CODEWITHSUNDEEP
tensorflowkerastensorflow-estimatortensorflow-litepruning
Anil Maddala
Anil Maddala

Reputation: 908

Tensorflow remove layers from pretrained model

Is there a way to load a pretrained model in Tensorflow and remove the top layers in the network? I am looking at Tensorflow release r1.10

The only documentation I could find is with tf.keras.Sequential.pop https://www.tensorflow.org/versions/r1.10/api_docs/python/tf/keras/Sequential#pop

I want to manually prune a pretrained network by removing bunch of top convolution layers and add a custom fully convoluted layer.

EDIT:

The model is ssd_mobilenet_v1_coco downloaded from Tensorflow Model Zoo. I have access to both the frozen_inference_graph.pb model file and checkpoint file.

I donot have access to the python code which is used to construct the model.

Thanks.

Upvotes: 1

Views: 3404

Answers (1)

DomJack
DomJack

Reputation: 4183

From inspecting the code, SSDMobileNetV1FeatureExtractor.extract_features redirects research.slim.nets:

  from nets import mobilenet_v1  # nets will have to be on your PYTHONPATH

with tf.variable_scope('MobilenetV1',
                       reuse=self._reuse_weights) as scope:
  with slim.arg_scope(
      mobilenet_v1.mobilenet_v1_arg_scope(
          is_training=None, regularize_depthwise=True)):
    with (slim.arg_scope(self._conv_hyperparams_fn())
          if self._override_base_feature_extractor_hyperparams
          else context_manager.IdentityContextManager()):
      _, image_features = mobilenet_v1.mobilenet_v1_base(
          ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
          final_endpoint='Conv2d_13_pointwise',
          min_depth=self._min_depth,
          depth_multiplier=self._depth_multiplier,
          use_explicit_padding=self._use_explicit_padding,
          scope=scope)

The mobilenet_v1_base function takes a final_endpoint argument. Rather than prune the constructed graph, just construct the graph up until the endpoint you want.

Upvotes: 1

Related Questions