(self, dev, num=400)
| 342 | return categorical |
| 343 | |
| 344 | def generate_data(self, dev, num=400): |
| 345 | f = lambda x: (5 * x + 1) |
| 346 | |
| 347 | x = np.random.uniform(-1, 1, num) |
| 348 | y = f(x) + 2 * np.random.randn(len(x)) |
| 349 | |
| 350 | self.label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)]) |
| 351 | self.data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np.float32) |
| 352 | self.label = self.to_categorical(self.label, 2).astype(np.float32) |
| 353 | |
| 354 | self.inputs = Tensor(data=self.data, device=dev) |
| 355 | self.target = Tensor(data=self.label, device=dev) |
| 356 | |
| 357 | def get_params(self, model): |
| 358 | params = model.get_params() |
no test coverage detected