(train_x, val_x)
| 71 | |
| 72 | |
| 73 | def normalize(train_x, val_x): |
| 74 | mean = [0.4914, 0.4822, 0.4465] |
| 75 | std = [0.2023, 0.1994, 0.2010] |
| 76 | train_x /= 255 |
| 77 | val_x /= 255 |
| 78 | for ch in range(0, 2): |
| 79 | train_x[:, ch, :, :] -= mean[ch] |
| 80 | train_x[:, ch, :, :] /= std[ch] |
| 81 | val_x[:, ch, :, :] -= mean[ch] |
| 82 | val_x[:, ch, :, :] /= std[ch] |
| 83 | return train_x, val_x |
| 84 | |
| 85 | def load(): # Need to pass in the path for loading training data |
| 86 | train_x, train_y = load_train_data() |