Copyright 2018 The TensorFlow Authors.



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#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# https://www.apache.org/licenses/LICENSE-2.0
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Running TFLite models

View source on GitHub

Setup



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import tensorflow as tf

import pathlib
import numpy as np
import matplotlib.pyplot as plt

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input

Create a basic model of the form y = mx + c



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# Create a simple Keras model.
x = [-1, 0, 1, 2, 3, 4]
y = [-3, -1, 1, 3, 5, 7]

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1])
])
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(x, y, epochs=200, verbose=1)

Generate a SavedModel



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export_dir = 'saved_model/1'
tf.saved_model.save(model, export_dir)

Convert the SavedModel to TFLite



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



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tflite_model_file = pathlib.Path('model.tflite')
tflite_model_file.write_bytes(tflite_model)

Initialize the TFLite interpreter to try it out



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# 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()



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# Test the TensorFlow Lite model on random input data.
input_shape = input_details[0]['shape']
inputs, outputs = [], []
for _ in range(100):
  input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
  interpreter.set_tensor(input_details[0]['index'], input_data)

  interpreter.invoke()
  tflite_results = interpreter.get_tensor(output_details[0]['index'])

  # Test the TensorFlow model on random input data.
  tf_results = model(tf.constant(input_data))
  output_data = np.array(tf_results)
  
  inputs.append(input_data[0][0])
  outputs.append(output_data[0][0])

Visualize the model



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plt.plot(inputs, outputs, 'r')
plt.show()

Download the TFLite model file



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try:
  from google.colab import files
  files.download(tflite_model_file)
except:
  pass