class_docstr: Regular classifier network for small-size images
  
  Args:
      input_size (int, optional): the dimension of the final feature vector. Defaults to 128.
      n_class (int, optional): the number of classes. Defaults to 10.
class_docstr_tok: ["Regular","classifier","network","for","small-size","images","Args",":","input_size","(","int",",","optional",")",":","the","dimension","of","the","final","feature","vector",".","Defaults","to","128.","n_class","(","int",",","optional",")",":","the","number","of","classes",".","Defaults","to","10","."]
code: def __init__(self, input_size=128, n_class=10):
      super(ClassNetSmallImage, self).__init__()
      self._n_classes = n_class
      self.fc1 = nn.Linear(input_size, 100)
      self.bn1 = nn.BatchNorm1d(100)
      self.relu1 = nn.ReLU()
      self.dp1 = nn.Dropout()
      self.fc2 = nn.Linear(100, 100)
      self.bn2 = nn.BatchNorm1d(100)
      self.relu2 = nn.ReLU()
      self.fc3 = nn.Linear(100, n_class)
  
imports: import torch
  import torch.nn
  from kale.embed.video_i3d import Unit3D
label_desc: Use image node_id 1 to create a new ClassNetSmallImage object from inherited base classes: nn with example: obj = ClassNetSmallImage(input_size, n_class)