alexnet.py 文件源码

def fc_layers(net,
              scope,
              end_points_collection,
              num_classes=1000,
              is_training=True,
              dropout_keep_prob=0.5,
              spatial_squeeze=True,
              name_prefix=None):
  full_scope_name = lambda scope_name: scope_name if name_prefix is None else '%s_%s' % (name_prefix, scope_name)
  # 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),
                      outputs_collections=[end_points_collection]):
    net = slim.conv2d(net, num_classes, [1, 1],
                      activation_fn=None,
                      normalizer_fn=None,
                      biases_initializer=tf.zeros_initializer(),
                      scope=full_scope_name('fc8'))

  if spatial_squeeze:
    net = tf.squeeze(net, [1, 2], name=full_scope_name('fc8/squeezed'))
    ops.add_to_collection(end_points_collection, net)
  return net, end_points_collection