Beispiel #1
0
 def activate(self):
     pred = predict(self.inputs, self.weights)
     if self.activation_function:
         pred = self.activation_function(pred) + self.bias
     self.inputs = [None] * len(self.inputs)
     for cb in self.observers:
         cb(pred)
Beispiel #2
0
def simple_case():
	features = [1.0, 3.0, 2.0]
	weights = [0, 0, 0]
	goal_pred = 10.0
	alpha = 1e-2
	times = 100
	print(f"goal_prediction={goal_pred}")
	label = predict(features, weights)
	print(f"*Before training*")
	print(f"prediction={label}")
	print(f"*After training*")
	weights = train(features, weights, goal_pred, times, alpha)
	label = predict(features, weights)
	print(f"prediction={label}")
	print(f"confidence={round(100-(abs(label-goal_pred))*100/(label+goal_pred), 2)}%")
	print(f"weights={weights}")
Beispiel #3
0
def main():
    model=base.loadchkpnt(path)
    with open('cat_to_name.json', 'r') as jfil:
    	cat_to_name = json.load(jfil)
    probs = base.predict(image_path, model, topk, gpu)
    classes = [cat_to_name[str(ix + 1)] for ix in np.array(probs[1][0])]
    probability = np.array(probs[0][0])
    i=0
    while i < topk:
        print("{} has probability: {}".format(classes[i], probability[i]))
        i += 1
Beispiel #4
0
def do_single(r):
	global training_x, training_y, testing_x, testing_y
	wl1, wl2 = doTrain(_g_M, _g_eta, r, _g_T, training_x.copy(), training_y.copy())
	eout = predict(wl1, wl2, testing_x.copy(), testing_y.copy())
	print "r:", r, ", eout:", eout
	return r, eout
                default='cat_to_name.json')
ap.add_argument(
    'image_path',
    default='/home/workspace/ImageClassifier/flowers/test/1/image_06752.jpg',
    nargs='*',
    action="store",
    type=str)

inputs = ap.parse_args()

image_path = inputs.image_path
topk = inputs.top_k
device = inputs.gpu
path = inputs.checkpoint

dataloaders, image_datasets = base.load_data()

model = base.load_checkpoint(path)

base.testdata_acc(model, dataloaders, image_datasets, 'test', True)

with open('cat_to_name.json', 'r') as json_file:
    cat_to_name = json.load(json_file)

img_tensor = base.process_image(image_path)

probs = base.predict(image_path, model, topk)

print("Image Directory: ", image_path)
print("Predictions probabilities: ", probs)
Beispiel #6
0
def do_single(M):
	global training_x, training_y, testing_x, testing_y
	wl1, wl2 = doTrain(M, _g_eta, _g_w_value_range, _g_T, training_x.copy(), training_y.copy())
	eout = predict(wl1, wl2, testing_x.copy(), testing_y.copy())
	return M, eout