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from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation, Flatten | |
from keras.layers.convolutional import Convolution2D, MaxPooling2D | |
from keras.layers.normalization import BatchNormalization | |
#AlexNet with batch normalization in Keras | |
#input image is 224x224 | |
model = Sequential() | |
model.add(Convolution2D(64, 3, 11, 11, border_mode='full')) | |
model.add(BatchNormalization((64,226,226))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(poolsize=(3, 3))) | |
model.add(Convolution2D(128, 64, 7, 7, border_mode='full')) | |
model.add(BatchNormalization((128,115,115))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(poolsize=(3, 3))) | |
model.add(Convolution2D(192, 128, 3, 3, border_mode='full')) | |
model.add(BatchNormalization((128,112,112))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(poolsize=(3, 3))) | |
model.add(Convolution2D(256, 192, 3, 3, border_mode='full')) | |
model.add(BatchNormalization((128,108,108))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(poolsize=(3, 3))) | |
model.add(Flatten()) | |
model.add(Dense(12*12*256, 4096, init='normal')) | |
model.add(BatchNormalization(4096)) | |
model.add(Activation('relu')) | |
model.add(Dense(4096, 4096, init='normal')) | |
model.add(BatchNormalization(4096)) | |
model.add(Activation('relu')) | |
model.add(Dense(4096, 1000, init='normal')) | |
model.add(BatchNormalization(1000)) | |
model.add(Activation('softmax')) | |
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AlexNet in Keras