full-integer-tflite-model-converter.py

import numpy as np
import tensorflow as tf

# Sample 2x FC (dense) model
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(48, input_shape=(16,)))
model.add(tf.keras.layers.Dense(48))
model.add(tf.keras.layers.Softmax())
model.compile(
        optimizer='adam',
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy'])
model.summary()

# Generate random data
data_x = np.random.rand(16, 16)
data_y = np.random.randint(2, size=(16, 1))

# Train
model.fit(data_x, data_y, epochs=16)

# Representative dataset for full quantization:
def representative_dataset_gen():
  for _ in range(16):
    yield [np.random.rand(1, 16).astype(np.float32)]

# Convert to fixed-point
converter = tf.lite.TFLiteConverter.from_keras_model(model)

converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
converter.representative_dataset = representative_dataset_gen

tflite_model = converter.convert()

# Save
open("/tmp/fc_fp_quant_model.tflite", "wb").write(tflite_model)