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working on model Multimodal-middle-fusion-model-based-on-AlexNet with RANDOM
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
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0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:06<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:06<00:56, 6.24s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:06<00:56, 6.24s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:12<00:56, 6.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:12<00:50, 6.31s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:12<00:50, 6.31s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:18<00:50, 6.31s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:19<00:44, 6.40s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:19<00:44, 6.40s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:25<00:44, 6.40s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 40%|████ | 4/10 [00:25<00:38, 6.43s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:25<00:38, 6.43s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:32<00:38, 6.43s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 50%|█████ | 5/10 [00:32<00:32, 6.50s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:32<00:32, 6.50s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:38<00:32, 6.50s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 60%|██████ | 6/10 [00:38<00:26, 6.59s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:38<00:26, 6.59s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:45<00:26, 6.59s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 70%|███████ | 7/10 [00:45<00:19, 6.63s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:45<00:19, 6.63s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:52<00:19, 6.63s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 80%|████████ | 8/10 [00:52<00:13, 6.75s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:52<00:13, 6.75s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:59<00:13, 6.75s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:59<00:06, 6.85s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:59<00:06, 6.85s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [01:06<00:06, 6.85s/it, data_size=11, test_acc=0.282, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [01:06<00:00, 6.96s/it, data_size=11, test_acc=0.282, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [01:06<00:00, 6.70s/it, data_size=11, test_acc=0.282, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:06<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:06<00:56, 6.31s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:06<00:56, 6.31s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:12<00:56, 6.31s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:12<00:50, 6.32s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:12<00:50, 6.32s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:18<00:50, 6.32s/it, data_size=4, test_acc=0.5, train_acc=nan] Test 4: Data size 4: : 30%|███ | 3/10 [00:19<00:44, 6.35s/it, data_size=4, test_acc=0.5, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:19<00:44, 6.35s/it, data_size=4, test_acc=0.5, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:25<00:44, 6.35s/it, data_size=5, test_acc=0.282, train_acc=nan]Test 4: Data size 5: : 40%|████ | 4/10 [00:25<00:38, 6.42s/it, data_size=5, test_acc=0.282, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:25<00:38, 6.42s/it, data_size=5, test_acc=0.282, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:32<00:38, 6.42s/it, data_size=6, test_acc=0.495, train_acc=nan]Test 4: Data size 6: : 50%|█████ | 5/10 [00:32<00:32, 6.50s/it, data_size=6, test_acc=0.495, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:32<00:32, 6.50s/it, data_size=6, test_acc=0.495, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:38<00:32, 6.50s/it, data_size=7, test_acc=0.495, train_acc=nan]Test 4: Data size 7: : 60%|██████ | 6/10 [00:38<00:26, 6.55s/it, data_size=7, test_acc=0.495, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:38<00:26, 6.55s/it, data_size=7, test_acc=0.495, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:45<00:26, 6.55s/it, data_size=8, test_acc=0.661, train_acc=nan]Test 4: Data size 8: : 70%|███████ | 7/10 [00:45<00:19, 6.61s/it, data_size=8, test_acc=0.661, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:45<00:19, 6.61s/it, data_size=8, test_acc=0.661, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:52<00:19, 6.61s/it, data_size=9, test_acc=0.637, train_acc=0.625]Test 4: Data size 9: : 80%|████████ | 8/10 [00:52<00:13, 6.73s/it, data_size=9, test_acc=0.637, train_acc=0.625]Test 4: Data size 10: : 80%|████████ | 8/10 [00:52<00:13, 6.73s/it, data_size=9, test_acc=0.637, train_acc=0.625]Test 4: Data size 10: : 80%|████████ | 8/10 [00:59<00:13, 6.73s/it, data_size=10, test_acc=0.69, train_acc=0.75] Test 4: Data size 10: : 90%|█████████ | 9/10 [00:59<00:06, 6.83s/it, data_size=10, test_acc=0.69, train_acc=0.75]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:59<00:06, 6.83s/it, data_size=10, test_acc=0.69, train_acc=0.75]Test 4: Data size 11: : 90%|█████████ | 9/10 [01:06<00:06, 6.83s/it, data_size=11, test_acc=0.69, train_acc=0.75]Test 4: Data size 11: : 100%|██████████| 10/10 [01:06<00:00, 6.95s/it, data_size=11, test_acc=0.69, train_acc=0.75]Test 4: Data size 11: : 100%|██████████| 10/10 [01:06<00:00, 6.68s/it, data_size=11, test_acc=0.69, train_acc=0.75]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:06<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:06<00:56, 6.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:06<00:56, 6.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:12<00:56, 6.25s/it, data_size=3, test_acc=0.293, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:12<00:50, 6.27s/it, data_size=3, test_acc=0.293, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:12<00:50, 6.27s/it, data_size=3, test_acc=0.293, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:18<00:50, 6.27s/it, data_size=4, test_acc=0.25, train_acc=nan] Test 5: Data size 4: : 30%|███ | 3/10 [00:18<00:44, 6.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:18<00:44, 6.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:25<00:44, 6.30s/it, data_size=5, test_acc=0.328, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:25<00:38, 6.34s/it, data_size=5, test_acc=0.328, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:25<00:38, 6.34s/it, data_size=5, test_acc=0.328, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:31<00:38, 6.34s/it, data_size=6, test_acc=0.343, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [00:31<00:31, 6.35s/it, data_size=6, test_acc=0.343, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:31<00:31, 6.35s/it, data_size=6, test_acc=0.343, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:38<00:31, 6.35s/it, data_size=7, test_acc=0.675, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [00:38<00:25, 6.42s/it, data_size=7, test_acc=0.675, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:38<00:25, 6.42s/it, data_size=7, test_acc=0.675, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:44<00:25, 6.42s/it, data_size=8, test_acc=0.716, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [00:44<00:19, 6.51s/it, data_size=8, test_acc=0.716, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:44<00:19, 6.51s/it, data_size=8, test_acc=0.716, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:51<00:19, 6.51s/it, data_size=9, test_acc=0.498, train_acc=nan]Test 5: Data size 9: : 80%|████████ | 8/10 [00:51<00:13, 6.64s/it, data_size=9, test_acc=0.498, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:51<00:13, 6.64s/it, data_size=9, test_acc=0.498, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:58<00:13, 6.64s/it, data_size=10, test_acc=0.679, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:58<00:06, 6.76s/it, data_size=10, test_acc=0.679, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:58<00:06, 6.76s/it, data_size=10, test_acc=0.679, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [01:05<00:06, 6.76s/it, data_size=11, test_acc=0.718, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.86s/it, data_size=11, test_acc=0.718, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.59s/it, data_size=11, test_acc=0.718, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:06<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:06<00:55, 6.18s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:06<00:55, 6.18s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:12<00:55, 6.18s/it, data_size=3, test_acc=0.332, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:12<00:49, 6.18s/it, data_size=3, test_acc=0.332, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:12<00:49, 6.18s/it, data_size=3, test_acc=0.332, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:18<00:49, 6.18s/it, data_size=4, test_acc=0.271, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [00:18<00:43, 6.26s/it, data_size=4, test_acc=0.271, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:18<00:43, 6.26s/it, data_size=4, test_acc=0.271, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:25<00:43, 6.26s/it, data_size=5, test_acc=0.257, train_acc=nan]Test 6: Data size 5: : 40%|████ | 4/10 [00:25<00:37, 6.32s/it, data_size=5, test_acc=0.257, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:25<00:37, 6.32s/it, data_size=5, test_acc=0.257, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:31<00:37, 6.32s/it, data_size=6, test_acc=0.392, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [00:31<00:31, 6.40s/it, data_size=6, test_acc=0.392, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:31<00:31, 6.40s/it, data_size=6, test_acc=0.392, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:38<00:31, 6.40s/it, data_size=7, test_acc=0.396, train_acc=nan]Test 6: Data size 7: : 60%|██████ | 6/10 [00:38<00:25, 6.46s/it, data_size=7, test_acc=0.396, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:38<00:25, 6.46s/it, data_size=7, test_acc=0.396, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:44<00:25, 6.46s/it, data_size=8, test_acc=0.28, train_acc=nan] Test 6: Data size 8: : 70%|███████ | 7/10 [00:44<00:19, 6.51s/it, data_size=8, test_acc=0.28, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:44<00:19, 6.51s/it, data_size=8, test_acc=0.28, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:51<00:19, 6.51s/it, data_size=9, test_acc=0.524, train_acc=0.583]Test 6: Data size 9: : 80%|████████ | 8/10 [00:51<00:13, 6.62s/it, data_size=9, test_acc=0.524, train_acc=0.583]Test 6: Data size 10: : 80%|████████ | 8/10 [00:51<00:13, 6.62s/it, data_size=9, test_acc=0.524, train_acc=0.583]Test 6: Data size 10: : 80%|████████ | 8/10 [00:58<00:13, 6.62s/it, data_size=10, test_acc=0.575, train_acc=0.75]Test 6: Data size 10: : 90%|█████████ | 9/10 [00:58<00:06, 6.75s/it, data_size=10, test_acc=0.575, train_acc=0.75]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:58<00:06, 6.75s/it, data_size=10, test_acc=0.575, train_acc=0.75]Test 6: Data size 11: : 90%|█████████ | 9/10 [01:05<00:06, 6.75s/it, data_size=11, test_acc=0.653, train_acc=0.75]Test 6: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.85s/it, data_size=11, test_acc=0.653, train_acc=0.75]Test 6: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.58s/it, data_size=11, test_acc=0.653, train_acc=0.75]
0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [00:06<00:54, 6.10s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:06<00:54, 6.10s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:12<00:54, 6.10s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [00:12<00:49, 6.15s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:12<00:49, 6.15s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:18<00:49, 6.15s/it, data_size=4, test_acc=0.391, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [00:18<00:43, 6.22s/it, data_size=4, test_acc=0.391, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:18<00:43, 6.22s/it, data_size=4, test_acc=0.391, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:24<00:43, 6.22s/it, data_size=5, test_acc=0.435, train_acc=nan]Test 7: Data size 5: : 40%|████ | 4/10 [00:24<00:37, 6.29s/it, data_size=5, test_acc=0.435, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:24<00:37, 6.29s/it, data_size=5, test_acc=0.435, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:31<00:37, 6.29s/it, data_size=6, test_acc=0.49, train_acc=nan] Test 7: Data size 6: : 50%|█████ | 5/10 [00:31<00:31, 6.36s/it, data_size=6, test_acc=0.49, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:31<00:31, 6.36s/it, data_size=6, test_acc=0.49, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:37<00:31, 6.36s/it, data_size=7, test_acc=0.321, train_acc=nan]Test 7: Data size 7: : 60%|██████ | 6/10 [00:37<00:25, 6.40s/it, data_size=7, test_acc=0.321, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:37<00:25, 6.40s/it, data_size=7, test_acc=0.321, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:44<00:25, 6.40s/it, data_size=8, test_acc=0.414, train_acc=nan]Test 7: Data size 8: : 70%|███████ | 7/10 [00:44<00:19, 6.50s/it, data_size=8, test_acc=0.414, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:44<00:19, 6.50s/it, data_size=8, test_acc=0.414, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:51<00:19, 6.50s/it, data_size=9, test_acc=0.5, train_acc=nan] Test 7: Data size 9: : 80%|████████ | 8/10 [00:51<00:13, 6.62s/it, data_size=9, test_acc=0.5, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:51<00:13, 6.62s/it, data_size=9, test_acc=0.5, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:58<00:13, 6.62s/it, data_size=10, test_acc=0.49, train_acc=nan]Test 7: Data size 10: : 90%|█████████ | 9/10 [00:58<00:06, 6.69s/it, data_size=10, test_acc=0.49, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:58<00:06, 6.69s/it, data_size=10, test_acc=0.49, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [01:05<00:06, 6.69s/it, data_size=11, test_acc=0.497, train_acc=0.5]Test 7: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.79s/it, data_size=11, test_acc=0.497, train_acc=0.5]Test 7: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.54s/it, data_size=11, test_acc=0.497, train_acc=0.5]
working on model Multimodal-middle-fusion-model-based-on-AlexNet with MIN_MAX
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:06<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [00:06<00:55, 6.17s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:06<00:55, 6.17s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:12<00:55, 6.17s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [00:12<00:49, 6.22s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:12<00:49, 6.22s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:18<00:49, 6.22s/it, data_size=4, test_acc=0.258, train_acc=0.25]Test 0: Data size 4: : 30%|███ | 3/10 [00:18<00:43, 6.28s/it, data_size=4, test_acc=0.258, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [00:18<00:43, 6.28s/it, data_size=4, test_acc=0.258, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [00:25<00:43, 6.28s/it, data_size=5, test_acc=0.494, train_acc=0.5] Test 0: Data size 5: : 40%|████ | 4/10 [00:25<00:38, 6.35s/it, data_size=5, test_acc=0.494, train_acc=0.5]Test 0: Data size 6: : 40%|████ | 4/10 [00:25<00:38, 6.35s/it, data_size=5, test_acc=0.494, train_acc=0.5]Test 0: Data size 6: : 40%|████ | 4/10 [00:31<00:38, 6.35s/it, data_size=6, test_acc=0.498, train_acc=0.5]Test 0: Data size 6: : 50%|█████ | 5/10 [00:31<00:31, 6.38s/it, data_size=6, test_acc=0.498, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [00:31<00:31, 6.38s/it, data_size=6, test_acc=0.498, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [00:38<00:31, 6.38s/it, data_size=7, test_acc=0.5, train_acc=0.5] Test 0: Data size 7: : 60%|██████ | 6/10 [00:38<00:25, 6.45s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [00:38<00:25, 6.45s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [00:44<00:25, 6.45s/it, data_size=8, test_acc=0.438, train_acc=0.5]Test 0: Data size 8: : 70%|███████ | 7/10 [00:44<00:19, 6.51s/it, data_size=8, test_acc=0.438, train_acc=0.5]Test 0: Data size 9: : 70%|███████ | 7/10 [00:44<00:19, 6.51s/it, data_size=8, test_acc=0.438, train_acc=0.5]Test 0: Data size 9: : 70%|███████ | 7/10 [00:51<00:19, 6.51s/it, data_size=9, test_acc=0.495, train_acc=0.5]Test 0: Data size 9: : 80%|████████ | 8/10 [00:51<00:13, 6.63s/it, data_size=9, test_acc=0.495, train_acc=0.5]Test 0: Data size 10: : 80%|████████ | 8/10 [00:51<00:13, 6.63s/it, data_size=9, test_acc=0.495, train_acc=0.5]Test 0: Data size 10: : 80%|████████ | 8/10 [00:58<00:13, 6.63s/it, data_size=10, test_acc=0.429, train_acc=0.667]Test 0: Data size 10: : 90%|█████████ | 9/10 [00:58<00:06, 6.72s/it, data_size=10, test_acc=0.429, train_acc=0.667]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:58<00:06, 6.72s/it, data_size=10, test_acc=0.429, train_acc=0.667]Test 0: Data size 11: : 90%|█████████ | 9/10 [01:05<00:06, 6.72s/it, data_size=11, test_acc=0.46, train_acc=0.688] Test 0: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.82s/it, data_size=11, test_acc=0.46, train_acc=0.688]Test 0: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.57s/it, data_size=11, test_acc=0.46, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 1: 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:06<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 2: : 10%|█ | 1/10 [00:06<00:55, 6.18s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:06<00:55, 6.18s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:12<00:55, 6.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 20%|██ | 2/10 [00:12<00:49, 6.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:12<00:49, 6.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:18<00:49, 6.18s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 30%|███ | 3/10 [00:18<00:43, 6.23s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:18<00:43, 6.23s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:24<00:43, 6.23s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 40%|████ | 4/10 [00:24<00:37, 6.27s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:24<00:37, 6.27s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:31<00:37, 6.27s/it, data_size=6, test_acc=0.293, train_acc=nan]Test 1: Data size 6: : 50%|█████ | 5/10 [00:31<00:31, 6.34s/it, data_size=6, test_acc=0.293, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:31<00:31, 6.34s/it, data_size=6, test_acc=0.293, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:37<00:31, 6.34s/it, data_size=7, test_acc=0.427, train_acc=nan]Test 1: Data size 7: : 60%|██████ | 6/10 [00:37<00:25, 6.40s/it, data_size=7, test_acc=0.427, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:37<00:25, 6.40s/it, data_size=7, test_acc=0.427, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:44<00:25, 6.40s/it, data_size=8, test_acc=0.47, train_acc=nan] Test 1: Data size 8: : 70%|███████ | 7/10 [00:44<00:19, 6.46s/it, data_size=8, test_acc=0.47, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:44<00:19, 6.46s/it, data_size=8, test_acc=0.47, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:51<00:19, 6.46s/it, data_size=9, test_acc=0.482, train_acc=nan]Test 1: Data size 9: : 80%|████████ | 8/10 [00:51<00:13, 6.61s/it, data_size=9, test_acc=0.482, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:51<00:13, 6.61s/it, data_size=9, test_acc=0.482, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:58<00:13, 6.61s/it, data_size=10, test_acc=0.452, train_acc=nan]Test 1: Data size 10: : 90%|█████████ | 9/10 [00:58<00:06, 6.72s/it, data_size=10, test_acc=0.452, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:58<00:06, 6.72s/it, data_size=10, test_acc=0.452, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [01:05<00:06, 6.72s/it, data_size=11, test_acc=0.426, train_acc=0.688]Test 1: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.83s/it, data_size=11, test_acc=0.426, train_acc=0.688]Test 1: Data size 11: : 100%|██████████| 10/10 [01:05<00:00, 6.55s/it, data_size=11, test_acc=0.426, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:06<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:06<00:55, 6.14s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:06<00:55, 6.14s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:12<00:55, 6.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:12<00:49, 6.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:12<00:49, 6.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:18<00:49, 6.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:18<00:43, 6.21s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:18<00:43, 6.21s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:24<00:43, 6.21s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [00:24<00:37, 6.28s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:24<00:37, 6.28s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:31<00:37, 6.28s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [00:31<00:31, 6.35s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:31<00:31, 6.35s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:37<00:31, 6.35s/it, data_size=7, test_acc=0.411, train_acc=nan]Test 2: Data size 7: : 60%|██████ | 6/10 [00:37<00:25, 6.34s/it, data_size=7, test_acc=0.411, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:37<00:25, 6.34s/it, data_size=7, test_acc=0.411, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:44<00:25, 6.34s/it, data_size=8, test_acc=0.436, train_acc=nan]Test 2: Data size 8: : 70%|███████ | 7/10 [00:44<00:19, 6.36s/it, data_size=8, test_acc=0.436, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:44<00:19, 6.36s/it, data_size=8, test_acc=0.436, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:50<00:19, 6.36s/it, data_size=9, test_acc=0.415, train_acc=nan]Test 2: Data size 9: : 80%|████████ | 8/10 [00:50<00:12, 6.42s/it, data_size=9, test_acc=0.415, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:50<00:12, 6.42s/it, data_size=9, test_acc=0.415, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:57<00:12, 6.42s/it, data_size=10, test_acc=0.431, train_acc=nan]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:57<00:06, 6.49s/it, data_size=10, test_acc=0.431, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:57<00:06, 6.49s/it, data_size=10, test_acc=0.431, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [01:03<00:06, 6.49s/it, data_size=11, test_acc=0.44, train_acc=nan] Test 2: Data size 11: : 100%|██████████| 10/10 [01:04<00:00, 6.57s/it, data_size=11, test_acc=0.44, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [01:04<00:00, 6.41s/it, data_size=11, test_acc=0.44, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.99s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.99s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:11<00:53, 5.99s/it, data_size=3, test_acc=0.252, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.98s/it, data_size=3, test_acc=0.252, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.98s/it, data_size=3, test_acc=0.252, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.98s/it, data_size=4, test_acc=0.352, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:18<00:42, 6.04s/it, data_size=4, test_acc=0.352, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:18<00:42, 6.04s/it, data_size=4, test_acc=0.352, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:24<00:42, 6.04s/it, data_size=5, test_acc=0.449, train_acc=0.5]Test 3: Data size 5: : 40%|████ | 4/10 [00:24<00:36, 6.09s/it, data_size=5, test_acc=0.449, train_acc=0.5]Test 3: Data size 6: : 40%|████ | 4/10 [00:24<00:36, 6.09s/it, data_size=5, test_acc=0.449, train_acc=0.5]Test 3: Data size 6: : 40%|████ | 4/10 [00:30<00:36, 6.09s/it, data_size=6, test_acc=0.474, train_acc=0.5]Test 3: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.13s/it, data_size=6, test_acc=0.474, train_acc=0.5]Test 3: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.13s/it, data_size=6, test_acc=0.474, train_acc=0.5]Test 3: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.13s/it, data_size=7, test_acc=0.48, train_acc=0.5] Test 3: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.18s/it, data_size=7, test_acc=0.48, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.18s/it, data_size=7, test_acc=0.48, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.18s/it, data_size=8, test_acc=0.469, train_acc=0.5]Test 3: Data size 8: : 70%|███████ | 7/10 [00:43<00:18, 6.24s/it, data_size=8, test_acc=0.469, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [00:43<00:18, 6.24s/it, data_size=8, test_acc=0.469, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.24s/it, data_size=9, test_acc=0.692, train_acc=0.917]Test 3: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.38s/it, data_size=9, test_acc=0.692, train_acc=0.917]Test 3: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.38s/it, data_size=9, test_acc=0.692, train_acc=0.917]Test 3: Data size 10: : 80%|████████ | 8/10 [00:56<00:12, 6.38s/it, data_size=10, test_acc=0.684, train_acc=0.875]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.47s/it, data_size=10, test_acc=0.684, train_acc=0.875]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.47s/it, data_size=10, test_acc=0.684, train_acc=0.875]Test 3: Data size 11: : 90%|█████████ | 9/10 [01:03<00:06, 6.47s/it, data_size=11, test_acc=0.538, train_acc=0.667]Test 3: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.56s/it, data_size=11, test_acc=0.538, train_acc=0.667]Test 3: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.32s/it, data_size=11, test_acc=0.538, train_acc=0.667]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.90s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.90s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:11<00:53, 5.90s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.94s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.94s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.94s/it, data_size=4, test_acc=0.409, train_acc=0.25]Test 4: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.97s/it, data_size=4, test_acc=0.409, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.97s/it, data_size=4, test_acc=0.409, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.97s/it, data_size=5, test_acc=0.498, train_acc=0.5] Test 4: Data size 5: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.498, train_acc=0.5]Test 4: Data size 6: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.498, train_acc=0.5]Test 4: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.01s/it, data_size=6, test_acc=0.5, train_acc=0.5] Test 4: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.05s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.05s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.05s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 4: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.13s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 4: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.13s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 4: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.13s/it, data_size=8, test_acc=0.481, train_acc=0.5]Test 4: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.15s/it, data_size=8, test_acc=0.481, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.15s/it, data_size=8, test_acc=0.481, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [00:48<00:18, 6.15s/it, data_size=9, test_acc=0.5, train_acc=0.5] Test 4: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.28s/it, data_size=9, test_acc=0.5, train_acc=0.5]Test 4: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.28s/it, data_size=9, test_acc=0.5, train_acc=0.5]Test 4: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.28s/it, data_size=10, test_acc=0.548, train_acc=0.667]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.40s/it, data_size=10, test_acc=0.548, train_acc=0.667]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.40s/it, data_size=10, test_acc=0.548, train_acc=0.667]Test 4: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.40s/it, data_size=11, test_acc=0.618, train_acc=0.688]Test 4: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.49s/it, data_size=11, test_acc=0.618, train_acc=0.688]Test 4: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.25s/it, data_size=11, test_acc=0.618, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.91s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.91s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:11<00:53, 5.91s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.94s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.94s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.94s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.99s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.99s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.99s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:24<00:36, 6.03s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:24<00:36, 6.03s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:30<00:36, 6.03s/it, data_size=6, test_acc=0.38, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.11s/it, data_size=6, test_acc=0.38, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.11s/it, data_size=6, test_acc=0.38, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.11s/it, data_size=7, test_acc=0.428, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.15s/it, data_size=7, test_acc=0.428, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.15s/it, data_size=7, test_acc=0.428, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.15s/it, data_size=8, test_acc=0.312, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.24s/it, data_size=8, test_acc=0.312, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.24s/it, data_size=8, test_acc=0.312, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.24s/it, data_size=9, test_acc=0.498, train_acc=nan]Test 5: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.38s/it, data_size=9, test_acc=0.498, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.38s/it, data_size=9, test_acc=0.498, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:56<00:12, 6.38s/it, data_size=10, test_acc=0.385, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.48s/it, data_size=10, test_acc=0.385, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.48s/it, data_size=10, test_acc=0.385, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [01:03<00:06, 6.48s/it, data_size=11, test_acc=0.463, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.59s/it, data_size=11, test_acc=0.463, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.31s/it, data_size=11, test_acc=0.463, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.89s/it, data_size=3, test_acc=0.255, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.98s/it, data_size=3, test_acc=0.255, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.98s/it, data_size=3, test_acc=0.255, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.98s/it, data_size=4, test_acc=0.33, train_acc=nan] Test 6: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 6.00s/it, data_size=4, test_acc=0.33, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 6.00s/it, data_size=4, test_acc=0.33, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 6.00s/it, data_size=5, test_acc=0.339, train_acc=0.5]Test 6: Data size 5: : 40%|████ | 4/10 [00:24<00:36, 6.03s/it, data_size=5, test_acc=0.339, train_acc=0.5]Test 6: Data size 6: : 40%|████ | 4/10 [00:24<00:36, 6.03s/it, data_size=5, test_acc=0.339, train_acc=0.5]Test 6: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.03s/it, data_size=6, test_acc=0.327, train_acc=0.25]Test 6: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.05s/it, data_size=6, test_acc=0.327, train_acc=0.25]Test 6: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.05s/it, data_size=6, test_acc=0.327, train_acc=0.25]Test 6: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.05s/it, data_size=7, test_acc=0.416, train_acc=0.5] Test 6: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.14s/it, data_size=7, test_acc=0.416, train_acc=0.5]Test 6: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.14s/it, data_size=7, test_acc=0.416, train_acc=0.5]Test 6: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.14s/it, data_size=8, test_acc=0.463, train_acc=0.5]Test 6: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.20s/it, data_size=8, test_acc=0.463, train_acc=0.5]Test 6: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.20s/it, data_size=8, test_acc=0.463, train_acc=0.5]Test 6: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.20s/it, data_size=9, test_acc=0.584, train_acc=0.667]Test 6: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.31s/it, data_size=9, test_acc=0.584, train_acc=0.667]Test 6: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.31s/it, data_size=9, test_acc=0.584, train_acc=0.667]Test 6: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.31s/it, data_size=10, test_acc=0.632, train_acc=0.688]Test 6: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.41s/it, data_size=10, test_acc=0.632, train_acc=0.688]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.41s/it, data_size=10, test_acc=0.632, train_acc=0.688]Test 6: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.41s/it, data_size=11, test_acc=0.624, train_acc=0.688]Test 6: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.49s/it, data_size=11, test_acc=0.624, train_acc=0.688]Test 6: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.26s/it, data_size=11, test_acc=0.624, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.99s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.99s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:12<00:53, 5.99s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [00:12<00:48, 6.10s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:12<00:48, 6.10s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:18<00:48, 6.10s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [00:18<00:43, 6.16s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:18<00:43, 6.16s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:24<00:43, 6.16s/it, data_size=5, test_acc=0.239, train_acc=nan]Test 7: Data size 5: : 40%|████ | 4/10 [00:24<00:37, 6.20s/it, data_size=5, test_acc=0.239, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:24<00:37, 6.20s/it, data_size=5, test_acc=0.239, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:30<00:37, 6.20s/it, data_size=6, test_acc=0.25, train_acc=nan] Test 7: Data size 6: : 50%|█████ | 5/10 [00:31<00:31, 6.26s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:31<00:31, 6.26s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:37<00:31, 6.26s/it, data_size=7, test_acc=0.309, train_acc=nan]Test 7: Data size 7: : 60%|██████ | 6/10 [00:37<00:25, 6.32s/it, data_size=7, test_acc=0.309, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:37<00:25, 6.32s/it, data_size=7, test_acc=0.309, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:43<00:25, 6.32s/it, data_size=8, test_acc=0.25, train_acc=nan] Test 7: Data size 8: : 70%|███████ | 7/10 [00:43<00:19, 6.38s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:43<00:19, 6.38s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:50<00:19, 6.38s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 80%|████████ | 8/10 [00:50<00:12, 6.50s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:50<00:12, 6.50s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:57<00:12, 6.50s/it, data_size=10, test_acc=0.352, train_acc=0.438]Test 7: Data size 10: : 90%|█████████ | 9/10 [00:57<00:06, 6.60s/it, data_size=10, test_acc=0.352, train_acc=0.438]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:57<00:06, 6.60s/it, data_size=10, test_acc=0.352, train_acc=0.438]Test 7: Data size 11: : 90%|█████████ | 9/10 [01:04<00:06, 6.60s/it, data_size=11, test_acc=0.25, train_acc=0.25] Test 7: Data size 11: : 100%|██████████| 10/10 [01:04<00:00, 6.68s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 7: Data size 11: : 100%|██████████| 10/10 [01:04<00:00, 6.44s/it, data_size=11, test_acc=0.25, train_acc=0.25]
working on model Multimodal-middle-fusion-model-based-on-AlexNet with MIN_MARGIN
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [00:06<00:54, 6.03s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:06<00:54, 6.03s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:11<00:54, 6.03s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [00:12<00:48, 6.05s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:12<00:48, 6.05s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:18<00:48, 6.05s/it, data_size=4, test_acc=0.282, train_acc=0.25]Test 0: Data size 4: : 30%|███ | 3/10 [00:18<00:42, 6.05s/it, data_size=4, test_acc=0.282, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [00:18<00:42, 6.05s/it, data_size=4, test_acc=0.282, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [00:24<00:42, 6.05s/it, data_size=5, test_acc=0.497, train_acc=0.5] Test 0: Data size 5: : 40%|████ | 4/10 [00:24<00:36, 6.11s/it, data_size=5, test_acc=0.497, train_acc=0.5]Test 0: Data size 6: : 40%|████ | 4/10 [00:24<00:36, 6.11s/it, data_size=5, test_acc=0.497, train_acc=0.5]Test 0: Data size 6: : 40%|████ | 4/10 [00:30<00:36, 6.11s/it, data_size=6, test_acc=0.496, train_acc=0.5]Test 0: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.17s/it, data_size=6, test_acc=0.496, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.17s/it, data_size=6, test_acc=0.496, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.17s/it, data_size=7, test_acc=0.438, train_acc=0.5]Test 0: Data size 7: : 60%|██████ | 6/10 [00:37<00:24, 6.25s/it, data_size=7, test_acc=0.438, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [00:37<00:24, 6.25s/it, data_size=7, test_acc=0.438, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [00:43<00:24, 6.25s/it, data_size=8, test_acc=0.5, train_acc=0.5] Test 0: Data size 8: : 70%|███████ | 7/10 [00:43<00:18, 6.29s/it, data_size=8, test_acc=0.5, train_acc=0.5]Test 0: Data size 9: : 70%|███████ | 7/10 [00:43<00:18, 6.29s/it, data_size=8, test_acc=0.5, train_acc=0.5]Test 0: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.29s/it, data_size=9, test_acc=0.652, train_acc=0.583]Test 0: Data size 9: : 80%|████████ | 8/10 [00:50<00:12, 6.41s/it, data_size=9, test_acc=0.652, train_acc=0.583]Test 0: Data size 10: : 80%|████████ | 8/10 [00:50<00:12, 6.41s/it, data_size=9, test_acc=0.652, train_acc=0.583]Test 0: Data size 10: : 80%|████████ | 8/10 [00:56<00:12, 6.41s/it, data_size=10, test_acc=0.654, train_acc=0.562]Test 0: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.52s/it, data_size=10, test_acc=0.654, train_acc=0.562]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.52s/it, data_size=10, test_acc=0.654, train_acc=0.562]Test 0: Data size 11: : 90%|█████████ | 9/10 [01:03<00:06, 6.52s/it, data_size=11, test_acc=0.697, train_acc=0.688]Test 0: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.59s/it, data_size=11, test_acc=0.697, train_acc=0.688]Test 0: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.36s/it, data_size=11, test_acc=0.697, train_acc=0.688]
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0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.83s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.83s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.83s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.90s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.90s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.90s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.96s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.96s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.96s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [00:23<00:36, 6.02s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:23<00:36, 6.02s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.02s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.08s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.08s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.08s/it, data_size=7, test_acc=0.42, train_acc=nan]Test 2: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.17s/it, data_size=7, test_acc=0.42, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.17s/it, data_size=7, test_acc=0.42, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.17s/it, data_size=8, test_acc=0.427, train_acc=nan]Test 2: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.24s/it, data_size=8, test_acc=0.427, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.24s/it, data_size=8, test_acc=0.427, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.24s/it, data_size=9, test_acc=0.393, train_acc=nan]Test 2: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.37s/it, data_size=9, test_acc=0.393, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.37s/it, data_size=9, test_acc=0.393, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:56<00:12, 6.37s/it, data_size=10, test_acc=0.443, train_acc=nan]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.49s/it, data_size=10, test_acc=0.443, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.49s/it, data_size=10, test_acc=0.443, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.49s/it, data_size=11, test_acc=0.486, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.58s/it, data_size=11, test_acc=0.486, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.30s/it, data_size=11, test_acc=0.486, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.78s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.78s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.78s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:11<00:46, 5.83s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:11<00:46, 5.83s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:17<00:46, 5.83s/it, data_size=4, test_acc=0.372, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.93s/it, data_size=4, test_acc=0.372, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.93s/it, data_size=4, test_acc=0.372, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.93s/it, data_size=5, test_acc=0.5, train_acc=nan] Test 3: Data size 5: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.5, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.5, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.01s/it, data_size=6, test_acc=0.436, train_acc=nan]Test 3: Data size 6: : 50%|█████ | 5/10 [00:29<00:30, 6.05s/it, data_size=6, test_acc=0.436, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:29<00:30, 6.05s/it, data_size=6, test_acc=0.436, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.05s/it, data_size=7, test_acc=0.528, train_acc=0.5]Test 3: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.11s/it, data_size=7, test_acc=0.528, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.11s/it, data_size=7, test_acc=0.528, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.11s/it, data_size=8, test_acc=0.542, train_acc=0.5]Test 3: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.14s/it, data_size=8, test_acc=0.542, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.14s/it, data_size=8, test_acc=0.542, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [00:48<00:18, 6.14s/it, data_size=9, test_acc=0.436, train_acc=0.438]Test 3: Data size 9: : 80%|████████ | 8/10 [00:48<00:12, 6.26s/it, data_size=9, test_acc=0.436, train_acc=0.438]Test 3: Data size 10: : 80%|████████ | 8/10 [00:48<00:12, 6.26s/it, data_size=9, test_acc=0.436, train_acc=0.438]Test 3: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.26s/it, data_size=10, test_acc=0.517, train_acc=0.438]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.37s/it, data_size=10, test_acc=0.517, train_acc=0.438]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.37s/it, data_size=10, test_acc=0.517, train_acc=0.438]Test 3: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.37s/it, data_size=11, test_acc=0.61, train_acc=0.625] Test 3: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.47s/it, data_size=11, test_acc=0.61, train_acc=0.625]Test 3: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.22s/it, data_size=11, test_acc=0.61, train_acc=0.625]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.89s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.92s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.92s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.92s/it, data_size=4, test_acc=0.403, train_acc=0.25]Test 4: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.98s/it, data_size=4, test_acc=0.403, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.98s/it, data_size=4, test_acc=0.403, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.98s/it, data_size=5, test_acc=0.5, train_acc=0.5] Test 4: Data size 5: : 40%|████ | 4/10 [00:24<00:36, 6.06s/it, data_size=5, test_acc=0.5, train_acc=0.5]Test 4: Data size 6: : 40%|████ | 4/10 [00:24<00:36, 6.06s/it, data_size=5, test_acc=0.5, train_acc=0.5]Test 4: Data size 6: : 40%|████ | 4/10 [00:30<00:36, 6.06s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 4: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.13s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.13s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.13s/it, data_size=7, test_acc=0.508, train_acc=0.625]Test 4: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.16s/it, data_size=7, test_acc=0.508, train_acc=0.625]Test 4: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.16s/it, data_size=7, test_acc=0.508, train_acc=0.625]Test 4: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.16s/it, data_size=8, test_acc=0.64, train_acc=0.5] Test 4: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.23s/it, data_size=8, test_acc=0.64, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.23s/it, data_size=8, test_acc=0.64, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.23s/it, data_size=9, test_acc=0.574, train_acc=0.667]Test 4: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.31s/it, data_size=9, test_acc=0.574, train_acc=0.667]Test 4: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.31s/it, data_size=9, test_acc=0.574, train_acc=0.667]Test 4: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.31s/it, data_size=10, test_acc=0.495, train_acc=0.438]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.43s/it, data_size=10, test_acc=0.495, train_acc=0.438]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.43s/it, data_size=10, test_acc=0.495, train_acc=0.438]Test 4: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.43s/it, data_size=11, test_acc=0.599, train_acc=0.854]Test 4: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.55s/it, data_size=11, test_acc=0.599, train_acc=0.854]Test 4: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.29s/it, data_size=11, test_acc=0.599, train_acc=0.854]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.85s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.85s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.85s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:11<00:46, 5.87s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:11<00:46, 5.87s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:17<00:46, 5.87s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.92s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.92s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.92s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:23<00:35, 5.97s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:23<00:35, 5.97s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:29<00:35, 5.97s/it, data_size=6, test_acc=0.312, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [00:29<00:30, 6.03s/it, data_size=6, test_acc=0.312, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:29<00:30, 6.03s/it, data_size=6, test_acc=0.312, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:35<00:30, 6.03s/it, data_size=7, test_acc=0.471, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.09s/it, data_size=7, test_acc=0.471, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.09s/it, data_size=7, test_acc=0.471, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.09s/it, data_size=8, test_acc=0.389, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.12s/it, data_size=8, test_acc=0.389, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.12s/it, data_size=8, test_acc=0.389, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:48<00:18, 6.12s/it, data_size=9, test_acc=0.404, train_acc=nan]Test 5: Data size 9: : 80%|████████ | 8/10 [00:48<00:12, 6.21s/it, data_size=9, test_acc=0.404, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:48<00:12, 6.21s/it, data_size=9, test_acc=0.404, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.21s/it, data_size=10, test_acc=0.423, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.29s/it, data_size=10, test_acc=0.423, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.29s/it, data_size=10, test_acc=0.423, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [01:01<00:06, 6.29s/it, data_size=11, test_acc=0.599, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:01<00:00, 6.41s/it, data_size=11, test_acc=0.599, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:01<00:00, 6.18s/it, data_size=11, test_acc=0.599, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:11<00:53, 5.89s/it, data_size=3, test_acc=0.253, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.94s/it, data_size=3, test_acc=0.253, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.94s/it, data_size=3, test_acc=0.253, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.94s/it, data_size=4, test_acc=0.388, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.98s/it, data_size=4, test_acc=0.388, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.98s/it, data_size=4, test_acc=0.388, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.98s/it, data_size=5, test_acc=0.25, train_acc=nan] Test 6: Data size 5: : 40%|████ | 4/10 [00:23<00:36, 6.02s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:23<00:36, 6.02s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:30<00:36, 6.02s/it, data_size=6, test_acc=0.362, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.12s/it, data_size=6, test_acc=0.362, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.12s/it, data_size=6, test_acc=0.362, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.12s/it, data_size=7, test_acc=0.4, train_acc=nan] Test 6: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.18s/it, data_size=7, test_acc=0.4, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.18s/it, data_size=7, test_acc=0.4, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.18s/it, data_size=8, test_acc=0.422, train_acc=nan]Test 6: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.24s/it, data_size=8, test_acc=0.422, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.24s/it, data_size=8, test_acc=0.422, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.24s/it, data_size=9, test_acc=0.467, train_acc=nan]Test 6: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.34s/it, data_size=9, test_acc=0.467, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.34s/it, data_size=9, test_acc=0.467, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:56<00:12, 6.34s/it, data_size=10, test_acc=0.464, train_acc=nan]Test 6: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.45s/it, data_size=10, test_acc=0.464, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.45s/it, data_size=10, test_acc=0.464, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.45s/it, data_size=11, test_acc=0.456, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.52s/it, data_size=11, test_acc=0.456, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.29s/it, data_size=11, test_acc=0.456, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.90s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.90s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:11<00:53, 5.90s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.90s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.90s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.90s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.98s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.98s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.98s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 7: Data size 5: : 40%|████ | 4/10 [00:23<00:36, 6.03s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 7: Data size 6: : 40%|████ | 4/10 [00:23<00:36, 6.03s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 7: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.03s/it, data_size=6, test_acc=0.448, train_acc=0.5]Test 7: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.06s/it, data_size=6, test_acc=0.448, train_acc=0.5]Test 7: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.06s/it, data_size=6, test_acc=0.448, train_acc=0.5]Test 7: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.06s/it, data_size=7, test_acc=0.435, train_acc=0.5]Test 7: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.09s/it, data_size=7, test_acc=0.435, train_acc=0.5]Test 7: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.09s/it, data_size=7, test_acc=0.435, train_acc=0.5]Test 7: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.09s/it, data_size=8, test_acc=0.491, train_acc=0.5]Test 7: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.15s/it, data_size=8, test_acc=0.491, train_acc=0.5]Test 7: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.15s/it, data_size=8, test_acc=0.491, train_acc=0.5]Test 7: Data size 9: : 70%|███████ | 7/10 [00:48<00:18, 6.15s/it, data_size=9, test_acc=0.5, train_acc=0.5] Test 7: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.27s/it, data_size=9, test_acc=0.5, train_acc=0.5]Test 7: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.27s/it, data_size=9, test_acc=0.5, train_acc=0.5]Test 7: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.27s/it, data_size=10, test_acc=0.441, train_acc=0.5]Test 7: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.39s/it, data_size=10, test_acc=0.441, train_acc=0.5]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.39s/it, data_size=10, test_acc=0.441, train_acc=0.5]Test 7: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.39s/it, data_size=11, test_acc=0.597, train_acc=0.604]Test 7: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.48s/it, data_size=11, test_acc=0.597, train_acc=0.604]Test 7: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.24s/it, data_size=11, test_acc=0.597, train_acc=0.604]
working on model Multimodal-middle-fusion-model-based-on-AlexNet with MAX_ENTROPY
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.97s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.97s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:11<00:53, 5.97s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.99s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.99s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:18<00:47, 5.99s/it, data_size=4, test_acc=0.284, train_acc=0.25]Test 0: Data size 4: : 30%|███ | 3/10 [00:18<00:42, 6.09s/it, data_size=4, test_acc=0.284, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [00:18<00:42, 6.09s/it, data_size=4, test_acc=0.284, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [00:24<00:42, 6.09s/it, data_size=5, test_acc=0.407, train_acc=0.25]Test 0: Data size 5: : 40%|████ | 4/10 [00:24<00:37, 6.17s/it, data_size=5, test_acc=0.407, train_acc=0.25]Test 0: Data size 6: : 40%|████ | 4/10 [00:24<00:37, 6.17s/it, data_size=5, test_acc=0.407, train_acc=0.25]Test 0: Data size 6: : 40%|████ | 4/10 [00:30<00:37, 6.17s/it, data_size=6, test_acc=0.413, train_acc=0.5] Test 0: Data size 6: : 50%|█████ | 5/10 [00:30<00:31, 6.23s/it, data_size=6, test_acc=0.413, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [00:30<00:31, 6.23s/it, data_size=6, test_acc=0.413, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [00:37<00:31, 6.23s/it, data_size=7, test_acc=0.454, train_acc=0.5]Test 0: Data size 7: : 60%|██████ | 6/10 [00:37<00:25, 6.30s/it, data_size=7, test_acc=0.454, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [00:37<00:25, 6.30s/it, data_size=7, test_acc=0.454, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [00:43<00:25, 6.30s/it, data_size=8, test_acc=0.439, train_acc=0.5]Test 0: Data size 8: : 70%|███████ | 7/10 [00:43<00:19, 6.36s/it, data_size=8, test_acc=0.439, train_acc=0.5]Test 0: Data size 9: : 70%|███████ | 7/10 [00:43<00:19, 6.36s/it, data_size=8, test_acc=0.439, train_acc=0.5]Test 0: Data size 9: : 70%|███████ | 7/10 [00:50<00:19, 6.36s/it, data_size=9, test_acc=0.464, train_acc=0.5]Test 0: Data size 9: : 80%|████████ | 8/10 [00:50<00:12, 6.49s/it, data_size=9, test_acc=0.464, train_acc=0.5]Test 0: Data size 10: : 80%|████████ | 8/10 [00:50<00:12, 6.49s/it, data_size=9, test_acc=0.464, train_acc=0.5]Test 0: Data size 10: : 80%|████████ | 8/10 [00:57<00:12, 6.49s/it, data_size=10, test_acc=0.594, train_acc=0.583]Test 0: Data size 10: : 90%|█████████ | 9/10 [00:57<00:06, 6.57s/it, data_size=10, test_acc=0.594, train_acc=0.583]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:57<00:06, 6.57s/it, data_size=10, test_acc=0.594, train_acc=0.583]Test 0: Data size 11: : 90%|█████████ | 9/10 [01:03<00:06, 6.57s/it, data_size=11, test_acc=0.52, train_acc=0.688] Test 0: Data size 11: : 100%|██████████| 10/10 [01:04<00:00, 6.66s/it, data_size=11, test_acc=0.52, train_acc=0.688]Test 0: Data size 11: : 100%|██████████| 10/10 [01:04<00:00, 6.41s/it, data_size=11, test_acc=0.52, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 1: 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 2: : 10%|█ | 1/10 [00:05<00:53, 5.96s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:05<00:53, 5.96s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:11<00:53, 5.96s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 20%|██ | 2/10 [00:11<00:48, 6.01s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:11<00:48, 6.01s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:17<00:48, 6.01s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 30%|███ | 3/10 [00:18<00:42, 6.05s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:18<00:42, 6.05s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:24<00:42, 6.05s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 40%|████ | 4/10 [00:24<00:36, 6.10s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:24<00:36, 6.10s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:30<00:36, 6.10s/it, data_size=6, test_acc=0.293, train_acc=nan]Test 1: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.17s/it, data_size=6, test_acc=0.293, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.17s/it, data_size=6, test_acc=0.293, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.17s/it, data_size=7, test_acc=0.427, train_acc=nan]Test 1: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.21s/it, data_size=7, test_acc=0.427, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.21s/it, data_size=7, test_acc=0.427, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:43<00:24, 6.21s/it, data_size=8, test_acc=0.482, train_acc=nan]Test 1: Data size 8: : 70%|███████ | 7/10 [00:43<00:18, 6.27s/it, data_size=8, test_acc=0.482, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:43<00:18, 6.27s/it, data_size=8, test_acc=0.482, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.27s/it, data_size=9, test_acc=0.482, train_acc=0.5]Test 1: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.39s/it, data_size=9, test_acc=0.482, train_acc=0.5]Test 1: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.39s/it, data_size=9, test_acc=0.482, train_acc=0.5]Test 1: Data size 10: : 80%|████████ | 8/10 [00:56<00:12, 6.39s/it, data_size=10, test_acc=0.482, train_acc=0.5]Test 1: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.48s/it, data_size=10, test_acc=0.482, train_acc=0.5]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.48s/it, data_size=10, test_acc=0.482, train_acc=0.5]Test 1: Data size 11: : 90%|█████████ | 9/10 [01:03<00:06, 6.48s/it, data_size=11, test_acc=0.482, train_acc=0.5]Test 1: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.59s/it, data_size=11, test_acc=0.482, train_acc=0.5]Test 1: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.34s/it, data_size=11, test_acc=0.482, train_acc=0.5]
0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:05<00:51, 5.77s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:05<00:51, 5.77s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:11<00:51, 5.77s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:11<00:46, 5.84s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:11<00:46, 5.84s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:17<00:46, 5.84s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.94s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.94s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.94s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.01s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [00:29<00:30, 6.03s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:29<00:30, 6.03s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.03s/it, data_size=7, test_acc=0.42, train_acc=nan]Test 2: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.10s/it, data_size=7, test_acc=0.42, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.10s/it, data_size=7, test_acc=0.42, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.10s/it, data_size=8, test_acc=0.44, train_acc=nan]Test 2: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.14s/it, data_size=8, test_acc=0.44, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.14s/it, data_size=8, test_acc=0.44, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:48<00:18, 6.14s/it, data_size=9, test_acc=0.42, train_acc=nan]Test 2: Data size 9: : 80%|████████ | 8/10 [00:48<00:12, 6.24s/it, data_size=9, test_acc=0.42, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:48<00:12, 6.24s/it, data_size=9, test_acc=0.42, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.24s/it, data_size=10, test_acc=0.456, train_acc=nan]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.32s/it, data_size=10, test_acc=0.456, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.32s/it, data_size=10, test_acc=0.456, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [01:01<00:06, 6.32s/it, data_size=11, test_acc=0.431, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [01:01<00:00, 6.42s/it, data_size=11, test_acc=0.431, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [01:01<00:00, 6.20s/it, data_size=11, test_acc=0.431, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.89s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.89s/it, data_size=3, test_acc=0.252, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.93s/it, data_size=3, test_acc=0.252, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.93s/it, data_size=3, test_acc=0.252, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.93s/it, data_size=4, test_acc=0.337, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:17<00:42, 6.02s/it, data_size=4, test_acc=0.337, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:17<00:42, 6.02s/it, data_size=4, test_acc=0.337, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:24<00:42, 6.02s/it, data_size=5, test_acc=0.495, train_acc=nan]Test 3: Data size 5: : 40%|████ | 4/10 [00:24<00:36, 6.08s/it, data_size=5, test_acc=0.495, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:24<00:36, 6.08s/it, data_size=5, test_acc=0.495, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:30<00:36, 6.08s/it, data_size=6, test_acc=0.5, train_acc=0.5] Test 3: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.13s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 3: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.13s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 3: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.13s/it, data_size=7, test_acc=0.49, train_acc=0.5]Test 3: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.19s/it, data_size=7, test_acc=0.49, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.19s/it, data_size=7, test_acc=0.49, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.19s/it, data_size=8, test_acc=0.484, train_acc=0.5]Test 3: Data size 8: : 70%|███████ | 7/10 [00:43<00:18, 6.25s/it, data_size=8, test_acc=0.484, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [00:43<00:18, 6.25s/it, data_size=8, test_acc=0.484, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.25s/it, data_size=9, test_acc=0.435, train_acc=0.667]Test 3: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.35s/it, data_size=9, test_acc=0.435, train_acc=0.667]Test 3: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.35s/it, data_size=9, test_acc=0.435, train_acc=0.667]Test 3: Data size 10: : 80%|████████ | 8/10 [00:56<00:12, 6.35s/it, data_size=10, test_acc=0.465, train_acc=0.5] Test 3: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.44s/it, data_size=10, test_acc=0.465, train_acc=0.5]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.44s/it, data_size=10, test_acc=0.465, train_acc=0.5]Test 3: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.44s/it, data_size=11, test_acc=0.627, train_acc=0.583]Test 3: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.54s/it, data_size=11, test_acc=0.627, train_acc=0.583]Test 3: Data size 11: : 100%|██████████| 10/10 [01:03<00:00, 6.30s/it, data_size=11, test_acc=0.627, train_acc=0.583]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.81s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.81s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.81s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:11<00:46, 5.85s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:11<00:46, 5.85s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:17<00:46, 5.85s/it, data_size=4, test_acc=0.398, train_acc=0.25]Test 4: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.92s/it, data_size=4, test_acc=0.398, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.92s/it, data_size=4, test_acc=0.398, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.92s/it, data_size=5, test_acc=0.46, train_acc=0.25] Test 4: Data size 5: : 40%|████ | 4/10 [00:23<00:35, 5.95s/it, data_size=5, test_acc=0.46, train_acc=0.25]Test 4: Data size 6: : 40%|████ | 4/10 [00:23<00:35, 5.95s/it, data_size=5, test_acc=0.46, train_acc=0.25]Test 4: Data size 6: : 40%|████ | 4/10 [00:29<00:35, 5.95s/it, data_size=6, test_acc=0.431, train_acc=0.5]Test 4: Data size 6: : 50%|█████ | 5/10 [00:29<00:30, 6.00s/it, data_size=6, test_acc=0.431, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [00:29<00:30, 6.00s/it, data_size=6, test_acc=0.431, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [00:35<00:30, 6.00s/it, data_size=7, test_acc=0.462, train_acc=0.5]Test 4: Data size 7: : 60%|██████ | 6/10 [00:35<00:24, 6.06s/it, data_size=7, test_acc=0.462, train_acc=0.5]Test 4: Data size 8: : 60%|██████ | 6/10 [00:35<00:24, 6.06s/it, data_size=7, test_acc=0.462, train_acc=0.5]Test 4: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.06s/it, data_size=8, test_acc=0.441, train_acc=0.5]Test 4: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.13s/it, data_size=8, test_acc=0.441, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.13s/it, data_size=8, test_acc=0.441, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [00:48<00:18, 6.13s/it, data_size=9, test_acc=0.5, train_acc=0.5] Test 4: Data size 9: : 80%|████████ | 8/10 [00:48<00:12, 6.24s/it, data_size=9, test_acc=0.5, train_acc=0.5]Test 4: Data size 10: : 80%|████████ | 8/10 [00:48<00:12, 6.24s/it, data_size=9, test_acc=0.5, train_acc=0.5]Test 4: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.24s/it, data_size=10, test_acc=0.445, train_acc=0.5]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.36s/it, data_size=10, test_acc=0.445, train_acc=0.5]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.36s/it, data_size=10, test_acc=0.445, train_acc=0.5]Test 4: Data size 11: : 90%|█████████ | 9/10 [01:01<00:06, 6.36s/it, data_size=11, test_acc=0.509, train_acc=0.688]Test 4: Data size 11: : 100%|██████████| 10/10 [01:01<00:00, 6.44s/it, data_size=11, test_acc=0.509, train_acc=0.688]Test 4: Data size 11: : 100%|██████████| 10/10 [01:01<00:00, 6.20s/it, data_size=11, test_acc=0.509, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.84s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.84s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.84s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.89s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.89s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.89s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.96s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.96s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.96s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:23<00:36, 6.01s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.01s/it, data_size=6, test_acc=0.356, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.07s/it, data_size=6, test_acc=0.356, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.07s/it, data_size=6, test_acc=0.356, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.07s/it, data_size=7, test_acc=0.498, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.15s/it, data_size=7, test_acc=0.498, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.15s/it, data_size=7, test_acc=0.498, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.15s/it, data_size=8, test_acc=0.451, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.20s/it, data_size=8, test_acc=0.451, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.20s/it, data_size=8, test_acc=0.451, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:49<00:18, 6.20s/it, data_size=9, test_acc=0.25, train_acc=nan] Test 5: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.34s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.34s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.34s/it, data_size=10, test_acc=0.498, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:56<00:06, 6.46s/it, data_size=10, test_acc=0.498, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:56<00:06, 6.46s/it, data_size=10, test_acc=0.498, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.46s/it, data_size=11, test_acc=0.433, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.53s/it, data_size=11, test_acc=0.433, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.27s/it, data_size=11, test_acc=0.433, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:05<00:52, 5.79s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:05<00:52, 5.79s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:11<00:52, 5.79s/it, data_size=3, test_acc=0.26, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:11<00:47, 5.88s/it, data_size=3, test_acc=0.26, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:11<00:47, 5.88s/it, data_size=3, test_acc=0.26, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:17<00:47, 5.88s/it, data_size=4, test_acc=0.407, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [00:17<00:41, 5.95s/it, data_size=4, test_acc=0.407, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:17<00:41, 5.95s/it, data_size=4, test_acc=0.407, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:23<00:41, 5.95s/it, data_size=5, test_acc=0.267, train_acc=nan]Test 6: Data size 5: : 40%|████ | 4/10 [00:23<00:35, 5.99s/it, data_size=5, test_acc=0.267, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:23<00:35, 5.99s/it, data_size=5, test_acc=0.267, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:29<00:35, 5.99s/it, data_size=6, test_acc=0.429, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [00:30<00:30, 6.07s/it, data_size=6, test_acc=0.429, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:30<00:30, 6.07s/it, data_size=6, test_acc=0.429, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:36<00:30, 6.07s/it, data_size=7, test_acc=0.479, train_acc=0.625]Test 6: Data size 7: : 60%|██████ | 6/10 [00:36<00:24, 6.12s/it, data_size=7, test_acc=0.479, train_acc=0.625]Test 6: Data size 8: : 60%|██████ | 6/10 [00:36<00:24, 6.12s/it, data_size=7, test_acc=0.479, train_acc=0.625]Test 6: Data size 8: : 60%|██████ | 6/10 [00:42<00:24, 6.12s/it, data_size=8, test_acc=0.544, train_acc=0.667]Test 6: Data size 8: : 70%|███████ | 7/10 [00:42<00:18, 6.16s/it, data_size=8, test_acc=0.544, train_acc=0.667]Test 6: Data size 9: : 70%|███████ | 7/10 [00:42<00:18, 6.16s/it, data_size=8, test_acc=0.544, train_acc=0.667]Test 6: Data size 9: : 70%|███████ | 7/10 [00:48<00:18, 6.16s/it, data_size=9, test_acc=0.537, train_acc=0.688]Test 6: Data size 9: : 80%|████████ | 8/10 [00:49<00:12, 6.29s/it, data_size=9, test_acc=0.537, train_acc=0.688]Test 6: Data size 10: : 80%|████████ | 8/10 [00:49<00:12, 6.29s/it, data_size=9, test_acc=0.537, train_acc=0.688]Test 6: Data size 10: : 80%|████████ | 8/10 [00:55<00:12, 6.29s/it, data_size=10, test_acc=0.56, train_acc=0.7] Test 6: Data size 10: : 90%|█████████ | 9/10 [00:55<00:06, 6.38s/it, data_size=10, test_acc=0.56, train_acc=0.7]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:55<00:06, 6.38s/it, data_size=10, test_acc=0.56, train_acc=0.7]Test 6: Data size 11: : 90%|█████████ | 9/10 [01:02<00:06, 6.38s/it, data_size=11, test_acc=0.605, train_acc=0.708]Test 6: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.46s/it, data_size=11, test_acc=0.605, train_acc=0.708]Test 6: Data size 11: : 100%|██████████| 10/10 [01:02<00:00, 6.23s/it, data_size=11, test_acc=0.605, train_acc=0.708]
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working on model Multimodal-middle-fusion-model-based-on-AlexNet with CLUSTER_MARGIN
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label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 0: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [01:05<09:48, 65.37s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [01:05<09:48, 65.37s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [01:10<09:48, 65.37s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [01:10<04:00, 30.03s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [01:10<04:00, 30.03s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [01:15<04:00, 30.03s/it, data_size=4, test_acc=0.336, train_acc=0.25]Test 0: Data size 4: : 30%|███ | 3/10 [01:16<02:11, 18.80s/it, data_size=4, test_acc=0.336, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [01:16<02:11, 18.80s/it, data_size=4, test_acc=0.336, train_acc=0.25]Test 0: Data size 5: : 30%|███ | 3/10 [01:21<02:11, 18.80s/it, data_size=5, test_acc=0.495, train_acc=0.5] Test 0: Data size 5: : 40%|████ | 4/10 [01:21<01:21, 13.56s/it, data_size=5, test_acc=0.495, train_acc=0.5]Test 0: Data size 6: : 40%|████ | 4/10 [01:21<01:21, 13.56s/it, data_size=5, test_acc=0.495, train_acc=0.5]Test 0: Data size 6: : 40%|████ | 4/10 [01:27<01:21, 13.56s/it, data_size=6, test_acc=0.5, train_acc=0.5] Test 0: Data size 6: : 50%|█████ | 5/10 [01:27<00:53, 10.69s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [01:27<00:53, 10.69s/it, data_size=6, test_acc=0.5, train_acc=0.5]Test 0: Data size 7: : 50%|█████ | 5/10 [01:32<00:53, 10.69s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 0: Data size 7: : 60%|██████ | 6/10 [01:32<00:35, 8.93s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [01:32<00:35, 8.93s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 0: Data size 8: : 60%|██████ | 6/10 [01:38<00:35, 8.93s/it, data_size=8, test_acc=0.528, train_acc=0.667]Test 0: Data size 8: : 70%|███████ | 7/10 [01:38<00:23, 7.88s/it, data_size=8, test_acc=0.528, train_acc=0.667]Test 0: Data size 9: : 70%|███████ | 7/10 [01:38<00:23, 7.88s/it, data_size=8, test_acc=0.528, train_acc=0.667]Test 0: Data size 9: : 70%|███████ | 7/10 [01:44<00:23, 7.88s/it, data_size=9, test_acc=0.687, train_acc=0.667]Test 0: Data size 9: : 80%|████████ | 8/10 [01:44<00:14, 7.27s/it, data_size=9, test_acc=0.687, train_acc=0.667]Test 0: Data size 10: : 80%|████████ | 8/10 [01:44<00:14, 7.27s/it, data_size=9, test_acc=0.687, train_acc=0.667]Test 0: Data size 10: : 80%|████████ | 8/10 [01:50<00:14, 7.27s/it, data_size=10, test_acc=0.565, train_acc=0.667]Test 0: Data size 10: : 90%|█████████ | 9/10 [01:50<00:06, 6.89s/it, data_size=10, test_acc=0.565, train_acc=0.667]Test 0: Data size 11: : 90%|█████████ | 9/10 [01:50<00:06, 6.89s/it, data_size=10, test_acc=0.565, train_acc=0.667]Test 0: Data size 11: : 90%|█████████ | 9/10 [01:56<00:06, 6.89s/it, data_size=11, test_acc=0.699, train_acc=0.688]Test 0: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 6.65s/it, data_size=11, test_acc=0.699, train_acc=0.688]Test 0: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 11.66s/it, data_size=11, test_acc=0.699, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 1: 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 1: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 2: : 10%|█ | 1/10 [01:05<09:47, 65.31s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [01:05<09:47, 65.31s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [01:10<09:47, 65.31s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 20%|██ | 2/10 [01:10<03:59, 29.98s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [01:10<03:59, 29.98s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [01:15<03:59, 29.98s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 30%|███ | 3/10 [01:15<02:10, 18.71s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [01:15<02:10, 18.71s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [01:21<02:10, 18.71s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 40%|████ | 4/10 [01:21<01:20, 13.48s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [01:21<01:20, 13.48s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [01:26<01:20, 13.48s/it, data_size=6, test_acc=0.305, train_acc=nan]Test 1: Data size 6: : 50%|█████ | 5/10 [01:26<00:53, 10.65s/it, data_size=6, test_acc=0.305, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [01:26<00:53, 10.65s/it, data_size=6, test_acc=0.305, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [01:32<00:53, 10.65s/it, data_size=7, test_acc=0.421, train_acc=nan]Test 1: Data size 7: : 60%|██████ | 6/10 [01:32<00:35, 8.93s/it, data_size=7, test_acc=0.421, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [01:32<00:35, 8.93s/it, data_size=7, test_acc=0.421, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [01:38<00:35, 8.93s/it, data_size=8, test_acc=0.439, train_acc=0.5]Test 1: Data size 8: : 70%|███████ | 7/10 [01:38<00:23, 7.86s/it, data_size=8, test_acc=0.439, train_acc=0.5]Test 1: Data size 9: : 70%|███████ | 7/10 [01:38<00:23, 7.86s/it, data_size=8, test_acc=0.439, train_acc=0.5]Test 1: Data size 9: : 70%|███████ | 7/10 [01:44<00:23, 7.86s/it, data_size=9, test_acc=0.451, train_acc=0.5]Test 1: Data size 9: : 80%|████████ | 8/10 [01:44<00:14, 7.25s/it, data_size=9, test_acc=0.451, train_acc=0.5]Test 1: Data size 10: : 80%|████████ | 8/10 [01:44<00:14, 7.25s/it, data_size=9, test_acc=0.451, train_acc=0.5]Test 1: Data size 10: : 80%|████████ | 8/10 [01:50<00:14, 7.25s/it, data_size=10, test_acc=0.433, train_acc=0.667]Test 1: Data size 10: : 90%|█████████ | 9/10 [01:50<00:06, 6.86s/it, data_size=10, test_acc=0.433, train_acc=0.667]Test 1: Data size 11: : 90%|█████████ | 9/10 [01:50<00:06, 6.86s/it, data_size=10, test_acc=0.433, train_acc=0.667]Test 1: Data size 11: : 90%|█████████ | 9/10 [01:56<00:06, 6.86s/it, data_size=11, test_acc=0.564, train_acc=0.75] Test 1: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 6.62s/it, data_size=11, test_acc=0.564, train_acc=0.75]Test 1: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 11.62s/it, data_size=11, test_acc=0.564, train_acc=0.75]
0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 2: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [01:05<09:52, 65.87s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [01:05<09:52, 65.87s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [01:11<09:52, 65.87s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [01:11<04:02, 30.35s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [01:11<04:02, 30.35s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [01:16<04:02, 30.35s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [01:16<02:13, 19.02s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [01:16<02:13, 19.02s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [01:22<02:13, 19.02s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [01:22<01:22, 13.69s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [01:22<01:22, 13.69s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [01:27<01:22, 13.69s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [01:28<00:53, 10.78s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [01:28<00:53, 10.78s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [01:33<00:53, 10.78s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 60%|██████ | 6/10 [01:33<00:36, 9.03s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [01:33<00:36, 9.03s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [01:39<00:36, 9.03s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 70%|███████ | 7/10 [01:39<00:23, 7.96s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [01:39<00:23, 7.96s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [01:45<00:23, 7.96s/it, data_size=9, test_acc=0.427, train_acc=nan]Test 2: Data size 9: : 80%|████████ | 8/10 [01:45<00:14, 7.38s/it, data_size=9, test_acc=0.427, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [01:45<00:14, 7.38s/it, data_size=9, test_acc=0.427, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [01:51<00:14, 7.38s/it, data_size=10, test_acc=0.459, train_acc=nan]Test 2: Data size 10: : 90%|█████████ | 9/10 [01:51<00:06, 7.00s/it, data_size=10, test_acc=0.459, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [01:51<00:06, 7.00s/it, data_size=10, test_acc=0.459, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [01:57<00:06, 7.00s/it, data_size=11, test_acc=0.493, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [01:58<00:00, 6.78s/it, data_size=11, test_acc=0.493, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [01:58<00:00, 11.80s/it, data_size=11, test_acc=0.493, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 3: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.252, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [01:05<09:49, 65.45s/it, data_size=2, test_acc=0.252, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [01:05<09:49, 65.45s/it, data_size=2, test_acc=0.252, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [01:10<09:49, 65.45s/it, data_size=3, test_acc=0.299, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [01:10<04:00, 30.06s/it, data_size=3, test_acc=0.299, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [01:10<04:00, 30.06s/it, data_size=3, test_acc=0.299, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [01:15<04:00, 30.06s/it, data_size=4, test_acc=0.25, train_acc=nan] Test 3: Data size 4: : 30%|███ | 3/10 [01:16<02:11, 18.78s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [01:16<02:11, 18.78s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [01:21<02:11, 18.78s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 3: Data size 5: : 40%|████ | 4/10 [01:21<01:20, 13.49s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 3: Data size 6: : 40%|████ | 4/10 [01:21<01:20, 13.49s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 3: Data size 6: : 40%|████ | 4/10 [01:26<01:20, 13.49s/it, data_size=6, test_acc=0.252, train_acc=0.25]Test 3: Data size 6: : 50%|█████ | 5/10 [01:27<00:53, 10.63s/it, data_size=6, test_acc=0.252, train_acc=0.25]Test 3: Data size 7: : 50%|█████ | 5/10 [01:27<00:53, 10.63s/it, data_size=6, test_acc=0.252, train_acc=0.25]Test 3: Data size 7: : 50%|█████ | 5/10 [01:32<00:53, 10.63s/it, data_size=7, test_acc=0.467, train_acc=0.5] Test 3: Data size 7: : 60%|██████ | 6/10 [01:32<00:35, 8.92s/it, data_size=7, test_acc=0.467, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [01:32<00:35, 8.92s/it, data_size=7, test_acc=0.467, train_acc=0.5]Test 3: Data size 8: : 60%|██████ | 6/10 [01:38<00:35, 8.92s/it, data_size=8, test_acc=0.434, train_acc=0.5]Test 3: Data size 8: : 70%|███████ | 7/10 [01:38<00:23, 7.86s/it, data_size=8, test_acc=0.434, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [01:38<00:23, 7.86s/it, data_size=8, test_acc=0.434, train_acc=0.5]Test 3: Data size 9: : 70%|███████ | 7/10 [01:44<00:23, 7.86s/it, data_size=9, test_acc=0.463, train_acc=0.5]Test 3: Data size 9: : 80%|████████ | 8/10 [01:44<00:14, 7.26s/it, data_size=9, test_acc=0.463, train_acc=0.5]Test 3: Data size 10: : 80%|████████ | 8/10 [01:44<00:14, 7.26s/it, data_size=9, test_acc=0.463, train_acc=0.5]Test 3: Data size 10: : 80%|████████ | 8/10 [01:50<00:14, 7.26s/it, data_size=10, test_acc=0.566, train_acc=0.75]Test 3: Data size 10: : 90%|█████████ | 9/10 [01:50<00:06, 6.89s/it, data_size=10, test_acc=0.566, train_acc=0.75]Test 3: Data size 11: : 90%|█████████ | 9/10 [01:50<00:06, 6.89s/it, data_size=10, test_acc=0.566, train_acc=0.75]Test 3: Data size 11: : 90%|█████████ | 9/10 [01:56<00:06, 6.89s/it, data_size=11, test_acc=0.738, train_acc=0.75]Test 3: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 6.67s/it, data_size=11, test_acc=0.738, train_acc=0.75]Test 3: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 11.65s/it, data_size=11, test_acc=0.738, train_acc=0.75]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 4: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [01:05<09:52, 65.85s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [01:05<09:52, 65.85s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [01:11<09:52, 65.85s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [01:11<04:02, 30.32s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [01:11<04:02, 30.32s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [01:16<04:02, 30.32s/it, data_size=4, test_acc=0.451, train_acc=0.25]Test 4: Data size 4: : 30%|███ | 3/10 [01:16<02:12, 18.96s/it, data_size=4, test_acc=0.451, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [01:16<02:12, 18.96s/it, data_size=4, test_acc=0.451, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [01:22<02:12, 18.96s/it, data_size=5, test_acc=0.5, train_acc=0.5] Test 4: Data size 5: : 40%|████ | 4/10 [01:22<01:22, 13.68s/it, data_size=5, test_acc=0.5, train_acc=0.5]Test 4: Data size 6: : 40%|████ | 4/10 [01:22<01:22, 13.68s/it, data_size=5, test_acc=0.5, train_acc=0.5]Test 4: Data size 6: : 40%|████ | 4/10 [01:27<01:22, 13.68s/it, data_size=6, test_acc=0.349, train_acc=0.5]Test 4: Data size 6: : 50%|█████ | 5/10 [01:27<00:53, 10.78s/it, data_size=6, test_acc=0.349, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [01:27<00:53, 10.78s/it, data_size=6, test_acc=0.349, train_acc=0.5]Test 4: Data size 7: : 50%|█████ | 5/10 [01:33<00:53, 10.78s/it, data_size=7, test_acc=0.5, train_acc=0.5] Test 4: Data size 7: : 60%|██████ | 6/10 [01:33<00:36, 9.10s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 4: Data size 8: : 60%|██████ | 6/10 [01:33<00:36, 9.10s/it, data_size=7, test_acc=0.5, train_acc=0.5]Test 4: Data size 8: : 60%|██████ | 6/10 [01:39<00:36, 9.10s/it, data_size=8, test_acc=0.5, train_acc=0.5]Test 4: Data size 8: : 70%|███████ | 7/10 [01:39<00:24, 8.03s/it, data_size=8, test_acc=0.5, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [01:39<00:24, 8.03s/it, data_size=8, test_acc=0.5, train_acc=0.5]Test 4: Data size 9: : 70%|███████ | 7/10 [01:45<00:24, 8.03s/it, data_size=9, test_acc=0.63, train_acc=0.5]Test 4: Data size 9: : 80%|████████ | 8/10 [01:45<00:14, 7.36s/it, data_size=9, test_acc=0.63, train_acc=0.5]Test 4: Data size 10: : 80%|████████ | 8/10 [01:45<00:14, 7.36s/it, data_size=9, test_acc=0.63, train_acc=0.5]Test 4: Data size 10: : 80%|████████ | 8/10 [01:51<00:14, 7.36s/it, data_size=10, test_acc=0.576, train_acc=0.604]Test 4: Data size 10: : 90%|█████████ | 9/10 [01:51<00:06, 6.94s/it, data_size=10, test_acc=0.576, train_acc=0.604]Test 4: Data size 11: : 90%|█████████ | 9/10 [01:51<00:06, 6.94s/it, data_size=10, test_acc=0.576, train_acc=0.604]Test 4: Data size 11: : 90%|█████████ | 9/10 [01:57<00:06, 6.94s/it, data_size=11, test_acc=0.688, train_acc=0.688]Test 4: Data size 11: : 100%|██████████| 10/10 [01:57<00:00, 6.72s/it, data_size=11, test_acc=0.688, train_acc=0.688]Test 4: Data size 11: : 100%|██████████| 10/10 [01:57<00:00, 11.78s/it, data_size=11, test_acc=0.688, train_acc=0.688]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 5: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [01:05<09:48, 65.40s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [01:05<09:48, 65.40s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [01:10<09:48, 65.40s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [01:10<04:00, 30.05s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [01:10<04:00, 30.05s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [01:15<04:00, 30.05s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [01:16<02:11, 18.79s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [01:16<02:11, 18.79s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [01:21<02:11, 18.79s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [01:21<01:21, 13.55s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [01:21<01:21, 13.55s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [01:26<01:21, 13.55s/it, data_size=6, test_acc=0.389, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [01:27<00:53, 10.64s/it, data_size=6, test_acc=0.389, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [01:27<00:53, 10.64s/it, data_size=6, test_acc=0.389, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [01:32<00:53, 10.64s/it, data_size=7, test_acc=0.423, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [01:32<00:35, 8.92s/it, data_size=7, test_acc=0.423, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [01:32<00:35, 8.92s/it, data_size=7, test_acc=0.423, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [01:38<00:35, 8.92s/it, data_size=8, test_acc=0.523, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [01:38<00:23, 7.89s/it, data_size=8, test_acc=0.523, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [01:38<00:23, 7.89s/it, data_size=8, test_acc=0.523, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [01:44<00:23, 7.89s/it, data_size=9, test_acc=0.418, train_acc=nan]Test 5: Data size 9: : 80%|████████ | 8/10 [01:44<00:14, 7.28s/it, data_size=9, test_acc=0.418, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [01:44<00:14, 7.28s/it, data_size=9, test_acc=0.418, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [01:50<00:14, 7.28s/it, data_size=10, test_acc=0.556, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [01:50<00:06, 6.89s/it, data_size=10, test_acc=0.556, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [01:50<00:06, 6.89s/it, data_size=10, test_acc=0.556, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [01:56<00:06, 6.89s/it, data_size=11, test_acc=0.571, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 6.68s/it, data_size=11, test_acc=0.571, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [01:56<00:00, 11.67s/it, data_size=11, test_acc=0.571, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 6: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [01:05<09:50, 65.67s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [01:05<09:50, 65.67s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [01:10<09:50, 65.67s/it, data_size=3, test_acc=0.253, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [01:11<04:01, 30.22s/it, data_size=3, test_acc=0.253, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [01:11<04:01, 30.22s/it, data_size=3, test_acc=0.253, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [01:16<04:01, 30.22s/it, data_size=4, test_acc=0.434, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [01:16<02:12, 18.92s/it, data_size=4, test_acc=0.434, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [01:16<02:12, 18.92s/it, data_size=4, test_acc=0.434, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [01:21<02:12, 18.92s/it, data_size=5, test_acc=0.496, train_acc=nan]Test 6: Data size 5: : 40%|████ | 4/10 [01:22<01:21, 13.65s/it, data_size=5, test_acc=0.496, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [01:22<01:21, 13.65s/it, data_size=5, test_acc=0.496, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [01:27<01:21, 13.65s/it, data_size=6, test_acc=0.282, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [01:27<00:53, 10.78s/it, data_size=6, test_acc=0.282, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [01:27<00:53, 10.78s/it, data_size=6, test_acc=0.282, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [01:33<00:53, 10.78s/it, data_size=7, test_acc=0.562, train_acc=0.5]Test 6: Data size 7: : 60%|██████ | 6/10 [01:33<00:36, 9.07s/it, data_size=7, test_acc=0.562, train_acc=0.5]Test 6: Data size 8: : 60%|██████ | 6/10 [01:33<00:36, 9.07s/it, data_size=7, test_acc=0.562, train_acc=0.5]Test 6: Data size 8: : 60%|██████ | 6/10 [01:39<00:36, 9.07s/it, data_size=8, test_acc=0.49, train_acc=0.5] Test 6: Data size 8: : 70%|███████ | 7/10 [01:39<00:24, 8.01s/it, data_size=8, test_acc=0.49, train_acc=0.5]Test 6: Data size 9: : 70%|███████ | 7/10 [01:39<00:24, 8.01s/it, data_size=8, test_acc=0.49, train_acc=0.5]Test 6: Data size 9: : 70%|███████ | 7/10 [01:45<00:24, 8.01s/it, data_size=9, test_acc=0.497, train_acc=0.5]Test 6: Data size 9: : 80%|████████ | 8/10 [01:45<00:14, 7.41s/it, data_size=9, test_acc=0.497, train_acc=0.5]Test 6: Data size 10: : 80%|████████ | 8/10 [01:45<00:14, 7.41s/it, data_size=9, test_acc=0.497, train_acc=0.5]Test 6: Data size 10: : 80%|████████ | 8/10 [01:51<00:14, 7.41s/it, data_size=10, test_acc=0.569, train_acc=0.5]Test 6: Data size 10: : 90%|█████████ | 9/10 [01:51<00:06, 6.99s/it, data_size=10, test_acc=0.569, train_acc=0.5]Test 6: Data size 11: : 90%|█████████ | 9/10 [01:51<00:06, 6.99s/it, data_size=10, test_acc=0.569, train_acc=0.5]Test 6: Data size 11: : 90%|█████████ | 9/10 [01:57<00:06, 6.99s/it, data_size=11, test_acc=0.657, train_acc=0.667]Test 6: Data size 11: : 100%|██████████| 10/10 [01:57<00:00, 6.75s/it, data_size=11, test_acc=0.657, train_acc=0.667]Test 6: Data size 11: : 100%|██████████| 10/10 [01:57<00:00, 11.78s/it, data_size=11, test_acc=0.657, train_acc=0.667]
0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 7: Data size 2: : 0%| | 0/10 [01:05<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [01:05<09:48, 65.42s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [01:05<09:48, 65.42s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [01:10<09:48, 65.42s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [01:10<04:00, 30.11s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [01:10<04:00, 30.11s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [01:16<04:00, 30.11s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [01:16<02:12, 18.87s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [01:16<02:12, 18.87s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [01:21<02:12, 18.87s/it, data_size=5, test_acc=0.323, train_acc=nan]Test 7: Data size 5: : 40%|████ | 4/10 [01:21<01:21, 13.61s/it, data_size=5, test_acc=0.323, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [01:21<01:21, 13.61s/it, data_size=5, test_acc=0.323, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [01:27<01:21, 13.61s/it, data_size=6, test_acc=0.25, train_acc=nan] Test 7: Data size 6: : 50%|█████ | 5/10 [01:27<00:53, 10.72s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [01:27<00:53, 10.72s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [01:32<00:53, 10.72s/it, data_size=7, test_acc=0.284, train_acc=nan]Test 7: Data size 7: : 60%|██████ | 6/10 [01:33<00:35, 8.97s/it, data_size=7, test_acc=0.284, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [01:33<00:35, 8.97s/it, data_size=7, test_acc=0.284, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [01:38<00:35, 8.97s/it, data_size=8, test_acc=0.239, train_acc=nan]Test 7: Data size 8: : 70%|███████ | 7/10 [01:38<00:23, 7.93s/it, data_size=8, test_acc=0.239, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [01:38<00:23, 7.93s/it, data_size=8, test_acc=0.239, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [01:44<00:23, 7.93s/it, data_size=9, test_acc=0.25, train_acc=nan] Test 7: Data size 9: : 80%|████████ | 8/10 [01:44<00:14, 7.33s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [01:44<00:14, 7.33s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [01:50<00:14, 7.33s/it, data_size=10, test_acc=0.328, train_acc=nan]Test 7: Data size 10: : 90%|█████████ | 9/10 [01:51<00:06, 6.96s/it, data_size=10, test_acc=0.328, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [01:51<00:06, 6.96s/it, data_size=10, test_acc=0.328, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [01:56<00:06, 6.96s/it, data_size=11, test_acc=0.344, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [01:57<00:00, 6.69s/it, data_size=11, test_acc=0.344, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [01:57<00:00, 11.71s/it, data_size=11, test_acc=0.344, train_acc=nan]
working on model Multimodal-middle-fusion-model-based-on-AlexNet with BADGE
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:13<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 0%| | 0/10 [00:13<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]
Traceback (most recent call last):
File "/home/agaut/CS229-Project/experiments/experiment.py", line 170, in run_experiments
self.tester.test_model(curr_model)
File "/home/agaut/CS229-Project/test_framework/tester.py", line 165, in test_model
for mode_ind in range(len(train_x))
File "/home/agaut/CS229-Project/test_framework/tester.py", line 165, in <listcomp>
for mode_ind in range(len(train_x))
IndexError: arrays used as indices must be of integer (or boolean) type
Got exception arrays used as indices must be of integer (or boolean) type for model BADGE with stack trace:
None
working on model Multimodal-late-fusion-model-based-on-AlexNet with RANDOM
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [00:00<00:04, 1.81it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.81it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.81it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [00:01<00:04, 1.83it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.83it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.83it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 30%|███ | 3/10 [00:01<00:03, 1.83it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.83it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:02<00:03, 1.83it/s, data_size=5, test_acc=0.25, train_acc=0.25]Test 0: Data size 5: : 40%|████ | 4/10 [00:02<00:03, 1.83it/s, data_size=5, test_acc=0.25, train_acc=0.25]Test 0: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.83it/s, data_size=5, test_acc=0.25, train_acc=0.25]Test 0: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.83it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 0: Data size 6: : 50%|█████ | 5/10 [00:02<00:02, 1.82it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 0: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.82it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 0: Data size 7: : 50%|█████ | 5/10 [00:03<00:02, 1.82it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 0: Data size 7: : 60%|██████ | 6/10 [00:03<00:02, 1.82it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 0: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.82it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 0: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.82it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 0: Data size 8: : 70%|███████ | 7/10 [00:03<00:01, 1.81it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 0: Data size 9: : 70%|███████ | 7/10 [00:03<00:01, 1.81it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 0: Data size 9: : 70%|███████ | 7/10 [00:04<00:01, 1.81it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 0: Data size 9: : 80%|████████ | 8/10 [00:04<00:01, 1.81it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 0: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.81it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 0: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.81it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 0: Data size 10: : 90%|█████████ | 9/10 [00:04<00:00, 1.81it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:04<00:00, 1.81it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:05<00:00, 1.81it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 0: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.81it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 0: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.81it/s, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 1: 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 2: : 10%|█ | 1/10 [00:00<00:04, 1.88it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.88it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.88it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 20%|██ | 2/10 [00:01<00:04, 1.87it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.87it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.87it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 30%|███ | 3/10 [00:01<00:03, 1.87it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.87it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 50%|█████ | 5/10 [00:02<00:02, 1.87it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.87it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:03<00:02, 1.87it/s, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 70%|███████ | 7/10 [00:03<00:01, 1.85it/s, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:03<00:01, 1.85it/s, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:04<00:01, 1.85it/s, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 80%|████████ | 8/10 [00:04<00:01, 1.84it/s, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.84it/s, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.84it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 1: Data size 10: : 90%|█████████ | 9/10 [00:04<00:00, 1.84it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:04<00:00, 1.84it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:05<00:00, 1.84it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 1: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.84it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 1: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.85it/s, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:00<00:04, 1.89it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.89it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.89it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:01<00:04, 1.89it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.89it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.89it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:01<00:03, 1.88it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.88it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:02<00:03, 1.88it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [00:02<00:02, 1.87it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.87it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:03<00:02, 1.87it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 7: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 70%|███████ | 7/10 [00:03<00:01, 1.87it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 70%|███████ | 7/10 [00:03<00:01, 1.87it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 70%|███████ | 7/10 [00:04<00:01, 1.87it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 80%|████████ | 8/10 [00:04<00:01, 1.87it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.87it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.87it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:04<00:00, 1.87it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:04<00:00, 1.87it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:05<00:00, 1.87it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.87it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.87it/s, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:00<00:04, 1.94it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.94it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.94it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:01<00:04, 1.94it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.94it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.94it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:01<00:03, 1.93it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.93it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.93it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 40%|████ | 4/10 [00:02<00:03, 1.93it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.93it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.93it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 50%|█████ | 5/10 [00:02<00:02, 1.93it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.93it/s, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.93it/s, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 60%|██████ | 6/10 [00:03<00:02, 1.92it/s, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.92it/s, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.92it/s, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 70%|███████ | 7/10 [00:03<00:01, 1.92it/s, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:03<00:01, 1.92it/s, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:04<00:01, 1.92it/s, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 80%|████████ | 8/10 [00:04<00:01, 1.91it/s, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.91it/s, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.91it/s, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:04<00:00, 1.91it/s, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:04<00:00, 1.91it/s, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:05<00:00, 1.91it/s, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.90it/s, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.92it/s, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:00<00:04, 1.87it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.87it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.87it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:01<00:04, 1.87it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.87it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.87it/s, data_size=4, test_acc=0.25, train_acc=0.25]Test 4: Data size 4: : 30%|███ | 3/10 [00:01<00:03, 1.87it/s, data_size=4, test_acc=0.25, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.87it/s, data_size=4, test_acc=0.25, train_acc=0.25]Test 4: Data size 5: : 30%|███ | 3/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=0.25]Test 4: Data size 5: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=0.25]Test 4: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=0.25]Test 4: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 4: Data size 6: : 50%|█████ | 5/10 [00:02<00:02, 1.86it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 4: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.86it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 4: Data size 7: : 50%|█████ | 5/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 4: Data size 7: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 4: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 4: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.86it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 4: Data size 8: : 70%|███████ | 7/10 [00:03<00:01, 1.86it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 4: Data size 9: : 70%|███████ | 7/10 [00:03<00:01, 1.86it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 4: Data size 9: : 70%|███████ | 7/10 [00:04<00:01, 1.86it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 4: Data size 9: : 80%|████████ | 8/10 [00:04<00:01, 1.85it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 4: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.85it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 4: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.85it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:04<00:00, 1.84it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:04<00:00, 1.84it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:05<00:00, 1.84it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 4: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.84it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 4: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.85it/s, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:00<00:04, 1.91it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.91it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.91it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:01<00:04, 1.90it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.90it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.90it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [00:01<00:03, 1.90it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.90it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.90it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:02<00:03, 1.89it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.89it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.89it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 5: Data size 6: : 50%|█████ | 5/10 [00:02<00:02, 1.89it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 5: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.89it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 5: Data size 7: : 50%|█████ | 5/10 [00:03<00:02, 1.89it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 5: Data size 7: : 60%|██████ | 6/10 [00:03<00:02, 1.89it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 5: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.89it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 5: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.89it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 5: Data size 8: : 70%|███████ | 7/10 [00:03<00:01, 1.89it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 5: Data size 9: : 70%|███████ | 7/10 [00:03<00:01, 1.89it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 5: Data size 9: : 70%|███████ | 7/10 [00:04<00:01, 1.89it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 5: Data size 9: : 80%|████████ | 8/10 [00:04<00:01, 1.88it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 5: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.88it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 5: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.88it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:04<00:00, 1.88it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:04<00:00, 1.88it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:05<00:00, 1.88it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 5: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.88it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 5: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.89it/s, data_size=11, test_acc=0.25, train_acc=0.25]
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0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [00:00<00:04, 1.88it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.88it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:00<00:04, 1.88it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [00:01<00:04, 1.88it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.88it/s, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:01<00:04, 1.88it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [00:01<00:03, 1.88it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:01<00:03, 1.88it/s, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:02<00:03, 1.88it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:02<00:03, 1.87it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 7: Data size 6: : 50%|█████ | 5/10 [00:02<00:02, 1.86it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 7: Data size 7: : 50%|█████ | 5/10 [00:02<00:02, 1.86it/s, data_size=6, test_acc=0.25, train_acc=0.25]Test 7: Data size 7: : 50%|█████ | 5/10 [00:03<00:02, 1.86it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 7: Data size 7: : 60%|██████ | 6/10 [00:03<00:02, 1.85it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 7: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.85it/s, data_size=7, test_acc=0.25, train_acc=0.25]Test 7: Data size 8: : 60%|██████ | 6/10 [00:03<00:02, 1.85it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 7: Data size 8: : 70%|███████ | 7/10 [00:03<00:01, 1.85it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 7: Data size 9: : 70%|███████ | 7/10 [00:03<00:01, 1.85it/s, data_size=8, test_acc=0.25, train_acc=0.25]Test 7: Data size 9: : 70%|███████ | 7/10 [00:04<00:01, 1.85it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 7: Data size 9: : 80%|████████ | 8/10 [00:04<00:01, 1.85it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 7: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.85it/s, data_size=9, test_acc=0.25, train_acc=0.25]Test 7: Data size 10: : 80%|████████ | 8/10 [00:04<00:01, 1.85it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 7: Data size 10: : 90%|█████████ | 9/10 [00:04<00:00, 1.84it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:04<00:00, 1.84it/s, data_size=10, test_acc=0.25, train_acc=0.25]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:05<00:00, 1.84it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 7: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.84it/s, data_size=11, test_acc=0.25, train_acc=0.25]Test 7: Data size 11: : 100%|██████████| 10/10 [00:05<00:00, 1.85it/s, data_size=11, test_acc=0.25, train_acc=0.25]
working on model Multimodal-late-fusion-model-based-on-AlexNet with MIN_MAX
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.33s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.33s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.33s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [00:08<00:34, 4.33s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:08<00:34, 4.33s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:12<00:34, 4.33s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 30%|███ | 3/10 [00:13<00:30, 4.34s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:13<00:30, 4.34s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:17<00:30, 4.34s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 40%|████ | 4/10 [00:17<00:26, 4.34s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 40%|████ | 4/10 [00:17<00:26, 4.34s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 40%|████ | 4/10 [00:21<00:26, 4.34s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.34s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.34s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.34s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 60%|██████ | 6/10 [00:26<00:17, 4.34s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 60%|██████ | 6/10 [00:26<00:17, 4.34s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 60%|██████ | 6/10 [00:30<00:17, 4.34s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 70%|███████ | 7/10 [00:30<00:13, 4.34s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 70%|███████ | 7/10 [00:30<00:13, 4.34s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 70%|███████ | 7/10 [00:34<00:13, 4.34s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 80%|████████ | 8/10 [00:34<00:08, 4.34s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 80%|████████ | 8/10 [00:34<00:08, 4.34s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 80%|████████ | 8/10 [00:38<00:08, 4.34s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 90%|█████████ | 9/10 [00:39<00:04, 4.34s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:39<00:04, 4.34s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:43<00:04, 4.34s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 100%|██████████| 10/10 [00:43<00:00, 4.34s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 100%|██████████| 10/10 [00:43<00:00, 4.34s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 1: 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.14s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.14s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 40%|████ | 4/10 [00:16<00:24, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:16<00:24, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:20<00:24, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.14s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 60%|██████ | 6/10 [00:24<00:16, 4.15s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:24<00:16, 4.15s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:28<00:16, 4.15s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.15s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.15s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.15s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.15s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.15s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.15s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.15s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.15s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:03<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:04<00:36, 4.10s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:04<00:36, 4.10s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:08<00:36, 4.10s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:08<00:32, 4.12s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:08<00:32, 4.12s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:12<00:32, 4.12s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:12<00:28, 4.11s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:12<00:28, 4.11s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:16<00:28, 4.11s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 2: Data size 5: : 40%|████ | 4/10 [00:16<00:24, 4.11s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 2: Data size 6: : 40%|████ | 4/10 [00:16<00:24, 4.11s/it, data_size=5, test_acc=0.25, train_acc=0.25]Test 2: Data size 6: : 40%|████ | 4/10 [00:20<00:24, 4.11s/it, data_size=6, test_acc=0.25, train_acc=0.25]Test 2: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.11s/it, data_size=6, test_acc=0.25, train_acc=0.25]Test 2: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.11s/it, data_size=6, test_acc=0.25, train_acc=0.25]Test 2: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.11s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 7: : 60%|██████ | 6/10 [00:24<00:16, 4.11s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 60%|██████ | 6/10 [00:24<00:16, 4.11s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 60%|██████ | 6/10 [00:28<00:16, 4.11s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 70%|███████ | 7/10 [00:28<00:12, 4.11s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 70%|███████ | 7/10 [00:28<00:12, 4.11s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 70%|███████ | 7/10 [00:32<00:12, 4.11s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 80%|████████ | 8/10 [00:32<00:08, 4.11s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 80%|████████ | 8/10 [00:32<00:08, 4.11s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 80%|████████ | 8/10 [00:36<00:08, 4.11s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:36<00:04, 4.11s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:36<00:04, 4.11s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:40<00:04, 4.11s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.11s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.11s/it, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.23s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.23s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.23s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.24s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.23s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.23s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.23s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.24s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.24s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:21<00:25, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:38<00:08, 4.24s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.24s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.24s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.24s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.25s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.24s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.22s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.22s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.22s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.19s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.19s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.19s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.18s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.18s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.18s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.18s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.18s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:20<00:25, 4.18s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.18s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.18s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.18s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.18s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.18s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.18s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.19s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.19s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.19s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.18s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.18s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.18s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.19s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.19s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.19s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.19s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.19s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.30s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.30s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.30s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:08<00:34, 4.30s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:08<00:34, 4.30s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:12<00:34, 4.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [00:12<00:30, 4.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:12<00:30, 4.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:17<00:30, 4.30s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:17<00:25, 4.30s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:17<00:25, 4.30s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:21<00:25, 4.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.30s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [00:25<00:17, 4.30s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:25<00:17, 4.30s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:29<00:17, 4.30s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [00:30<00:12, 4.30s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:30<00:12, 4.30s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:34<00:12, 4.30s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 80%|████████ | 8/10 [00:34<00:08, 4.30s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:34<00:08, 4.30s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:38<00:08, 4.30s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.30s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.30s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.30s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [00:43<00:00, 4.30s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [00:43<00:00, 4.30s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.18s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.18s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.18s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.18s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.18s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.18s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.18s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.18s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:20<00:25, 4.18s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.18s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.18s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.18s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.18s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.18s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.18s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.19s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.19s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.19s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.18s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.18s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.18s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.18s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.18s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.18s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.18s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.18s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.19s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.19s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.19s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.19s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.19s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.19s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.19s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.19s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.19s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.19s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.19s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:20<00:25, 4.19s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.19s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.19s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:25<00:20, 4.19s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.19s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.19s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.19s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.19s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.19s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.19s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.19s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.19s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.19s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.19s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.19s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.19s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.20s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.19s/it, data_size=11, test_acc=0.25, train_acc=nan]
working on model Multimodal-late-fusion-model-based-on-AlexNet with MIN_MARGIN
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0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.29s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.29s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.29s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:08<00:34, 4.29s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:08<00:34, 4.29s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:12<00:34, 4.29s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:12<00:30, 4.29s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:12<00:30, 4.29s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:17<00:30, 4.29s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [00:17<00:25, 4.29s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:17<00:25, 4.29s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:21<00:25, 4.29s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.29s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.29s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.29s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 60%|██████ | 6/10 [00:25<00:17, 4.29s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:25<00:17, 4.29s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:29<00:17, 4.29s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 70%|███████ | 7/10 [00:30<00:12, 4.28s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:30<00:12, 4.28s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:34<00:12, 4.28s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 80%|████████ | 8/10 [00:34<00:08, 4.29s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:34<00:08, 4.29s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:38<00:08, 4.29s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.29s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.29s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.29s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.29s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.29s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.22s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.22s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.22s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.22s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.22s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.22s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.22s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.22s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.22s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.22s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.22s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:20<00:25, 4.22s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.22s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.22s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.22s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.23s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.23s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.23s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.23s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.23s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.23s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.22s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.22s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.22s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.22s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.22s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.22s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.22s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.22s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.15s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.15s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.15s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.15s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.15s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.15s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.15s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.15s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.15s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 40%|████ | 4/10 [00:16<00:24, 4.15s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:16<00:24, 4.15s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:20<00:24, 4.15s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.15s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.15s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.15s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 60%|██████ | 6/10 [00:24<00:16, 4.15s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:24<00:16, 4.15s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:28<00:16, 4.15s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.15s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.15s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.15s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.15s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.15s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.15s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.15s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.15s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:03<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:04<00:36, 4.10s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:04<00:36, 4.10s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:08<00:36, 4.10s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:08<00:32, 4.10s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:08<00:32, 4.10s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:12<00:32, 4.10s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [00:12<00:28, 4.11s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:12<00:28, 4.11s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:16<00:28, 4.11s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:16<00:24, 4.11s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:16<00:24, 4.11s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:20<00:24, 4.11s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.11s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.11s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.11s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [00:24<00:16, 4.11s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:24<00:16, 4.11s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:28<00:16, 4.11s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [00:28<00:12, 4.11s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:28<00:12, 4.11s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:32<00:12, 4.11s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 80%|████████ | 8/10 [00:32<00:08, 4.11s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:32<00:08, 4.11s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:36<00:08, 4.11s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:36<00:04, 4.11s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:36<00:04, 4.11s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:40<00:04, 4.11s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.11s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.11s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.25s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.24s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.26s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.26s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.26s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 40%|████ | 4/10 [00:17<00:25, 4.25s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:17<00:25, 4.25s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:21<00:25, 4.25s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.25s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.25s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.25s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 60%|██████ | 6/10 [00:25<00:17, 4.25s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:25<00:17, 4.25s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:29<00:17, 4.25s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.25s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.25s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.25s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 80%|████████ | 8/10 [00:34<00:08, 4.25s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:34<00:08, 4.25s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:38<00:08, 4.25s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 6: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.25s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.25s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.25s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 6: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.25s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 6: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.25s/it, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.14s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.14s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [00:12<00:28, 4.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:12<00:28, 4.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:16<00:28, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 40%|████ | 4/10 [00:16<00:24, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:16<00:24, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:20<00:24, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.14s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 60%|██████ | 6/10 [00:24<00:16, 4.14s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:24<00:16, 4.14s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:28<00:16, 4.14s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 70%|███████ | 7/10 [00:28<00:12, 4.14s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:28<00:12, 4.14s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.14s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.15s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.15s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.14s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.14s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.14s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.14s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.14s/it, data_size=11, test_acc=0.25, train_acc=nan]
working on model Multimodal-late-fusion-model-based-on-AlexNet with MAX_ENTROPY
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.13s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.13s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.13s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.14s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 30%|███ | 3/10 [00:12<00:28, 4.13s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:12<00:28, 4.13s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:16<00:28, 4.13s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 40%|████ | 4/10 [00:16<00:24, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 40%|████ | 4/10 [00:16<00:24, 4.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 40%|████ | 4/10 [00:20<00:24, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.14s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.14s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 60%|██████ | 6/10 [00:24<00:16, 4.14s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 60%|██████ | 6/10 [00:24<00:16, 4.14s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 60%|██████ | 6/10 [00:28<00:16, 4.14s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 70%|███████ | 7/10 [00:28<00:12, 4.14s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 70%|███████ | 7/10 [00:28<00:12, 4.14s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 70%|███████ | 7/10 [00:32<00:12, 4.14s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.14s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.14s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.14s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.14s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.14s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.14s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.14s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 100%|██████████| 10/10 [00:41<00:00, 4.14s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 1: 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:03<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 2: : 10%|█ | 1/10 [00:04<00:36, 4.09s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:04<00:36, 4.09s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:08<00:36, 4.09s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 20%|██ | 2/10 [00:08<00:32, 4.09s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:08<00:32, 4.09s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:12<00:32, 4.09s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 30%|███ | 3/10 [00:12<00:28, 4.09s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:12<00:28, 4.09s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:16<00:28, 4.09s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 40%|████ | 4/10 [00:16<00:24, 4.09s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:16<00:24, 4.09s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:20<00:24, 4.09s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 50%|█████ | 5/10 [00:20<00:20, 4.09s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:20<00:20, 4.09s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:24<00:20, 4.09s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 60%|██████ | 6/10 [00:24<00:16, 4.10s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:24<00:16, 4.10s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:28<00:16, 4.10s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 70%|███████ | 7/10 [00:28<00:12, 4.10s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:28<00:12, 4.10s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:32<00:12, 4.10s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 80%|████████ | 8/10 [00:32<00:08, 4.10s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:32<00:08, 4.10s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:36<00:08, 4.10s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 90%|█████████ | 9/10 [00:36<00:04, 4.09s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:36<00:04, 4.09s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:40<00:04, 4.09s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 100%|██████████| 10/10 [00:40<00:00, 4.09s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 100%|██████████| 10/10 [00:40<00:00, 4.09s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.31s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.31s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.31s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:08<00:34, 4.30s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:08<00:34, 4.30s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:12<00:34, 4.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:12<00:30, 4.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:12<00:30, 4.30s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:17<00:30, 4.30s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [00:17<00:25, 4.30s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:17<00:25, 4.30s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:21<00:25, 4.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.30s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 60%|██████ | 6/10 [00:25<00:17, 4.30s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:25<00:17, 4.30s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 60%|██████ | 6/10 [00:29<00:17, 4.30s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 8: : 70%|███████ | 7/10 [00:30<00:12, 4.30s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:30<00:12, 4.30s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 70%|███████ | 7/10 [00:34<00:12, 4.30s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 2: Data size 9: : 80%|████████ | 8/10 [00:34<00:08, 4.31s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:34<00:08, 4.31s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 2: Data size 10: : 80%|████████ | 8/10 [00:38<00:08, 4.31s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.31s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.31s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.31s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [00:43<00:00, 4.31s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 2: Data size 11: : 100%|██████████| 10/10 [00:43<00:00, 4.30s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.25s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.24s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.24s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.24s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.24s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.24s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.24s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:21<00:25, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 80%|████████ | 8/10 [00:38<00:08, 4.24s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.24s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.24s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.24s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.24s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 3: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.24s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:04<00:37, 4.20s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:04<00:37, 4.20s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:08<00:37, 4.20s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.20s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.20s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.20s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.21s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.21s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.21s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.21s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.21s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:20<00:25, 4.21s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.20s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.20s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.20s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.20s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.20s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.20s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.20s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.20s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.20s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.20s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.20s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.20s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:37<00:04, 4.20s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:37<00:04, 4.20s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:41<00:04, 4.20s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.20s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.20s/it, data_size=11, test_acc=0.25, train_acc=nan]
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0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:04<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:04<00:38, 4.23s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:04<00:38, 4.23s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:08<00:38, 4.23s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:08<00:33, 4.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:12<00:33, 4.24s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [00:12<00:29, 4.23s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:12<00:29, 4.23s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:16<00:29, 4.23s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 40%|████ | 4/10 [00:16<00:25, 4.24s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:16<00:25, 4.24s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:21<00:25, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [00:21<00:21, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:21<00:21, 4.24s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:25<00:21, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 60%|██████ | 6/10 [00:25<00:16, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:25<00:16, 4.24s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:29<00:16, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 70%|███████ | 7/10 [00:29<00:12, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:29<00:12, 4.24s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:33<00:12, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 80%|████████ | 8/10 [00:33<00:08, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:33<00:08, 4.24s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:37<00:08, 4.24s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 90%|█████████ | 9/10 [00:38<00:04, 4.23s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:38<00:04, 4.23s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:42<00:04, 4.23s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.24s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [00:42<00:00, 4.24s/it, data_size=11, test_acc=0.25, train_acc=nan]
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working on model Multimodal-late-fusion-model-based-on-AlexNet with CLUSTER_MARGIN
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label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 0: Data size 2: : 0%| | 0/10 [00:16<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 10%|█ | 1/10 [00:16<02:25, 16.20s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:16<02:25, 16.20s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 10%|█ | 1/10 [00:20<02:25, 16.20s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 3: : 20%|██ | 2/10 [00:20<01:13, 9.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:20<01:13, 9.18s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 20%|██ | 2/10 [00:24<01:13, 9.18s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 4: : 30%|███ | 3/10 [00:24<00:48, 6.93s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:24<00:48, 6.93s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 30%|███ | 3/10 [00:28<00:48, 6.93s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 5: : 40%|████ | 4/10 [00:28<00:35, 5.88s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 40%|████ | 4/10 [00:28<00:35, 5.88s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 40%|████ | 4/10 [00:33<00:35, 5.88s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 6: : 50%|█████ | 5/10 [00:33<00:26, 5.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 50%|█████ | 5/10 [00:33<00:26, 5.30s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 50%|█████ | 5/10 [00:37<00:26, 5.30s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 7: : 60%|██████ | 6/10 [00:37<00:19, 4.95s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 60%|██████ | 6/10 [00:37<00:19, 4.95s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 60%|██████ | 6/10 [00:41<00:19, 4.95s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 8: : 70%|███████ | 7/10 [00:41<00:14, 4.74s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 70%|███████ | 7/10 [00:41<00:14, 4.74s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 70%|███████ | 7/10 [00:46<00:14, 4.74s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 9: : 80%|████████ | 8/10 [00:46<00:09, 4.60s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 80%|████████ | 8/10 [00:46<00:09, 4.60s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 80%|████████ | 8/10 [00:50<00:09, 4.60s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 10: : 90%|█████████ | 9/10 [00:50<00:04, 4.51s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:50<00:04, 4.51s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 90%|█████████ | 9/10 [00:54<00:04, 4.51s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 100%|██████████| 10/10 [00:54<00:00, 4.44s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 0: Data size 11: : 100%|██████████| 10/10 [00:54<00:00, 5.47s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 1: 0%| | 0/10 [00:00<?, ?it/s]Test 1: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 1: Data size 2: : 0%| | 0/10 [00:16<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 2: : 10%|█ | 1/10 [00:16<02:26, 16.30s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:16<02:26, 16.30s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 10%|█ | 1/10 [00:20<02:26, 16.30s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 3: : 20%|██ | 2/10 [00:20<01:14, 9.26s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:20<01:14, 9.26s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 20%|██ | 2/10 [00:24<01:14, 9.26s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 4: : 30%|███ | 3/10 [00:24<00:49, 7.01s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:24<00:49, 7.01s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 30%|███ | 3/10 [00:29<00:49, 7.01s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 5: : 40%|████ | 4/10 [00:29<00:35, 5.96s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:29<00:35, 5.96s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 40%|████ | 4/10 [00:33<00:35, 5.96s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 6: : 50%|█████ | 5/10 [00:33<00:26, 5.37s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:33<00:26, 5.37s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 50%|█████ | 5/10 [00:37<00:26, 5.37s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 7: : 60%|██████ | 6/10 [00:37<00:20, 5.03s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:37<00:20, 5.03s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 60%|██████ | 6/10 [00:42<00:20, 5.03s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 8: : 70%|███████ | 7/10 [00:42<00:14, 4.80s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:42<00:14, 4.80s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 70%|███████ | 7/10 [00:46<00:14, 4.80s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 9: : 80%|████████ | 8/10 [00:46<00:09, 4.65s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:46<00:09, 4.65s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 80%|████████ | 8/10 [00:50<00:09, 4.65s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 10: : 90%|█████████ | 9/10 [00:50<00:04, 4.55s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:50<00:04, 4.55s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 90%|█████████ | 9/10 [00:55<00:04, 4.55s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 100%|██████████| 10/10 [00:55<00:00, 4.49s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 1: Data size 11: : 100%|██████████| 10/10 [00:55<00:00, 5.53s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 2: 0%| | 0/10 [00:00<?, ?it/s]Test 2: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 2: Data size 2: : 0%| | 0/10 [00:16<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 2: : 10%|█ | 1/10 [00:16<02:29, 16.58s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:16<02:29, 16.58s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 10%|█ | 1/10 [00:20<02:29, 16.58s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 3: : 20%|██ | 2/10 [00:20<01:15, 9.42s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:20<01:15, 9.42s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 20%|██ | 2/10 [00:25<01:15, 9.42s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 4: : 30%|███ | 3/10 [00:25<00:49, 7.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:25<00:49, 7.14s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 30%|███ | 3/10 [00:29<00:49, 7.14s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 5: : 40%|████ | 4/10 [00:29<00:36, 6.07s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.07s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 40%|████ | 4/10 [00:34<00:36, 6.07s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 6: : 50%|█████ | 5/10 [00:34<00:27, 5.48s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:34<00:27, 5.48s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 2: Data size 7: : 50%|█████ | 5/10 [00:38<00:27, 5.48s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 7: : 60%|██████ | 6/10 [00:38<00:20, 5.12s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 60%|██████ | 6/10 [00:38<00:20, 5.12s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 60%|██████ | 6/10 [00:43<00:20, 5.12s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 8: : 70%|███████ | 7/10 [00:43<00:14, 4.90s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 70%|███████ | 7/10 [00:43<00:14, 4.90s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 70%|███████ | 7/10 [00:47<00:14, 4.90s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 9: : 80%|████████ | 8/10 [00:47<00:09, 4.75s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 80%|████████ | 8/10 [00:47<00:09, 4.75s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 80%|████████ | 8/10 [00:51<00:09, 4.75s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 10: : 90%|█████████ | 9/10 [00:51<00:04, 4.64s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:51<00:04, 4.64s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 90%|█████████ | 9/10 [00:56<00:04, 4.64s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 100%|██████████| 10/10 [00:56<00:00, 4.58s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 2: Data size 11: : 100%|██████████| 10/10 [00:56<00:00, 5.64s/it, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 3: 0%| | 0/10 [00:00<?, ?it/s]Test 3: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 3: Data size 2: : 0%| | 0/10 [00:15<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 2: : 10%|█ | 1/10 [00:16<02:24, 16.06s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:16<02:24, 16.06s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 10%|█ | 1/10 [00:20<02:24, 16.06s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 3: : 20%|██ | 2/10 [00:20<01:12, 9.11s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:20<01:12, 9.11s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 20%|██ | 2/10 [00:24<01:12, 9.11s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 4: : 30%|███ | 3/10 [00:24<00:48, 6.88s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:24<00:48, 6.88s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 30%|███ | 3/10 [00:28<00:48, 6.88s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 5: : 40%|████ | 4/10 [00:28<00:35, 5.84s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:28<00:35, 5.84s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 40%|████ | 4/10 [00:32<00:35, 5.84s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 6: : 50%|█████ | 5/10 [00:32<00:26, 5.26s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:33<00:26, 5.26s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 3: Data size 7: : 50%|█████ | 5/10 [00:37<00:26, 5.26s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 3: Data size 7: : 60%|██████ | 6/10 [00:37<00:19, 4.91s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 3: Data size 8: : 60%|██████ | 6/10 [00:37<00:19, 4.91s/it, data_size=7, test_acc=0.25, train_acc=0.25]Test 3: Data size 8: : 60%|██████ | 6/10 [00:41<00:19, 4.91s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 3: Data size 8: : 70%|███████ | 7/10 [00:41<00:14, 4.69s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 3: Data size 9: : 70%|███████ | 7/10 [00:41<00:14, 4.69s/it, data_size=8, test_acc=0.25, train_acc=0.25]Test 3: Data size 9: : 70%|███████ | 7/10 [00:45<00:14, 4.69s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 3: Data size 9: : 80%|████████ | 8/10 [00:45<00:09, 4.55s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 3: Data size 10: : 80%|████████ | 8/10 [00:45<00:09, 4.55s/it, data_size=9, test_acc=0.25, train_acc=0.25]Test 3: Data size 10: : 80%|████████ | 8/10 [00:49<00:09, 4.55s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 3: Data size 10: : 90%|█████████ | 9/10 [00:49<00:04, 4.46s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:49<00:04, 4.46s/it, data_size=10, test_acc=0.25, train_acc=0.25]Test 3: Data size 11: : 90%|█████████ | 9/10 [00:54<00:04, 4.46s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 3: Data size 11: : 100%|██████████| 10/10 [00:54<00:00, 4.39s/it, data_size=11, test_acc=0.25, train_acc=0.25]Test 3: Data size 11: : 100%|██████████| 10/10 [00:54<00:00, 5.42s/it, data_size=11, test_acc=0.25, train_acc=0.25]
0%| | 0/10 [00:00<?, ?it/s]Test 4: 0%| | 0/10 [00:00<?, ?it/s]Test 4: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 4: Data size 2: : 0%| | 0/10 [00:16<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 2: : 10%|█ | 1/10 [00:16<02:26, 16.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:16<02:26, 16.25s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 10%|█ | 1/10 [00:20<02:26, 16.25s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 3: : 20%|██ | 2/10 [00:20<01:14, 9.25s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:20<01:14, 9.25s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 20%|██ | 2/10 [00:24<01:14, 9.25s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 4: : 30%|███ | 3/10 [00:24<00:49, 7.01s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:24<00:49, 7.01s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 30%|███ | 3/10 [00:29<00:49, 7.01s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 5: : 40%|████ | 4/10 [00:29<00:35, 5.96s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:29<00:35, 5.96s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 40%|████ | 4/10 [00:33<00:35, 5.96s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 6: : 50%|█████ | 5/10 [00:33<00:26, 5.38s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:33<00:26, 5.38s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 50%|█████ | 5/10 [00:37<00:26, 5.38s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 7: : 60%|██████ | 6/10 [00:37<00:20, 5.03s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:37<00:20, 5.03s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 60%|██████ | 6/10 [00:42<00:20, 5.03s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 8: : 70%|███████ | 7/10 [00:42<00:14, 4.80s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:42<00:14, 4.80s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 70%|███████ | 7/10 [00:46<00:14, 4.80s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 9: : 80%|████████ | 8/10 [00:46<00:09, 4.65s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:46<00:09, 4.65s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 80%|████████ | 8/10 [00:50<00:09, 4.65s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 10: : 90%|█████████ | 9/10 [00:51<00:04, 4.56s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:51<00:04, 4.56s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 90%|█████████ | 9/10 [00:55<00:04, 4.56s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:55<00:00, 4.49s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 4: Data size 11: : 100%|██████████| 10/10 [00:55<00:00, 5.54s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 5: 0%| | 0/10 [00:00<?, ?it/s]Test 5: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 5: Data size 2: : 0%| | 0/10 [00:16<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 2: : 10%|█ | 1/10 [00:16<02:26, 16.24s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:16<02:26, 16.24s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 10%|█ | 1/10 [00:20<02:26, 16.24s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 3: : 20%|██ | 2/10 [00:20<01:13, 9.23s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:20<01:13, 9.23s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 20%|██ | 2/10 [00:24<01:13, 9.23s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 4: : 30%|███ | 3/10 [00:24<00:48, 6.98s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:24<00:48, 6.98s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 30%|███ | 3/10 [00:29<00:48, 6.98s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 5: : 40%|████ | 4/10 [00:29<00:35, 5.92s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:29<00:35, 5.92s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 40%|████ | 4/10 [00:33<00:35, 5.92s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 6: : 50%|█████ | 5/10 [00:33<00:26, 5.34s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:33<00:26, 5.34s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 50%|█████ | 5/10 [00:37<00:26, 5.34s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 7: : 60%|██████ | 6/10 [00:37<00:19, 4.98s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:37<00:19, 4.98s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 60%|██████ | 6/10 [00:41<00:19, 4.98s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 8: : 70%|███████ | 7/10 [00:42<00:14, 4.76s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:42<00:14, 4.76s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 70%|███████ | 7/10 [00:46<00:14, 4.76s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 9: : 80%|████████ | 8/10 [00:46<00:09, 4.61s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:46<00:09, 4.61s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 80%|████████ | 8/10 [00:50<00:09, 4.61s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 10: : 90%|█████████ | 9/10 [00:50<00:04, 4.52s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:50<00:04, 4.52s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 90%|█████████ | 9/10 [00:54<00:04, 4.52s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [00:54<00:00, 4.45s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 5: Data size 11: : 100%|██████████| 10/10 [00:54<00:00, 5.50s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 6: 0%| | 0/10 [00:00<?, ?it/s]Test 6: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 6: Data size 2: : 0%| | 0/10 [00:16<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 2: : 10%|█ | 1/10 [00:16<02:28, 16.49s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:16<02:28, 16.49s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 10%|█ | 1/10 [00:20<02:28, 16.49s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 3: : 20%|██ | 2/10 [00:20<01:15, 9.38s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:20<01:15, 9.38s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 20%|██ | 2/10 [00:25<01:15, 9.38s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 4: : 30%|███ | 3/10 [00:25<00:49, 7.12s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:25<00:49, 7.12s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 30%|███ | 3/10 [00:29<00:49, 7.12s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 5: : 40%|████ | 4/10 [00:29<00:36, 6.04s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.04s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 40%|████ | 4/10 [00:33<00:36, 6.04s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 6: : 50%|█████ | 5/10 [00:34<00:27, 5.45s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:34<00:27, 5.45s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 50%|█████ | 5/10 [00:38<00:27, 5.45s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 7: : 60%|██████ | 6/10 [00:38<00:20, 5.09s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:38<00:20, 5.09s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 60%|██████ | 6/10 [00:42<00:20, 5.09s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 8: : 70%|███████ | 7/10 [00:42<00:14, 4.87s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:42<00:14, 4.87s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 70%|███████ | 7/10 [00:47<00:14, 4.87s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 9: : 80%|████████ | 8/10 [00:47<00:09, 4.72s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:47<00:09, 4.72s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 80%|████████ | 8/10 [00:51<00:09, 4.72s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 10: : 90%|█████████ | 9/10 [00:51<00:04, 4.63s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:51<00:04, 4.63s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 90%|█████████ | 9/10 [00:56<00:04, 4.63s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [00:56<00:00, 4.56s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 6: Data size 11: : 100%|██████████| 10/10 [00:56<00:00, 5.62s/it, data_size=11, test_acc=0.25, train_acc=nan]
0%| | 0/10 [00:00<?, ?it/s]Test 7: 0%| | 0/10 [00:00<?, ?it/s]Test 7: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]/home/agaut/CS229-Project/test_framework/metrics.py:41: RuntimeWarning: invalid value encountered in true_divide
label_accuracies = np.sum(correct_predictions, axis=0) / np.sum(y_actual, axis=0)
Test 7: Data size 2: : 0%| | 0/10 [00:16<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 2: : 10%|█ | 1/10 [00:16<02:29, 16.61s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:16<02:29, 16.61s/it, data_size=2, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 10%|█ | 1/10 [00:20<02:29, 16.61s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 3: : 20%|██ | 2/10 [00:21<01:15, 9.45s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:21<01:15, 9.45s/it, data_size=3, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 20%|██ | 2/10 [00:25<01:15, 9.45s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 4: : 30%|███ | 3/10 [00:25<00:50, 7.17s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:25<00:50, 7.17s/it, data_size=4, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 30%|███ | 3/10 [00:29<00:50, 7.17s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 5: : 40%|████ | 4/10 [00:29<00:36, 6.09s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:29<00:36, 6.09s/it, data_size=5, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 40%|████ | 4/10 [00:34<00:36, 6.09s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 6: : 50%|█████ | 5/10 [00:34<00:27, 5.49s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:34<00:27, 5.49s/it, data_size=6, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 50%|█████ | 5/10 [00:38<00:27, 5.49s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 7: : 60%|██████ | 6/10 [00:38<00:20, 5.13s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:38<00:20, 5.13s/it, data_size=7, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 60%|██████ | 6/10 [00:43<00:20, 5.13s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 8: : 70%|███████ | 7/10 [00:43<00:14, 4.90s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:43<00:14, 4.90s/it, data_size=8, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 70%|███████ | 7/10 [00:47<00:14, 4.90s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 9: : 80%|████████ | 8/10 [00:47<00:09, 4.75s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:47<00:09, 4.75s/it, data_size=9, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 80%|████████ | 8/10 [00:51<00:09, 4.75s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 10: : 90%|█████████ | 9/10 [00:52<00:04, 4.64s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:52<00:04, 4.64s/it, data_size=10, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 90%|█████████ | 9/10 [00:56<00:04, 4.64s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [00:56<00:00, 4.58s/it, data_size=11, test_acc=0.25, train_acc=nan]Test 7: Data size 11: : 100%|██████████| 10/10 [00:56<00:00, 5.65s/it, data_size=11, test_acc=0.25, train_acc=nan]
working on model Multimodal-late-fusion-model-based-on-AlexNet with BADGE
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 2: : 0%| | 0/10 [00:11<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]Test 0: Data size 2: : 0%| | 0/10 [00:11<?, ?it/s, data_size=2, test_acc=0.25, train_acc=nan]
Traceback (most recent call last):
File "/home/agaut/CS229-Project/experiments/experiment.py", line 170, in run_experiments
self.tester.test_model(curr_model)
File "/home/agaut/CS229-Project/test_framework/tester.py", line 165, in test_model
for mode_ind in range(len(train_x))
File "/home/agaut/CS229-Project/test_framework/tester.py", line 165, in <listcomp>
for mode_ind in range(len(train_x))
IndexError: arrays used as indices must be of integer (or boolean) type
Got exception arrays used as indices must be of integer (or boolean) type for model BADGE with stack trace:
None
working on model Multimodal-middle-fusion-model-based-on-AlexNet with RANDOM
0%| | 0/10 [00:00<?, ?it/s]Test 0: 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 208: : 0%| | 0/10 [00:00<?, ?it/s]Test 0: Data size 208: : 0%| | 0/10 [00:26<?, ?it/s, data_size=208, test_acc=0.708, train_acc=0.696]Test 0: Data size 208: : 10%|█ | 1/10 [00:26<03:55, 26.16s/it, data_size=208, test_acc=0.708, train_acc=0.696]Test 0: Data size 240: : 10%|█ | 1/10 [00:26<03:55, 26.16s/it, data_size=208, test_acc=0.708, train_acc=0.696]Test 0: Data size 240: : 10%|█ | 1/10 [00:54<03:55, 26.16s/it, data_size=240, test_acc=0.707, train_acc=0.703]Test 0: Data size 240: : 20%|██ | 2/10 [00:54<03:41, 27.71s/it, data_size=240, test_acc=0.707, train_acc=0.703]Test 0: Data size 272: : 20%|██ | 2/10 [00:54<03:41, 27.71s/it, data_size=240, test_acc=0.707, train_acc=0.703]Test 0: Data size 272: : 20%|██ | 2/10 [01:27<03:41, 27.71s/it, data_size=272, test_acc=0.757, train_acc=0.745]Test 0: Data size 272: : 30%|███ | 3/10 [01:27<03:29, 29.91s/it, data_size=272, test_acc=0.757, train_acc=0.745]Test 0: Data size 304: : 30%|███ | 3/10 [01:27<03:29, 29.91s/it, data_size=272, test_acc=0.757, train_acc=0.745]Test 0: Data size 304: : 30%|███ | 3/10 [02:02<03:29, 29.91s/it, data_size=304, test_acc=0.768, train_acc=0.812]Test 0: Data size 304: : 40%|████ | 4/10 [02:02<03:11, 31.98s/it, data_size=304, test_acc=0.768, train_acc=0.812]Test 0: Data size 336: : 40%|████ | 4/10 [02:02<03:11, 31.98s/it, data_size=304, test_acc=0.768, train_acc=0.812]