BT4221 Assignment 4 Question 3

question3.py

from keras.datasets import mnist
from keras.utils import np_utils
from keras.layers import Input, Dense, Dropout, Activation, Flatten, Convolution2D, ZeroPadding2D, MaxPooling2D
from keras.models import Model

batch_size = 128
nb_classes = 10
epochs = 10

# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (1, 1)

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

# input functions for CNN
input_layer = Input(shape=(input_shape))
layers = ZeroPadding2D((1,1))(input_layer)
layers = Convolution2D(nb_filters, (3, 3), activation='relu')(layers)
layers = ZeroPadding2D((1,1))(input_layer)
layers = Convolution2D(nb_filters, (3, 3), activation='relu')(layers)
layers = ZeroPadding2D((1,1))(input_layer)
layers = Convolution2D(nb_filters, (3, 3), activation='relu')(layers)
layers = ZeroPadding2D((1,1))(input_layer)
layers = Convolution2D(nb_filters, (3, 3), activation='relu')(layers)
layers = ZeroPadding2D((1,1))(layers)
layers = MaxPooling2D(pool_size, strides=(2,2))(layers)
layers= Flatten()(layers)
layers= Dense(128, activation='relu')(layers)
output_layer=Dense(10, activation='sigmoid')(layers)

model = Model(inputs=input_layer, outputs=output_layer)

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs,
          verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])