.. _qmodel_api:

tensorflow_quantization.quantize_model

.. automodule:: tensorflow_quantization.quantize :members: quantize_model

.. note:: Currently only Functional and Sequential models are supported.

Examples

.. code:: python

import tensorflow as tf from tensorflow_quantization.quantize import quantize_model

Simple full model quantization.

1. Create a simple network

input_img = tf.keras.layers.Input(shape=(28, 28)) r = tf.keras.layers.Reshape(target_shape=(28, 28, 1))(input_img) x = tf.keras.layers.Conv2D(filters=2, kernel_size=(3, 3))(r) x = tf.keras.layers.ReLU()(x) x = tf.keras.layers.Conv2D(filters=2, kernel_size=(3, 3))(x) x = tf.keras.layers.ReLU()(x) x = tf.keras.layers.Flatten()(x) model = tf.keras.Model(input_img, x)

print(model.summary())

2. Quantize the network

q_model = quantize_model(model) print(q_model.summary())