alexnet.py

# import tflearn.datasets.oxflower17 as oxflower17
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten,\
    Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
import numpy as np


def alexnet():
    model = Sequential()
# 1st Convolutional Layer
    model.add(Conv2D(filters=96, input_shape=(224, 224, 1), kernel_size=(11, 11),
                     strides=(4, 4), padding='valid'))
    model.add(Activation('relu'))
    # Pooling
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))
    # Batch Normalisation before passing it to the next layer
    model.add(BatchNormalization())

    # 2nd Convolutional Layer
    model.add(Conv2D(filters=256, kernel_size=(
        11, 11), strides=(1, 1), padding='valid'))
    model.add(Activation('relu'))
    # Pooling
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))
    # Batch Normalisation
    model.add(BatchNormalization())

    # 3rd Convolutional Layer
    model.add(Conv2D(filters=384, kernel_size=(
        3, 3), strides=(1, 1), padding='valid'))
    model.add(Activation('relu'))
    # Batch Normalisation
    model.add(BatchNormalization())

    # 4th Convolutional Layer
    model.add(Conv2D(filters=384, kernel_size=(
        3, 3), strides=(1, 1), padding='valid'))
    model.add(Activation('relu'))
    # Batch Normalisation
    model.add(BatchNormalization())

    # 5th Convolutional Layer
    model.add(Conv2D(filters=256, kernel_size=(
        3, 3), strides=(1, 1), padding='valid'))
    model.add(Activation('relu'))
    # Pooling
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))
    # Batch Normalisation
    model.add(BatchNormalization())

    # Passing it to a dense layer
    model.add(Flatten())
    # 1st Dense Layer
    model.add(Dense(4096, input_shape=(224*224*1,)))
    model.add(Activation('relu'))
    # Add Dropout to prevent overfitting
    model.add(Dropout(0.4))
    # Batch Normalisation
    model.add(BatchNormalization())

    # 2nd Dense Layer
    model.add(Dense(4096))
    model.add(Activation('relu'))
    # Add Dropout
    model.add(Dropout(0.4))
    # Batch Normalisation
    model.add(BatchNormalization())

    # 3rd Dense Layer
    model.add(Dense(1000))
    model.add(Activation('relu'))
    # Add Dropout
    model.add(Dropout(0.4))
    # Batch Normalisation
    model.add(BatchNormalization())

    # Output Layer
    model.add(Dense(9))
    model.add(Activation('softmax'))

    model.summary()
    optimizer = Adam(lr=0.0001)

    # (4) Compile
    model.compile(loss='categorical_crossentropy', optimizer=optimizer,
                  metrics=['accuracy'])
    return model