docs: def build_alexnet_weights(self):
          """ Build a set of convnet weights for AlexNet """
          net_data = self._net_data
          #conv1
          #conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
          k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
          conv1W = tf.Variable(net_data["conv1"][0])
          conv1b = tf.Variable(net_data["conv1"][1])
    
          #conv2
          #conv(5, 5, 256, 1, 1, group=2, name='conv2')
          k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2
          conv2W = tf.Variable(net_data["conv2"][0])
          conv2b = tf.Variable(net_data["conv2"][1])
          
          #conv3
          #conv(3, 3, 384, 1, 1, name='conv3')
          k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1
          conv3W = tf.Variable(net_data["conv3"][0])
          conv3b = tf.Variable(net_data["conv3"][1])
          
          #conv4
          #conv(3, 3, 384, 1, 1, group=2, name='conv4')
          k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2
          conv4W = tf.Variable(net_data["conv4"][0])
          conv4b = tf.Variable(net_data["conv4"][1])
          
          #conv5
          #conv(3, 3, 256, 1, 1, group=2, name='conv5')
          k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2
          conv5W = tf.Variable(net_data["conv5"][0])
          conv5b = tf.Variable(net_data["conv5"][1])
      
          #fc6
          #fc(4096, name='fc6')
          fc6_in_size = net_data["fc6"][0].shape[0]
          fc6_out_size = net_data["fc6"][0].shape[1]
          fc6W = tf.Variable(net_data["fc6"][0])
          fc6b = tf.Variable(net_data["fc6"][1])
  
          #fc7
          #fc(4096, name='fc7') 
          fc7_in_size = fc6_out_size
          fc7_out_size = net_data["fc7"][0].shape[1]
          fc7W = tf.Variable(net_data["fc7"][0])
          fc7b = tf.Variable(net_data["fc7"][1])
      
          #fc8
          #fc(num_cats, relu=False, name='fc8')                       
          fc8_in_size = fc7_out_size
          fc8_out_size = self._out_size
          fc8W = tf.Variable(tf.truncated_normal([fc8_in_size, fc8_out_size],
                                                 stddev=0.01,
                                                 seed=None))
          fc8b = tf.Variable(tf.constant(0.0, shape=[fc8_out_size]))
    
          # make return object
          weights = AlexNetWeights()
          weights.conv1W = conv1W
          weights.conv1b = conv1b
          weights.conv2W = conv2W
          weights.conv2b = conv2b
          weights.conv3W = conv3W
          weights.conv3b = conv3b 
          weights.conv4W = conv4W
          weights.conv4b = conv4b 
          weights.conv5W = conv5W
          weights.conv5b = conv5b 
          weights.fc6W = fc6W
          weights.fc6b = fc6b
          weights.fc7W = fc7W
          weights.fc7b = fc7b
          weights.fc8W = fc8W
          weights.fc8b = fc8b
          return weights