Quantize a Keras neural network model

Question

Recently, I\'ve started creating neural networks with Tensorflow + Keras and I would like to try the quantization feature available in Tensorflow. So far, experimenting with exa

Answer 1

As your network looks quite simple, you can maybe use Tensorflow lite.

Answer 2

As mentioned in other answers, TensorFlow Lite can help you with network quantization.

TensorFlow Lite provides several levels of support for quantization.

Tensorflow Lite post-training quantization quantizes weights and activations post training easily. Quantization-aware training allows for training of networks that can be quantized with minimal accuracy drop; this is only available for a subset of convolutional neural network architectures.

So first, you need to decide whether you need post-training quantization or quantization-aware training. For example, if you already saved the model as *.h5 files, you would probably want to follow @Mitiku's instruction and do the post-training quantization.

If you prefer to achieve higher performance by simulating the effect of quantization in training (using the method you quoted in the question), and your model is in the subset of CNN architecture supported by quantization-aware training, this example may help you in terms of interaction between Keras and TensorFlow. Basically, you only need to add this code between model definition and its fitting:

sess = tf.keras.backend.get_session()
tf.contrib.quantize.create_training_graph(sess.graph)
sess.run(tf.global_variables_initializer())

Answer 3

Tensorflow lite can be used to quantize keras model.

The following code was written for tensorflow 1.14. It might not work for earlier versions.

First, after training the model you should save your model to h5

model.fit(train_images, train_labels, epochs=epochs)

# evaluate the accuracy of model on test data
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
model.save("model.h5")

To load keras model use tf.lite.TFLiteConverter.from_keras_model_file

# load the previously saved model
converter  = tf.lite.TFLiteConverter.from_keras_model_file("model.h5")
tflite_model = converter.convert()
# Save the model to file
with open("tflite_model.tflite", "wb") as output_file:
    output_file.write(tflite_model)

The saved model can be loaded to python script or to other platforms and languages. To use saved tflite model, tensorlfow.lite provides Interpreter. The following example from here shows how to load tflite model from local file using python scripts.

import numpy as np
import tensorflow as tf

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

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

# Test 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.
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)