Source code for akida_models.cwru.model_convtiny

#!/usr/bin/env python
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# Copyright 2021 Brainchip Holdings Ltd.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""
Convtiny model definition for CWRU classification.
"""

from keras import Model
from keras.layers import Input, Softmax, Rescaling, Reshape
from keras.utils.data_utils import get_file

from cnn2snn import load_quantized_model

from ..layer_blocks import conv_block, separable_conv_block

BASE_WEIGHT_PATH = 'http://data.brainchip.com/models/convtiny/'


[docs]def convtiny_cwru(): """ Instantiates a CNN for CWRU classification with input shape (32, 32, 1) and 10 classes. Returns: keras.Model: a Keras model for Convtiny/CWRU """ img_input = Input(shape=(32, 32, 1)) x = Rescaling(1, -127)(img_input) x = conv_block(x, filters=32, name='conv_1', kernel_size=(7, 7), padding='same', add_activation=True, pooling='max', pool_size=(2, 2)) x = conv_block(x, filters=32, name='conv_2', kernel_size=(7, 7), padding='same', add_activation=True, pooling='max', pool_size=(2, 2)) x = separable_conv_block(x, filters=512, name='separable_1', kernel_size=(3, 3), padding='same', pooling='global_avg', use_bias=False, add_batchnorm=True, add_activation=True) x = Reshape((1, 1, int(512)), name='reshape_1')(x) x = separable_conv_block(x, filters=10, name='predictions', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=False, add_activation=False) x = Reshape((10,), name='reshape_2')(x) x = Softmax(name='act_softmax')(x) return Model(img_input, x, name='convtiny_cwru_32_10')
[docs]def convtiny_cwru_pretrained(): """ Helper method to retrieve a `convtiny_cwru` model that was trained on CWRU dataset. Returns: keras.Model: a Keras Model instance. """ model_name = 'convtiny_cwru_iq8_wq2_aq4.h5' file_hash = '15bc18ea85942185d90dbcd35300d86f8751bfd19ff175589d81e05dc2854d40' model_path = get_file(fname=model_name, origin=BASE_WEIGHT_PATH + model_name, file_hash=file_hash, cache_subdir='models') return load_quantized_model(model_path)