def main():
# load MNIST images
images, labels = dataset.load_train_images()
# config
config = model.config
# settings
max_epoch = 10000
num_trains_per_epoch = 5000
num_validation_data = 10000
batchsize = 128
# seed
np.random.seed(args.seed)
if args.gpu_device != -1:
cuda.cupy.random.seed(args.seed)
# save validation accuracy per epoch
csv_results = []
# create semi-supervised split
training_images, training_labels, validation_images, validation_labels = dataset.split_data(images, labels, num_validation_data, seed=args.seed)
training_labels = np.random.randint(0, config.num_classes, training_labels.size).astype(np.int32)
validation_labels = np.random.randint(0, config.num_classes, validation_labels.size).astype(np.int32)
# training
progress = Progress()
for epoch in xrange(1, max_epoch):
progress.start_epoch(epoch, max_epoch)
sum_loss = 0
for t in xrange(num_trains_per_epoch):
# sample from data distribution
image_batch, label_batch = dataset.sample_data(training_images, training_labels, batchsize, binarize=False)
image_batch = np.reshape(image_batch, (-1, 1, 28, 28))
distribution = model.discriminate(image_batch, apply_softmax=False)
loss = F.softmax_cross_entropy(distribution, model.to_variable(label_batch))
sum_loss += float(loss.data)
model.backprop(loss)
if t % 10 == 0:
progress.show(t, num_trains_per_epoch, {})
model.save(args.model_dir)
train_accuracy = compute_accuracy(training_images, training_labels)
validation_accuracy = compute_accuracy(validation_images, validation_labels)
progress.show(num_trains_per_epoch, num_trains_per_epoch, {
"loss": sum_loss / num_trains_per_epoch,
"accuracy (validation)": validation_accuracy,
"accuracy (train)": train_accuracy,
})
# write accuracy to csv
csv_results.append([epoch, train_accuracy, validation_accuracy, progress.get_total_time()])
data = pd.DataFrame(csv_results)
data.columns = ["epoch", "train_accuracy", "validation_accuracy", "min"]
data.to_csv("{}/result.csv".format(args.model_dir))
def compute_accuracy(image_batch, label_batch): num_data = image_batch.shape[0] images_l_segments = np.split(image_batch, num_data // 500) label_ids_l_segments = np.split(label_batch, num_data // 500) sum_accuracy = 0 for image_batch, label_batch in zip(images_l_segments, label_ids_l_segments): distribution = model.discriminate(image_batch, apply_softmax=True, test=True) accuracy = F.accuracy(distribution, model.to_variable(label_batch)) sum_accuracy += float(accuracy.data) return sum_accuracy / len(images_l_segments)