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")