#!/usr/bin/env python
# ******************************************************************************
# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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
<https://www.sciencedirect.com/science/article/pii/S0893608018300108>`_.
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_i4_w4_a4.h5'
file_hash_v2 = 'b6ff95699525666a6f43600d002f8a8d63c389875066e8ce1c4d9fdd002ceeb5'
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)