'''
how you might build a convolutional neural network (CNN) for the MNIST dataset using the Keras deep learning library in Python.
'''
#Load the MNIST dataset
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#Preprocess the data
from keras.utils import to_categorical
x_train = x_train.reshape((x_train.shape[0], 28, 28, 1))
x_train = x_train.astype('float32') / 255.0
y_train = to_categorical(y_train)

x_test = x_test.reshape((x_test.shape[0], 28, 28, 1))
x_test = x_test.astype('float32') / 255.0
y_test = to_categorical(y_test)
#Build the CNN model
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential()
model.add(Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(64, (3,3), activation='relu'))
model.add(MaxPooling2D((2,2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
#Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
#Train the model
model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))
#Evaluate the model
loss, acc = model.evaluate(x_test, y_test)
print('Test Accuracy: %.3f' % (acc * 100))