Keras Data Generation

model_compression_toolkit.data_generation.keras_data_generation_experimental(model, n_images, output_image_size, data_generation_config)

Function to perform data generation using the provided Keras model and data generation configuration.

Parameters:

  • model (Model) – Keras model to generate data for.

  • n_images (int) – Number of images to generate.

  • output_image_size (Union[int, Tuple[int, int]]) – Size of the output images.

  • data_generation_config (DataGenerationConfig) – Configuration for data generation.

Returns:

Finalized list containing generated images.

Return type:

List[tf.Tensor]

Examples

In this example, we’ll walk through generating images using a simple Keras model and a data generation configuration. The process involves creating a model, setting up a data generation configuration, and finally generating images with specified parameters.

Start by importing the Model Compression Toolkit (MCT), TensorFlow, and some layers from tensorflow.keras:

>>> import model_compression_toolkit as mct
>>> from tensorflow.keras.models import Sequential
>>> from tensorflow.keras.layers import Conv2D, BatchNormalization, Flatten, Dense, Reshape

Next, define a simple Keras model:

>>> model = Sequential([Conv2D(2, 3, input_shape=(8,8,3)), BatchNormalization(), Flatten(), Dense(10)])

Configure the data generation process using get_keras_data_generation_config. This function allows customization of the data generation process. For simplicity, this example sets the number of iterations (n_iter) to 1 and the batch size (data_gen_batch_size) to 2.

>>> config = mct.data_generation.get_keras_data_generation_config(n_iter=1, data_gen_batch_size=2)

Finally, use the keras_data_generation_experimental function to generate images based on the model and data generation configuration. Notice that this function is experimental and may change in future versions of MCT. The n_images parameter specifies the number of images to generate, and output_image_size sets the size of the generated images.

>>> generated_images = mct.data_generation.keras_data_generation_experimental(model=model, n_images=4, output_image_size=(8, 8), data_generation_config=config)

The generated images can then be used for various purposes, such as data-free quantization.

Return type:

Tensor