel=3,output_chanel=20,
                      conv_size=5, padding=1, stride =2)

# [29X29X3] Input
# [14X14X20] CONV: 20 5X5 filters at stride 2, pad 1; params:(3*5*5)*20
# (14, 20)

Pooling Output Size

• Output Size = (Input Size - Pooling Size) / Stride + 1

def calculate_pooling(input_size=1,output_size=None, input_chanel="-",
                      pool_size=2, stride =1, print_input=True):
  output_size = (input_size - pool_size)/stride + 1
  output_size = int(output_size) if output_size % 1 == 0 else False
  if print_input:
    print("[%sX%sX%s] Input"%(input_size,input_size,input_chanel))
  print("[%sX%sX%s] POOL: %sX%s filters at stride %s"%(
      output_size, output_size,input_chanel,pool_size,pool_size,stride            
  ))
  return output_size, input_chanel

calculate_pooling(input_size=12,output_size=20, input_chanel=20,
                  pool_size=2, stride =2, print_input=True)
                  
# [12X12X20] Input
# [6X6X20] POOL: 2X2 filters at stride 2
# (6, 20)