i += 1
#saving model
torch.save(cn.state_dict(), model_save_path)
print('обучение закончено')
cn = ConvNet(num_classes)
cn.load_state_dict(torch.load(model_save_path))
batch = next(iter(signsValidationLoader))
predictions = cn(batch['image'])
y_test = batch['label']
#print(predictions, y_test)
_, predictions = torch.max(predictions, 1)
plt.imshow(PIL.ToPIL(batch['image'][0]))
print('Gound-true:', dataset.labels[batch['label'][0]])
print('Prediction:', dataset.labels[predictions[0]])
#####Кривые обучения
def smooth_curve(points, factor=0.9):
smoothed_points = []
for point in points:
if smoothed_points:
previous = smoothed_points[-1]
smoothed_points.append(previous * factor + point * (1 - factor))
else:
smoothed_points.append(point)
return smoothed_points
