alexnet_modified.py

# import the necessary packages
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dense
from keras.layers.core import Dropout
from keras import backend as K


class networkArchFonc:
    @staticmethod
    def build(width, height, depth, classes):
        # initialize the model
        model = Sequential()
        inputShape = (height, width, depth)

        # if we are using "channels first", update the input shape
        if K.image_data_format() == "channels_first":
            inputShape = (depth, height, width)

        # 1st Convolutional Layer
        model.add(Conv2D(filters=96, input_shape=inputShape, kernel_size=(11, 11), strides=(4, 4), padding='valid'))
        model.add(Activation('relu'))
        # Max Pooling
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))

        # 2nd Convolutional Layer
        model.add(Conv2D(filters=96, kernel_size=(11, 11), strides=(1, 1), padding='valid'))
        model.add(Activation('relu'))
        # Max Pooling
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))

        # 3rd Convolutional Layer
        model.add(Conv2D(filters=96, kernel_size=(3, 3), strides=(1, 1), padding='valid'))
        model.add(Activation('relu'))

        # 4th Convolutional Layer
        model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='valid'))
        model.add(Activation('relu'))

        model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='valid'))
        model.add(Activation('relu'))

        # 5th Convolutional Layer
        model.add(Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), padding='valid'))
        model.add(Activation('relu'))
        # Max Pooling
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))

        # Passing it to a Fully Connected layer
        model.add(Flatten())
        # 1st Fully Connected Layer
        model.add(Dense(4096, input_shape=inputShape))
        model.add(Activation('relu'))
        # Add Dropout to prevent overfitting
        model.add(Dropout(0.4))

        # 2nd Fully Connected Layer
        model.add(Dense(4096))
        model.add(Activation('relu'))
        # Add Dropout
        model.add(Dropout(0.4))

             # 3rd Fully Connected Layer
        model.add(Dense(1000))
        model.add(Activation('relu'))
        # Add Dropout
        model.add(Dropout(0.4))

        # Output Layer
        model.add(Dense(classes))
        model.add(Activation('softmax'))

        # return the constructed network architecture
        return model