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AlexNet.py
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AlexNet.py
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from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense, Activation
from keras.layers.convolutional import Convolution2D, MaxPooling2D, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
"""
AlexNet
"""
def AlexNet():
model = Sequential()
model.add(Conv2D(filters=96, input_shape=(224,224,3), kernel_size=(11,11),strides=(4,4), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
model.add(BatchNormalization())
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
model.add(BatchNormalization())
# Dense layer
model.add(Flatten())
model.add(Dense(4096, input_shape=(224*224*3,)))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(1000))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
# Output Layer
model.add(Dense(6))
model.add(Activation('softmax'))
return model