在tensorflow slim模型中，alexnetèv2.py使用conv2d而不是完全连接的lay

Question

在实现中，它使用conv2d而不是完全连接的层。你知道吗

pool5的张量形状是6*6*256。你知道吗

在内核大小为5*5没有填充，步长为1之后，输出应该是2*2*4096。你知道吗

但从全连接层实现来看，神经元是4096。你知道吗

有人能解释一下吗？非常感谢你。你知道吗

with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                    outputs_collections=[end_points_collection]):
  net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
                    scope='conv1')
  net = slim.max_pool2d(net, [3, 3], 2, scope='pool1')
  net = slim.conv2d(net, 192, [5, 5], scope='conv2')
  net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
  net = slim.conv2d(net, 384, [3, 3], scope='conv3')
  net = slim.conv2d(net, 384, [3, 3], scope='conv4')
  net = slim.conv2d(net, 256, [3, 3], scope='conv5')
  net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')
  # Use conv2d instead of fully_connected layers.
  with slim.arg_scope([slim.conv2d],
                      weights_initializer=trunc_normal(0.005),
                      biases_initializer=tf.constant_initializer(0.1)):
    net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
                      scope='fc6')
    net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                       scope='dropout6')
    net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
    # Convert end_points_collection into a end_point dict.
    end_points = slim.utils.convert_collection_to_dict(
        end_points_collection)
    if global_pool:
      net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
      end_points['global_pool'] = net
    if num_classes:
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout7')
      net = slim.conv2d(net, num_classes, [1, 1],
                        activation_fn=None,
                        normalizer_fn=None,
                        biases_initializer=tf.zeros_initializer(),
                        scope='fc8')
      if spatial_squeeze:
        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
      end_points[sc.name + '/fc8'] = net