conv-net-mnist.py

print('Convnet solution to MNIST dataset')

from hack import hack

hack()

from keras import datasets

from keras.utils.np_utils import to_categorical

(train_images, train_labels), (validation_images, validation_labels) = datasets.mnist.load_data()

train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255

validation_images = validation_images.reshape((10000, 28, 28, 1))
validation_images = validation_images.astype('float32') / 255

train_labels = to_categorical(train_labels)
validation_labels = to_categorical(validation_labels)

from keras import models
from keras import layers

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

model.summary()

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

model.fit(train_images, train_labels, epochs=5, batch_size=64)

test_loss, test_accuracy = model.evaluate(validation_images, validation_labels)

print(test_accuracy)