with open(name, 'w') as f: for i in arr: f.write(str(i) + '\n') if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('-r', dest='data_raw') parser.add_argument('-p', dest='data_processed') parser.add_argument('-m', dest='model') args = parser.parse_args() frames = np.load(args.data_raw) processed = np.load(args.data_processed) INPUT_SHAPE = frames[0].shape model = VGG(INPUT_SHAPE, 1, 1, args.model, True) predictions = [] for i in range(len(frames)): pred = model.predict([ processed[i].reshape(1, processed[i].shape[0], processed[i].shape[1], processed[i].shape[2]), frames[i].reshape(1, frames[i].shape[0], frames[i].shape[1], frames[i].shape[2]) ]) predictions.append(pred[0][0]) write_txt('deliverable.txt', predictions)
import os import numpy as np import pandas as pd from argparse import ArgumentParser from sklearn.model_selection import roc_curve, balanced_accuracy_score from vgg import VGG if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('-t', dest='testset') parser.add_argument('-l', dest='labels') parser.add_argument('-m', dest='model') args = parser.parse_args() frames = np.load(args.testset) labels = np.load(args.labels) INPUT_SHAPE = frames[0].shape model = VGG(INPUT_SHAPE, 1, 1, args.model) predictions = model.predict(frames) print(roc_curve(labels, predictions)) print(balanced_accuracy_score(labels, predictions))
parser.add_argument('-e', dest='epochs') parser.add_argument('-b', dest='batch_size') parser.add_argument('-f', dest='model_name') parser.add_argument('-d', dest='dual') args = parser.parse_args() train_X = np.load('cached/train_X_sub.npy') train_y = np.load('cached/train_y.npy') test_X = np.load('cached/test_X_sub.npy') test_y = np.load('cached/test_y.npy') INPUT_SHAPE = train_X[0].shape if args.dual: print("dual ran") raw_train = np.load('cached/train_X_raw.npy') raw_test = np.load('cached/test_X_raw.npy') train_X = [train_X, raw_train] test_X = [test_X, raw_test] model = VGG(INPUT_SHAPE, args.epochs, args.batch_size, args.model_name, int(args.dual)) print("Training model") print("Train running!") model.predict(train_X) print("Testing running!") model.predict(test_X) model.train(train_X, train_y, test_X, test_y) print("Finished training")