class_docstr: Small CNN module proposed for DQN in NeurIPS DL Workshop, 2013.
  
  See: https://arxiv.org/abs/1312.5602
class_docstr_tok: ["Small","CNN","module","proposed","for","DQN","in","NeurIPS","DL","Workshop",",","2013",".","See",":","https",":","\/\/arxiv.org\/abs\/1312.5602"]
code: def __init__(
      self,
      n_input_channels=4,
      n_output_channels=256,
      activation=F.relu,
      bias=0.1,
  ):
      self.n_input_channels = n_input_channels
      self.activation = activation
      self.n_output_channels = n_output_channels
      super().__init__()
      self.layers = nn.ModuleList(
          [
              nn.Conv2d(n_input_channels, 16, 8, stride=4),
              nn.Conv2d(16, 32, 4, stride=2),
          ]
      )
      self.output = nn.Linear(2592, n_output_channels)
  
      self.apply(init_chainer_default)
      self.apply(constant_bias_initializer(bias=bias))
  
file_path: pfrl/pfrl/nn/atari_cnn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pfrl.initializers import init_chainer_default
label_desc: Use image node_id 1 to create a new SmallAtariCNN object from inherited base classes: nn with example: obj = SmallAtariCNN(n_input_channels, n_output_channels, activation, bias)