def main_predict() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_04_08_2') model = CNNAugDataRegularized((32, 64, 128, 256), (512, 128), 4, device) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) model.train(data, 18, 20, tracker, learning_rate=0.0001) test_X, imgs_ids = data.get_test() pred_y = model.predict(test_X) create_submit(pred_y, imgs_ids, path.join(save_dir, 'submission.csv'))
def main_predict() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_04_25_1') model = DropoutCNN((32, 64, 128, 256), (512, 128), 4, device, drop_dense_p=0.2, drop_conv_p=0.2) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) model.train(data, 30, 20, tracker, learning_rate=0.0001) test_X, imgs_ids = data.get_test() pred_y = model.predict(test_X) create_submit(pred_y, imgs_ids, path.join(save_dir, 'submission.csv'))
def main() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_05_10_1') model = VGGStyleNet(4, device) summary(model.cuda(), (3, 128, 128)) print(model) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) model.train(data, 40, 64, tracker, learning_rate=0.00001) tracker.graphs() tracker.save('metrics.csv') test_X, imgs_ids = data.get_test() pred_y = model.predict(test_X) create_submit(pred_y, imgs_ids, path.join(save_dir, 'submission.csv'))
def main_train() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_04_08_1') model = CNNAugDataRegularized((32, 64, 128, 256), (1024, 128), 4, device) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) model.train(data, 60, 20, tracker, learning_rate=0.0001) tracker.graphs() tracker.save('metrics.csv')
def main_train() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_04_26_2') model = TransferCNN(device) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) model.train(data, 20, 20, tracker, learning_rate = 0.0001) tracker.graphs() tracker.save('metrics.csv')
def main() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_05_10_2') batch_size = 64 epochs = 15 model = VGGStyleBNNet(4, device) summary(model.cuda(), (3, 128, 128)) print(model) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) try: model.train(data, epochs, batch_size, tracker, learning_rate=0.001) except KeyboardInterrupt: print('Training interrupted, writing stats...') finally: tracker.graphs() tracker.save('metrics.csv') test_X, imgs_ids = data.get_test() pred_y = model.predict(test_X, batch_size) create_submit(pred_y, imgs_ids, path.join(save_dir, 'submission.csv'))
def main_train() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_04_26_1') conv_filter_nums = (16, 32, 64, 64, 128, 128) neuron_nums = (512, 128) model = BigCNN(conv_filter_nums, neuron_nums, device) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) model.train(data, 40, 20, tracker, learning_rate=0.0001) tracker.graphs() tracker.save('metrics.csv')
def main_train() -> None: detect_gpu() device = get_device() save_dir = path.join('modelling', 'model_2020_04_25_1') model = DropoutCNN((32, 64, 128, 256), (512, 128), 4, device, drop_dense_p=0.2, drop_conv_p=0.2) data = FirstAugmentedDataset() tracker = PerformanceTracker(save_dir) model.train(data, 40, 20, tracker, learning_rate=0.0001) tracker.graphs() tracker.save('metrics.csv')