In this blog, we are going to use Convolutional Neural Network (CNN) to do image classification. The following figure shows the comparison between a 3-layer Neural Network and a simple Convolutional Neural Network. If you are interested in CNN, you can refer this paper which proposes AlexNet.

1. A simple Convolutional Neural Network on MNIST dataset as an example.

import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D

# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')

# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

# create model
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)

# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))	

2. Assignment

Please run the above code before you design yours. You will notice that using a CNN model gains a higher accuracy than the Neural Netowork on MNIST dataset. Design your own CNN to do Image Classification on Boat Dataset. Boat Dataset consists of 5 different types of boats:

• Training Dataset (249.6 MB) Download

  Class	Number of images
  aircraft_carrier	500
  banana_boat	500
  oil_tanker	500
  passenger_ship	500
  yacht	500
  In total	2500

• Testing Dataset (97.1 MB, 1000 images) Download

Train your model on training dataset and test the trained model on testing dataset.

Hint

• Data augmentation (search how to do data augmentation in Keras)
• Finetune a pre-trained CNN model. (refer to: keras.applications)

3. Submite your sulotion:

• Your final python code. Please name it using your Lehigh ID. (<your_LehighID>.py)
• A short <your_LehighID>.pdf file. Simply describe what you did, what you got, and other things you want to report, e.g. what you have learned.