GenreNet(
  (conv_part): Sequential(
    (0): Conv1d(1, 8, kernel_size=(5,), stride=(1,))
    (1): LeakyReLU(negative_slope=0.01)
    (2): Conv1d(8, 16, kernel_size=(5,), stride=(1,), dilation=(2,))
    (3): LPPool1d(norm_type=2, kernel_size2, stride=None, ceil_mode=False)
    (4): LeakyReLU(negative_slope=0.01)
    (5): Conv1d(16, 32, kernel_size=(5,), stride=(1,), dilation=(4,))
    (6): LPPool1d(norm_type=2, kernel_size2, stride=None, ceil_mode=False)
    (7): LeakyReLU(negative_slope=0.01)
    (8): Conv1d(32, 16, kernel_size=(5,), stride=(1,), dilation=(8,))
    (9): LPPool1d(norm_type=2, kernel_size2, stride=None, ceil_mode=False)
    (10): LeakyReLU(negative_slope=0.01)
    (11): Conv1d(16, 16, kernel_size=(5,), stride=(1,), dilation=(16,))
    (12): LPPool1d(norm_type=2, kernel_size2, stride=None, ceil_mode=False)
    (13): LeakyReLU(negative_slope=0.01)
    (14): Conv1d(16, 16, kernel_size=(5,), stride=(1,), dilation=(32,))
    (15): LPPool1d(norm_type=2, kernel_size2, stride=None, ceil_mode=False)
    (16): LeakyReLU(negative_slope=0.01)
  )
  (linear_part): Sequential(
    (0): Linear(in_features=108880, out_features=23, bias=True)
    (1): Softmax()
  )
)