code: def conv_net(self, input, class_dim=1000):
      np.random.seed(1)
  
      stdv = 1.0 / math.sqrt(input.shape[1] * 11 * 11)
      conv1 = fluid.layers.conv2d(
          input=input,
          num_filters=64,
          filter_size=11,
          stride=4,
          padding=2,
          groups=1,
          act="relu",
          bias_attr=fluid.param_attr.ParamAttr(
              name="conv2d_0" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="conv2d_0" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
      pool1 = fluid.layers.pool2d(
          input=conv1,
          pool_size=3,
          pool_stride=2,
          pool_padding=0,
          pool_type="max",
      )
  
      stdv = 1.0 / math.sqrt(pool1.shape[1] * 5 * 5)
      conv2 = fluid.layers.conv2d(
          input=pool1,
          num_filters=192,
          filter_size=5,
          stride=1,
          padding=2,
          groups=1,
          act="relu",
          bias_attr=fluid.param_attr.ParamAttr(
              name="conv2d_1" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="conv2d_1" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
      pool2 = fluid.layers.pool2d(
          input=conv2,
          pool_size=3,
          pool_stride=2,
          pool_padding=0,
          pool_type="max",
      )
  
      stdv = 1.0 / math.sqrt(pool2.shape[1] * 3 * 3)
      conv3 = fluid.layers.conv2d(
          input=pool2,
          num_filters=384,
          filter_size=3,
          stride=1,
          padding=1,
          groups=1,
          act="relu",
          bias_attr=fluid.param_attr.ParamAttr(
              name="conv2d_2" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="conv2d_2" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
  
      stdv = 1.0 / math.sqrt(conv3.shape[1] * 3 * 3)
      conv4 = fluid.layers.conv2d(
          input=conv3,
          num_filters=256,
          filter_size=3,
          stride=1,
          padding=1,
          groups=1,
          act="relu",
          bias_attr=fluid.param_attr.ParamAttr(
              name="conv2d_3" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="conv2d_3" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
  
      stdv = 1.0 / math.sqrt(conv4.shape[1] * 3 * 3)
      conv5 = fluid.layers.conv2d(
          input=conv4,
          num_filters=256,
          filter_size=3,
          stride=1,
          padding=1,
          groups=1,
          act="relu",
          bias_attr=fluid.param_attr.ParamAttr(
              name="conv2d_4" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="conv2d_4" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
      pool5 = fluid.layers.pool2d(
          input=conv5,
          pool_size=3,
          pool_stride=2,
          pool_padding=0,
          pool_type="max",
      )
  
      drop6 = fluid.layers.dropout(x=pool5, dropout_prob=0.5, seed=1)
  
      stdv = 1.0 / math.sqrt(
          drop6.shape[1] * drop6.shape[2] * drop6.shape[3] * 1.0
      )
      fc6 = fluid.layers.fc(
          input=drop6,
          size=4096,
          act="relu",
          bias_attr=fluid.param_attr.ParamAttr(
              name="fc_0" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="fc_0" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
  
      drop7 = fluid.layers.dropout(x=fc6, dropout_prob=0.5, seed=1)
  
      stdv = 1.0 / math.sqrt(drop7.shape[1] * 1.0)
      fc7 = fluid.layers.fc(
          input=drop7,
          size=4096,
          act="relu",
          bias_attr=fluid.param_attr.ParamAttr(
              name="fc_1" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="fc_1" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
  
      stdv = 1.0 / math.sqrt(fc7.shape[1] * 1.0)
      out = fluid.layers.fc(
          input=fc7,
          size=class_dim,
          act=None,
          bias_attr=fluid.param_attr.ParamAttr(
              name="fc_2" + ".b" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
          param_attr=fluid.param_attr.ParamAttr(
              name="fc_2" + ".w" + "_0",
              initializer=fluid.initializer.Uniform(-stdv, stdv),
          ),
      )
      return out
  
file_path: AdvBox/example/models/alexnet.py
imports: from builtins import object
  import paddle
  import paddle.fluid
  import math
  import numpy
label_desc: Use image node_id 2 for calling the AlexNet obj's underlying member method code with example usage: obj.conv_net(input, class_dim) and returns: out