Wes weklund

I hereby claim:

To claim this, I am signing this object:

	LMPD

	UNMANNED VEHICLE,GROUND 1 Each $298,861.00 Q 3 Oct 4, 2018 12:00:00 AM
	HELMET,FLYER'S 10 Each $886.57 Q 3 Dec 8, 2016 12:00:00 AM
	SPECTACLES SET,BALLISTIC AND LASER PROTECTIVE 35 Each $28.27 D 1 May 2, 2010 12:00:00 AM
	SPECTACLES,BALLISTIC AND LASER PROTECTIVE 50 Each $20.16 D 1 Jul 30, 2010 12:00:00 AM
	SPECTACLES KIT,BALLISTIC AND LASER PROTECTIVE 45 Kit $15.16 D 1 Oct 28, 2009 12:00:00 AM
	HELMET,FLYER'S 1 Each $449.88 C Mar 2, 2010 12:00:00 AM
	HELMET,ADVANCED COMBAT 18 Each $276.71 D 1 Nov 23, 2009 12:00:00 AM
	HELMET,ADVANCED COMBAT 9 Each $223.69 D 1 Sep 30, 2009 12:00:00 AM

	def path_to_tensor(path):

	img = image.load_img(path, target_size = (224,224))
	x= image.img_to_array(img)
	return np.expand_dims(x, axis=0)

	def paths_to_tensor(paths):
	list_of_tensors = [path_to_tensor(path) for path in tqdm(paths)]
	return np.vstack(list_of_tensors)

	def load_dataset(path):
	data = load_files(path, shuffle=True)
	img_files = np.array(data['filenames'])
	img_targets = np_utils.to_categorical(np.array(data['target']), 3)
	return img_files, img_targets

	train_files, train_labels = load_dataset('data/train')
	valid_files, valid_labels = load_dataset('data/valid')

	def load_dataset_no_shuffle(path):

	from keras.callbacks import ModelCheckpoint

	checkpoint_inception = ModelCheckpoint(
	save_best_only = True,
	verbose = 1,
	filepath = 'saved_models/weights.best.from_inception_resnet_v2.hdf5'
	)

	model.fit(
	train_features_inception,

	submission_inception_resnet = pd.DataFrame({'Id':test_files, 'task_1':test_predictions_task1,'task_2':test_predictions_task2})
	pd.DataFrame.to_csv(submission_inception_resnet, 'submission.csv', index=False)

	preds_path = sys.argv[1]

	thresh = 0.5

	# get ground truth labels for test dataset
	truth = pd.read_csv('ground_truth.csv')
	y_true = truth.as_matrix(columns=["task_1", "task_2"])

	model = InceptionResNetV2(weights = 'imagenet', include_top = False)
	model = Sequential()

	model.add(GlobalAveragePooling2D(input_shape = train_features_inception.shape[1:]))
	model.add(Dropout(0.2))
	model.add(Dense(1024, activation = 'relu'))
	model.add(Dropout(0.2))
	model.add(Dense(512, activation = 'relu'))
	model.add(Dropout(0.2))
	model.add(Dense(128, activation = 'relu'))

	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console