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