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vgg_16.py
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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