In this tutorial, we are going to use Convolutional Neural Network 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 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.