| import tensorflow_datasets as tfds | |
| import numpy as np | |
| def ds_to_numpy(ds): | |
| images = [] | |
| labels = [] | |
| for image, label in ds: | |
| images.append(image.numpy()) | |
| labels.append(label.numpy()) | |
| return np.array(images), np.array(labels) | |
| train_ds, test_ds = tfds.load("mnist", split=["train", "test"], as_supervised=True) | |
| train_images, train_labels = ds_to_numpy(train_ds) | |
| test_images, test_labels = ds_to_numpy(test_ds) | |
| train_images = train_images.astype(np.float32) / 255.0 | |
| test_images = test_images.astype(np.float32) / 255.0 | |
| train_labels = train_labels.astype(np.float32) | |
| test_labels = test_labels.astype(np.float32) | |
| train_images.tofile("train_images.mat") | |
| train_labels.tofile("train_labels.mat") | |
| test_images.tofile("test_images.mat") | |
| test_labels.tofile("test_labels.mat") | |
| print(train_images.shape) | |
| print(train_labels.shape) | |
| print(test_images.shape) | |
| print(test_labels.shape) | |