def image_embedding(images,
                    model_fn=resnet_v1_152,
                    trainable=True,
                    is_training=True,
                    weight_decay=0.0001,
                    batch_norm_decay=0.997,
                    batch_norm_epsilon=1e-5,
                    batch_norm_scale=True,
                    add_summaries=False,
                    reuse=False):
  """Extract image features from pretrained resnet model."""

  is_resnet_training = trainable and is_training

  batch_norm_params = {
      "is_training": is_resnet_training,
      "trainable": trainable,
      "decay": batch_norm_decay,
      "epsilon": batch_norm_epsilon,
      "scale": batch_norm_scale,
  }

  if trainable:
    weights_regularizer = contrib.layers().l2_regularizer(weight_decay)
  else:
    weights_regularizer = None

  with tf.variable_scope(model_fn.__name__, [images], reuse=reuse) as scope:
    with slim.arg_scope(
        [slim.conv2d],
        weights_regularizer=weights_regularizer,
        trainable=trainable):
      with slim.arg_scope(
          [slim.conv2d],
          weights_initializer=slim.variance_scaling_initializer(),
          activation_fn=tf.nn.relu,
          normalizer_fn=slim.batch_norm,
          normalizer_params=batch_norm_params):
        with slim.arg_scope([slim.batch_norm],
                            is_training=is_resnet_training,
                            trainable=trainable):
          with slim.arg_scope([slim.max_pool2d], padding="SAME"):
            net, end_points = model_fn(
                images, num_classes=None, global_pool=False,
                is_training=is_resnet_training,
                reuse=reuse, scope=scope)

  if add_summaries:
    for v in end_points.values():
      contrib.layers().summaries.summarize_activation(v)

  return net
Extract image features from pretrained resnet model.
tensor2tensor/layers/vqa_layers.py