text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  created on Wen. Feb 28th, 2018
                    by Xiaolong
  """
  import torch
  from torch import nn
  import torch.nn.functional as F
  # We use a simple densely-connected net for feature blending, without conv layers
  
  
  class blendNet(nn.Module):
      def __init__(self):
          super(blendNet, self).__init__()
          self.relu = nn.LeakyReLU(0.01)
          self.dropout= nn.Dropout(p=0.5)
          self.conv1d = nn.Conv1d(1, 32, 2)# input channel, and output channel, kernel size
          self.fc1 = nn.Linear(8192, 256)
          self.fc2 = nn.Linear(256, 32)
          self.fc3 = nn.Linear(32, 5)
  
      def forward(self, x):
          x = self.relu(self.fc1(x))
          x = self.relu(self.fc2(x))
          x = self.relu(self.fc3(x))
          return F.softmax(x, dim=0)
  
  
  if __name__ == "__main__":
      dd = torch.randn(20, 1, 1, 4096)
      dd = torch.autograd.Variable(dd)
      model = blendNet()
      y = model(dd)
  
      print(y.size())
  
  
  
  
  