old_contents: import chainer
  import chainer.functions as F
  
  
  class Alex(chainer.FunctionSet):
  
      """Single-GPU AlexNet.
  
      """
  
      insize = 227
  
      def __init__(self):
          super(Alex, self).__init__(
              conv1=F.Convolution2D(3,  96, 11, stride=4),
              conv2=F.Convolution2D(96, 256,  5, pad=2),
              conv3=F.Convolution2D(256, 384,  3, pad=1),
              conv4=F.Convolution2D(384, 384,  3, pad=1),
              conv5=F.Convolution2D(384, 256,  3, pad=1),
              fc6=F.Linear(9216, 4096),
              fc7=F.Linear(4096, 4096),
              fc8=F.Linear(4096, 1000),
          )
  
      def forward(self, x_data, y_data, train=True):
          x = chainer.Variable(x_data, volatile=not train)
          t = chainer.Variable(y_data, volatile=not train)
  
          h = F.max_pooling_2d(F.relu(
              F.local_response_normalization(self.conv1(x))), 3, stride=2)
          h = F.max_pooling_2d(F.relu(
              F.local_response_normalization(self.conv2(h))), 3, stride=2)
          h = F.relu(self.conv3(h))
          h = F.relu(self.conv4(h))
          h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
          h = F.dropout(F.relu(self.fc6(h)))
          h = F.dropout(F.relu(self.fc7(h)))
          h = self.fc8(h)
          return F.softmax_cross_entropy(h, t), F.accuracy(h, t)
  
new_contents: import chainer
  import chainer.functions as F
  
  
  class Alex(chainer.FunctionSet):
  
      """Single-GPU AlexNet without partition toward the channel axis.
  
      """
  
      insize = 227
  
      def __init__(self):
          super(Alex, self).__init__(
              conv1=F.Convolution2D(3,  96, 11, stride=4),
              conv2=F.Convolution2D(96, 256,  5, pad=2),
              conv3=F.Convolution2D(256, 384,  3, pad=1),
              conv4=F.Convolution2D(384, 384,  3, pad=1),
              conv5=F.Convolution2D(384, 256,  3, pad=1),
              fc6=F.Linear(9216, 4096),
              fc7=F.Linear(4096, 4096),
              fc8=F.Linear(4096, 1000),
          )
  
      def forward(self, x_data, y_data, train=True):
          x = chainer.Variable(x_data, volatile=not train)
          t = chainer.Variable(y_data, volatile=not train)
  
          h = F.max_pooling_2d(F.relu(
              F.local_response_normalization(self.conv1(x))), 3, stride=2)
          h = F.max_pooling_2d(F.relu(
              F.local_response_normalization(self.conv2(h))), 3, stride=2)
          h = F.relu(self.conv3(h))
          h = F.relu(self.conv4(h))
          h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
          h = F.dropout(F.relu(self.fc6(h)))
          h = F.dropout(F.relu(self.fc7(h)))
          h = self.fc8(h)
          return F.softmax_cross_entropy(h, t), F.accuracy(h, t)
  
subject: Fix short description of AlexNet