ML | Getting Started With AlexNet (original) (raw)

`model = Sequential()

Layer 1

model.add(Conv2D(96, kernel_size=(3,3), strides=(1,1), input_shape=(32,32,3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2))) model.add(BatchNormalization())

Layer 2

model.add(Conv2D(256, kernel_size=(3,3), strides=(1,1), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2))) model.add(BatchNormalization())

Layer 3

model.add(Conv2D(384, kernel_size=(3,3), strides=(1,1), padding='same')) model.add(Activation('relu'))

Layer 4

model.add(Conv2D(384, kernel_size=(3,3), strides=(1,1), padding='same')) model.add(Activation('relu'))

Layer 5

model.add(Conv2D(256, kernel_size=(3,3), strides=(1,1), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

Flatten

model.add(Flatten())

Fully Connected Layer 1

model.add(Dense(1024)) model.add(Activation('relu')) model.add(Dropout(0.5))

Fully Connected Layer 2

model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5))

Output Layer

model.add(Dense(10)) model.add(Activation('softmax'))

`