Interesting: FYI - comparing in tensorflow MobileNet and MobileNet TFLite version
running on a computer using Python --> to see differences in performance when packaged for Mobile

 

see https://www.tensorflow.org/lite/convert/python_api

End-to-end MobileNet conversion

The following example shows how to convert and run inference on a pre-trained tf.keras MobileNet model to TensorFlow Lite. It compares the results of the TensorFlow and TensorFlow Lite model on random data. In order to load the model from file, use model_path instead of model_content.

import numpy as np
import tensorflow as tf

# Load the MobileNet tf.keras model.
model = tf.keras.applications.MobileNetV2(
    weights="imagenet", input_shape=(224, 224, 3))

# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()


# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()



# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Test the TensorFlow Lite model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)

interpreter.invoke()

# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
tflite_results = interpreter.get_tensor(output_details[0]['index'])

# Test the TensorFlow model on random input data.
tf_results = model(tf.constant(input_data))

# Compare the result.
for tf_result, tflite_result in zip(tf_results, tflite_results):
  np.testing.assert_almost_equal(tf_result, tflite_result, decimal=5)