loaded = tf.saved_model.load('mobilenet')

print('MobileNet has {} trainable variables: {},...'.format(

       len(loaded.trainable_variables),

       ', '.join([v.name for v in loaded.trainable_variables[:5]])))

trainable_variable_ids = {id(v) for v in loaded.trainable_variables}

non_trainable_variables = [v for v in loaded.variables if id(v) not in trainable_variable_ids]

print('MobileNet also has {} non-trainable variables: {}, ...'.format(

       len(non_trainable_variables),

       ', '.join([v.name for v in non_trainable_variables[:3]])))

MobileNet has  trainable variables: conv1/kernel:, conv1_bn/gamma:, conv1_bn/beta:, conv_dw_1/depthwise_kernel:, conv_dw_1_bn/gamma:,...

MobileNet also has  non-trainable variables: conv1_bn/moving_mean:, conv1_bn/moving_variance:, conv_dw_1_bn/moving_mean:, ...

Traceback (most recent call last):

  File "inspect_saved_model.py", line , in <module>

    len(facebox_model.trainable_variables),

AttributeError: '_UserObject' object has no attribute 'trainable_variables'

usage: saved_model_cli show [-h] --dir DIR [--all]

[--tag_set TAG_SET] [--signature_def SIGNATURE_DEF_KEY]

saved_model_cli show --dir mobilenet/ --all
or

saved_model_cli show --dir model/detector/ --tag_set serve --signature_def serving_default

(tf_test) ~/workspace/test_code/github_test/faceboxes-tensorflow$ saved_model_cli show --dir model/detector --all

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:

  The given SavedModel SignatureDef contains the following input(s):

  The given SavedModel SignatureDef contains the following output(s):

    outputs['__saved_model_init_op'] tensor_info:

        dtype: DT_INVALID

        shape: unknown_rank

        name: NoOp

  Method name is: 

signature_def['serving_default']:

  The given SavedModel SignatureDef contains the following input(s):

    inputs['images'] tensor_info:

        dtype: DT_FLOAT

        shape: (-, -, -, -)

        name: serving_default_images:

  The given SavedModel SignatureDef contains the following output(s):

    outputs['boxes'] tensor_info:

        dtype: DT_FLOAT

        shape: (-, , )

        name: StatefulPartitionedCall:

    outputs['num_boxes'] tensor_info:

        dtype: DT_INT32

        shape: (-)

        name: StatefulPartitionedCall:

    outputs['scores'] tensor_info:

        dtype: DT_FLOAT

        shape: (-, )

        name: StatefulPartitionedCall:

  Method name is: tensorflow/serving/predict