Source code for akida_models.mnist.model_gxnor

#!/usr/bin/env python
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# Copyright 2021 Brainchip Holdings Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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GXNOR model definition for MNIST classification.

__all__ = ["gxnor_mnist", "gxnor_mnist_pretrained"]

from keras import Model
from keras.layers import Input, Flatten, Rescaling

from ..layer_blocks import conv_block, dense_block
from ..utils import fetch_file
from ..model_io import load_model, get_model_path

[docs]def gxnor_mnist(): """ Instantiates a Keras GXNOR model with an additional dense layer to make better classification. The paper describing the original model can be found `here <>`_. Note: input preprocessing is included as part of the model (as a Rescaling layer). This model expects inputs to be float tensors of pixels with values in the [0, 255] range. Returns: keras.Model: a Keras model for GXNOR/MNIST """ img_input = Input(shape=(28, 28, 1), name="input") x = Rescaling(1. / 255, name="rescaling")(img_input) # Block 1 x = conv_block(x, filters=32, name='block_1/conv_1', kernel_size=(5, 5), padding='same', add_batchnorm=True, relu_activation='ReLU2', pooling='max', pool_size=(2, 2)) # Block 2 x = conv_block(x, filters=64, name='block_2/conv_1', kernel_size=(3, 3), padding='same', add_batchnorm=True, relu_activation='ReLU2', strides=2, pool_size=(2, 2)) # Classification block x = Flatten(name='flatten')(x) x = dense_block(x, units=512, name='fc_1', add_batchnorm=True, relu_activation='ReLU2') x = dense_block(x, units=10, name='predictions', add_batchnorm=True, relu_activation=False) # Create model return Model(img_input, x, name='gxnor_mnist')
[docs]def gxnor_mnist_pretrained(quantized=True): """ Helper method to retrieve a `gxnor_mnist` model that was trained on MNIST dataset. Args: quantized (bool, optional): a boolean indicating whether the model should be loaded quantized or not. Defaults to True. Returns: keras.Model: a Keras Model instance. """ if quantized: model_name_v1 = 'gxnor_mnist_iq2_wq2_aq1.h5' file_hash_v1 = 'f6f3e077c39fa4a65e401d3758af624fb276322e1d694fbf4f773941d43e7c5f' model_name_v2 = 'gxnor_mnist_i2_w2_a1.h5' file_hash_v2 = 'a040971632633547612975d1ee30d7ede6d7345bc7c6c1bcf5e2ebd0755578dc' else: model_name_v1 = 'gxnor_mnist.h5' file_hash_v1 = '8546a8efde963ff46e42072e2752baeb0cf984ad9a87c88e1d5ee0eb25af25f5' model_name_v2 = 'gxnor_mnist.h5' file_hash_v2 = '83537b8f24acd843ecf4645f0b7286c6ae90868973298ebb67f2a078797d6055' model_path, model_name, file_hash = get_model_path("gxnor", model_name_v1, file_hash_v1, model_name_v2, file_hash_v2) model_path = fetch_file(model_path, fname=model_name, file_hash=file_hash, cache_subdir='models') return load_model(model_path)