Post-training float16 quantization

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TensorFlow Lite now supports converting weights to 16-bit floating point values during model conversion from TensorFlow to TensorFlow Lite's flat buffer format. This results in a 2x reduction in model size. Some harware, like GPUs, can compute natively in this reduced precision arithmetic, realizing a speedup over traditional floating point execution. The Tensorflow Lite GPU delegate can be configured to run in this way. However, a model converted to float16 weights can still run on the CPU without additional modification: the float16 weights are upsampled to float32 prior to the first inference. This permits a significant reduction in model size in exchange for a minimal impacts to latency and accuracy.

In this tutorial, you train an MNIST model from scratch, check its accuracy in TensorFlow, and then convert the model into a Tensorflow Lite flatbuffer with float16 quantization. Finally, check the accuracy of the converted model and compare it to the original float32 model.

Build an MNIST model


import logging

import tensorflow as tf
from tensorflow import keras
import numpy as np
import pathlib

Train and export the model

# 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)),

# Train the digit classification model
  validation_data=(test_images, test_labels)
Downloading data from
11493376/11490434 [==============================] - 0s 0us/step
1875/1875 [==============================] - 15s 2ms/step - loss: 0.5048 - accuracy: 0.8596 - val_loss: 0.1465 - val_accuracy: 0.9598

<tensorflow.python.keras.callbacks.History at 0x7f233a9dc518>

For the example, you trained the model for just a single epoch, so it only trains to ~96% accuracy.

Convert to a TensorFlow Lite model

Using the Python TFLiteConverter, you can now convert the trained model into a TensorFlow Lite model.

Now load the model using the TFLiteConverter:

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
INFO:tensorflow:Assets written to: /tmp/tmphrlr_scf/assets

Write it out to a .tflite file:

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"

To instead quantize the model to float16 on export, first set the optimizations flag to use default optimizations. Then specify that float16 is the supported type on the target platform:

converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]

Finally, convert the model like usual. Note, by default the converted model will still use float input and outputs for invocation convenience.

tflite_fp16_model = converter.convert()
tflite_model_fp16_file = tflite_models_dir/"mnist_model_quant_f16.tflite"
INFO:tensorflow:Assets written to: /tmp/tmpexbkmgd6/assets

INFO:tensorflow:Assets written to: /tmp/tmpexbkmgd6/assets


Note how the resulting file is approximately 1/2 the size.

ls -lh {tflite_models_dir}
total 128K
-rw-rw-r-- 1 kbuilder kbuilder 83K Dec 17 20:21 mnist_model.tflite
-rw-rw-r-- 1 kbuilder kbuilder 44K Dec 17 20:21 mnist_model_quant_f16.tflite

Run the TensorFlow Lite models

Run the TensorFlow Lite model using the Python TensorFlow Lite Interpreter.

Load the model into the interpreters

interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))
interpreter_fp16 = tf.lite.Interpreter(model_path=str(tflite_model_fp16_file))

Test the models on one image

test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)

input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]

interpreter.set_tensor(input_index, test_image)
predictions = interpreter.get_tensor(output_index)
import matplotlib.pylab as plt

template = "True:{true}, predicted:{predict}"
_ = plt.title(template.format(true= str(test_labels[0]),


test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)

input_index = interpreter_fp16.get_input_details()[0]["index"]
output_index = interpreter_fp16.get_output_details()[0]["index"]

interpreter_fp16.set_tensor(input_index, test_image)
predictions = interpreter_fp16.get_tensor(output_index)
template = "True:{true}, predicted:{predict}"
_ = plt.title(template.format(true= str(test_labels[0]),


Evaluate the models

# A helper function to evaluate the TF Lite model using "test" dataset.
def evaluate_model(interpreter):
  input_index = interpreter.get_input_details()[0]["index"]
  output_index = interpreter.get_output_details()[0]["index"]

  # Run predictions on every image in the "test" dataset.
  prediction_digits = []
  for test_image in test_images:
    # Pre-processing: add batch dimension and convert to float32 to match with
    # the model's input data format.
    test_image = np.expand_dims(test_image, axis=0).astype(np.float32)
    interpreter.set_tensor(input_index, test_image)

    # Run inference.

    # Post-processing: remove batch dimension and find the digit with highest
    # probability.
    output = interpreter.tensor(output_index)
    digit = np.argmax(output()[0])

  # Compare prediction results with ground truth labels to calculate accuracy.
  accurate_count = 0
  for index in range(len(prediction_digits)):
    if prediction_digits[index] == test_labels[index]:
      accurate_count += 1
  accuracy = accurate_count * 1.0 / len(prediction_digits)

  return accuracy

Repeat the evaluation on the float16 quantized model to obtain:

# NOTE: Colab runs on server CPUs. At the time of writing this, TensorFlow Lite
# doesn't have super optimized server CPU kernels. For this reason this may be
# slower than the above float interpreter. But for mobile CPUs, considerable
# speedup can be observed.

In this example, you have quantized a model to float16 with no difference in the accuracy.

It's also possible to evaluate the fp16 quantized model on the GPU. To perform all arithmetic with the reduced precision values, be sure to create the TfLiteGPUDelegateOptions struct in your app and set precision_loss_allowed to 1, like this:

//Prepare GPU delegate.
const TfLiteGpuDelegateOptions options = {
  .metadata = NULL,
  .compile_options = {
    .precision_loss_allowed = 1,  // FP16
    .preferred_gl_object_type = TFLITE_GL_OBJECT_TYPE_FASTEST,
    .dynamic_batch_enabled = 0,   // Not fully functional yet

Detailed documentation on the TFLite GPU delegate and how to use it in your application can be found here