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vgg.py
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vgg.py
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"""
VGG16/VGG19 Keras Implementation
BibTeX Citation:
@article{simonyan2014very,
title={Very deep convolutional networks for large-scale image recognition},
author={Simonyan, Karen and Zisserman, Andrew},
journal={arXiv preprint arXiv:1409.1556},
year={2014}
}
"""
# Import necessary packages
import argparse
# Import necessary components to build LeNet
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.regularizers import l2
def vgg16_model(img_shape=(224, 224, 3), n_classes=1000, l2_reg=0.,
weights=None):
# Initialize model
vgg16 = Sequential()
# Layer 1 & 2
vgg16.add(Conv2D(64, (3, 3), padding='same',
input_shape=img_shape, kernel_regularizer=l2(l2_reg)))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(64, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 3 & 4
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(128, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(128, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 5, 6, & 7
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(256, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(256, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(256, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(MaxPooling2D(pool_size=(2, 2)))
# Layers 8, 9, & 10
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(512, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(512, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(512, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(MaxPooling2D(pool_size=(2, 2)))
# Layers 11, 12, & 13
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(512, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(512, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(ZeroPadding2D((1, 1)))
vgg16.add(Conv2D(512, (3, 3), padding='same'))
vgg16.add(Activation('relu'))
vgg16.add(MaxPooling2D(pool_size=(2, 2)))
# Layers 14, 15, & 16
vgg16.add(Flatten())
vgg16.add(Dense(4096))
vgg16.add(Activation('relu'))
vgg16.add(Dropout(0.5))
vgg16.add(Dense(4096))
vgg16.add(Activation('relu'))
vgg16.add(Dropout(0.5))
vgg16.add(Dense(n_classes))
vgg16.add(Activation('softmax'))
if weights is not None:
vgg16.load_weights(weights)
return vgg16
def vgg19_model(img_shape=(224, 224, 3), n_classes=1000, l2_reg=0.,
weights=None):
# Initialize model
vgg19 = Sequential()
# Layer 1 & 2
vgg19.add(Conv2D(64, (3, 3), padding='same',
input_shape=img_shape, kernel_regularizer=l2(l2_reg)))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(64, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 3 & 4
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(128, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(128, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 5, 6, 7, & 8
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(256, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(256, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(256, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(256, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(MaxPooling2D(pool_size=(2, 2)))
# Layers 9, 10, 11, & 12
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(MaxPooling2D(pool_size=(2, 2)))
# Layers 13, 14, 15, & 16
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(ZeroPadding2D((1, 1)))
vgg19.add(Conv2D(512, (3, 3), padding='same'))
vgg19.add(Activation('relu'))
vgg19.add(MaxPooling2D(pool_size=(2, 2)))
# Layers 17, 18, & 19
vgg19.add(Flatten())
vgg19.add(Dense(4096))
vgg19.add(Activation('relu'))
vgg19.add(Dropout(0.5))
vgg19.add(Dense(4096))
vgg19.add(Activation('relu'))
vgg19.add(Dropout(0.5))
vgg19.add(Dense(n_classes))
vgg19.add(Activation('softmax'))
if weights is not None:
vgg19.load_weights(weights)
return vgg19
def parse_args():
"""
Parse command line arguments.
Parameters:
None
Returns:
parser arguments
"""
parser = argparse.ArgumentParser(description='vgg16 model')
optional = parser._action_groups.pop()
required = parser.add_argument_group('required arguments')
optional.add_argument('--print_model',
dest='print_model',
help='Print vgg16 model',
action='store_true')
parser._action_groups.append(optional)
return parser.parse_args()
if __name__ == "__main__":
# Command line parameters
args = parse_args()
# Create VGG16 model
model = vgg16_model()
# Print
if args.print_model:
model.summary()
# Create VGG19 model
model = vgg19_model()
# Print
if args.print_model:
model.summary()