How To Verify Optimized Model In Tensorflow

I'm following a tutorial from codelabs. They use this script to optimize the model python -m tensorflow.python.tools.optimize_for_inference \ --input=tf_files/retrained_graph.pb

Solution 1:

I've solved the error by changing the placeholder's type with tf.float32 when exporting the model:

def my_serving_input_fn():
    input_data = {
        "featurename" : tf.placeholder(tf.float32, [None, 4], name='inputtensors')
    }
    return tf.estimator.export.ServingInputReceiver(input_data, input_data)

and then change the prediction function above to:

defpredict(model_path, input_data):
    # load tf graph
    tf_model, tf_input, tf_output = load_graph(model_path)

    x = tf_model.get_tensor_by_name(tf_input)
    y = tf_model.get_tensor_by_name(tf_output) 

    num_outputs = 3
    predictions = np.zeros(num_outputs)
    with tf.Session(graph=tf_model) as sess:
        y_out = sess.run(y, feed_dict={x: [input_data]})
        predictions = y_out

    return predictions

After freezing the model, the prediction code above will be work. But unfortunately it raises another error when trying to load pb directly after exporting the model.