mnist_tutorial_from_tf.py

# python3 -m pip install tensorflow==2.0.0 tflite==2.0.0

import logging
logging.getLogger("tensorflow").setLevel(logging.DEBUG)

try:
  # %tensorflow_version only exists in Colab.
  import tensorflow.compat.v2 as tf
except Exception:
  pass
tf.enable_v2_behavior()

from tensorflow import keras
import numpy as np
import pathlib



# Load MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images = test_images / 255.0

# Define the model architecture
model = keras.Sequential([
  keras.layers.InputLayer(input_shape=(28, 28)),
  keras.layers.Reshape(target_shape=(28, 28, 1)),
  keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),
  keras.layers.MaxPooling2D(pool_size=(2, 2)),
  keras.layers.Flatten(),
  keras.layers.Dense(10, activation=tf.nn.softmax)
])

# Train the digit classification model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(
  train_images,
  train_labels,
  epochs=1,
  validation_data=(test_images, test_labels)
)


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

tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)

tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model)



converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]

mnist_train, _ = tf.keras.datasets.mnist.load_data()
images = tf.cast(mnist_train[0], tf.float32) / 255.0
mnist_ds = tf.data.Dataset.from_tensor_slices((images)).batch(1)
def representative_data_gen():
  for input_value in mnist_ds.take(100):
    yield [input_value]

converter.representative_dataset = representative_data_gen

tflite_model_quant = converter.convert()
tflite_model_quant_file = tflite_models_dir/"mnist_model_quant.tflite"
tflite_model_quant_file.write_bytes(tflite_model_quant)

# converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
# converter.inference_input_type = tf.uint8
# converter.inference_output_type = tf.uint8
# 
# tflite_model_quant = converter.convert()
# tflite_model_quant_file = tflite_models_dir/"mnist_model_quant_io.tflite"
# tflite_model_quant_file.write_bytes(tflite_model_quant)