|
134 | 134 | " nb_classes=10,\n", |
135 | 135 | " clip_values=(0, 1),\n", |
136 | 136 | " processor=upsampler)\n", |
137 | | - " hf_model.fit(x_train, y_train, nb_epochs=2, display_progress_bar=True)\n", |
| 137 | + " hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n", |
138 | 138 | " return hf_model" |
139 | 139 | ] |
140 | 140 | }, |
|
701 | 701 | " clip_values=(0, 1),\n", |
702 | 702 | " processor=upsampler)\n", |
703 | 703 | " (x_train, y_train), (x_test, y_test) = get_cifar_data(train_on_subset)\n", |
704 | | - " hf_model.fit(x_train, y_train, nb_epochs=2, display_progress_bar=True)\n", |
| 704 | + " hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n", |
705 | 705 | "\n", |
706 | 706 | " return hf_model" |
707 | 707 | ] |
|
1010 | 1010 | " processor=None) # No processor is needed as the data is of the correct size for the model.\n", |
1011 | 1011 | " \n", |
1012 | 1012 | " (x_train, y_train), (x_test, y_test) = get_cifar_data()\n", |
1013 | | - " simple_hf_classifier.fit(x_train, y_train, nb_epochs=20, display_progress_bar=True)\n", |
| 1013 | + " simple_hf_classifier.fit(x_train, y_train, nb_epochs=20, verbose=True)\n", |
1014 | 1014 | "\n", |
1015 | 1015 | " num_samples = 100\n", |
1016 | 1016 | " outputs = simple_hf_classifier.predict(x_test[:num_samples])\n", |
|
1363 | 1363 | "name": "python", |
1364 | 1364 | "nbconvert_exporter": "python", |
1365 | 1365 | "pygments_lexer": "ipython3", |
1366 | | - "version": "3.10.12" |
| 1366 | + "version": "3.8.10" |
1367 | 1367 | } |
1368 | 1368 | }, |
1369 | 1369 | "nbformat": 4, |
|
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