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alexnet.py
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alexnet.py
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"""
AlexNet Keras Implementation
BibTeX Citation:
@inproceedings{krizhevsky2012imagenet,
title={Imagenet classification with deep convolutional neural networks},
author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle={Advances in neural information processing systems},
pages={1097--1105},
year={2012}
}
"""
# Import necessary packages
import argparse
# Import necessary components to build LeNet
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
def alexnet_model(img_shape=(224, 224, 3), n_classes=10, l2_reg=0.,
weights=None):
# Initialize model
alexnet = Sequential()
# Layer 1
alexnet.add(Conv2D(96, (11, 11), input_shape=img_shape,
padding='same', kernel_regularizer=l2(l2_reg)))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 2
alexnet.add(Conv2D(256, (5, 5), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 3
alexnet.add(ZeroPadding2D((1, 1)))
alexnet.add(Conv2D(512, (3, 3), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 4
alexnet.add(ZeroPadding2D((1, 1)))
alexnet.add(Conv2D(1024, (3, 3), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
# Layer 5
alexnet.add(ZeroPadding2D((1, 1)))
alexnet.add(Conv2D(1024, (3, 3), padding='same'))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 6
alexnet.add(Flatten())
alexnet.add(Dense(3072))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(Dropout(0.5))
# Layer 7
alexnet.add(Dense(4096))
alexnet.add(BatchNormalization())
alexnet.add(Activation('relu'))
alexnet.add(Dropout(0.5))
# Layer 8
alexnet.add(Dense(n_classes))
alexnet.add(BatchNormalization())
alexnet.add(Activation('softmax'))
if weights is not None:
alexnet.load_weights(weights)
return alexnet
def parse_args():
"""
Parse command line arguments.
Parameters:
None
Returns:
parser arguments
"""
parser = argparse.ArgumentParser(description='AlexNet model')
optional = parser._action_groups.pop()
required = parser.add_argument_group('required arguments')
optional.add_argument('--print_model',
dest='print_model',
help='Print AlexNet model',
action='store_true')
parser._action_groups.append(optional)
return parser.parse_args()
if __name__ == "__main__":
# Command line parameters
args = parse_args()
# Create AlexNet model
model = alexnet_model()
# Print
if args.print_model:
model.summary()