unable to use Trained Tensorflow model

Question

I am new to Deep Learning and Tensorflow. I retrained a pretrained tensorflow inceptionv3 model as saved_model.pb to recognize different type of images but when

Answer 1

I am assuming that you saved your trained model using tf.saved_model.Builder provided by TensorFlow, in which case you could possibly do something like:

Load model

export_path = './path/to/saved_model.pb'

# We start a session using a temporary fresh Graph
with tf.Session(graph=tf.Graph()) as sess:
    '''
    You can provide 'tags' when saving a model,
    in my case I provided, 'serve' tag 
    '''

    tf.saved_model.loader.load(sess, ['serve'], export_path)
    graph = tf.get_default_graph()

    # print your graph's ops, if needed
    print(graph.get_operations())

    '''
    In my case, I named my input and output tensors as
    input:0 and output:0 respectively
    ''' 
    y_pred = sess.run('output:0', feed_dict={'input:0': X_test})

To give some more context here, this is how I saved my model which can be loaded as above.

Save model

x = tf.get_default_graph().get_tensor_by_name('input:0')
y = tf.get_default_graph().get_tensor_by_name('output:0')

export_path = './models/'
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
signature = tf.saved_model.predict_signature_def(
                inputs={'input': x}, outputs={'output': y}
                )

# using custom tag instead of: tags=[tf.saved_model.tag_constants.SERVING]
builder.add_meta_graph_and_variables(sess=obj.sess,
                                     tags=['serve'],
                                     signature_def_map={'predict': signature})
builder.save()

This will save your protobuf ('saved_model.pb') in the said folder ('models' here) which can then be loaded as stated above.