def knn(image, method='euclidean', k=3): train_samples = MNIST.data['train'] np.random.shuffle(train_samples) image = MNIST.flatten_image(image) nearest_dist = [np.inf for _ in range(k)] nearest_lbls = [-1 for _ in range(k)] for sample in train_samples[0:999]: candidate = MNIST.flatten_image(sample['image']) dist = distances(image, candidate, method) if dist < np.max(nearest_dist): max_idx = np.argmax(nearest_dist) nearest_dist[max_idx] = dist nearest_lbls[max_idx] = sample['label'] return np.argmax(np.bincount(nearest_lbls))
self.terminal = sys.stdout
self.log = open(time.strftime("./Logs/%Y%m%d-%H%M%S-logfile.log"), "a")
def write(self, message):
self.terminal.write(message)
self.log.write(message)
def flush(self):
self.log.flush()
self.terminal.flush()
pass
sys.stdout = Logger()
train_count = 60000
train_labels = MNIST.label(*range(train_count), setname='train', decode=True)
train_samples = MNIST.image(*range(train_count), setname='train', flat=True, normalize=True)
test_count = 9000
test_labels = MNIST.label(*range(test_count), setname='test', decode=True)
test_samples = MNIST.image(*range(test_count), setname='test', flat=True, normalize=True)
mlp = MLP([784, 30, 10])
epoch_count = 10
train_errors = np.zeros(epoch_count)
test_errors = np.zeros(epoch_count)
begin_time = time.time()
print(time.strftime("Began training at %Y%m%d-%H%M%S"))
print()
