Я использую autokeras (версия 0.3.6), чтобы найти лучшую модель для моей проблемы классификации.Процесс поиска может занять много времени, и, предоставив достаточно времени, мы сможем получить лучшие результаты.Проблема в том, что он иногда падает.Я использую Ubuntu 16.04 LTS с python 3.6
Я использую следующие коды:
from autokeras import ImageClassifier
import autokeras
clf.fit(trdata_X, trdata_Y, time_limit= 1 * 60)
trdata_X, trdata_Y - данные, которые я предоставил в виде массивов.
Я получаю сообщения об ошибках типа
self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [64, 192, 1, 1], expected input[128, 256, 32, 32] to have 192 channels, but got 256 channels instead
. Вот полные журналы:
Preprocessing the images.
Preprocessing finished.
Initializing search.
Initialization finished.
+----------------------------------------------+
| Training model 0 |
+----------------------------------------------+
No loss decrease after 5 epochs.
Saving model.
+--------------------------------------------------------------------------+
| Model ID | Loss | Metric Value |
+--------------------------------------------------------------------------+
| 0 | 0.6538878202438354 | 0.6875 |
+--------------------------------------------------------------------------+
+----------------------------------------------+
| Training model 1 |
+----------------------------------------------+
Epoch-1, Current Metric - 0: 0%| | 0/2 [00:00<?, ? batch/s]
Current model size is too big. Discontinuing training this model to search for other models.
+----------------------------------------------+
| Training model 2 |
+----------------------------------------------+
Epoch-1, Current Metric - 0: 0%| | 0/2 [00:00<?, ? batch/s]
Current model size is too big. Discontinuing training this model to search for other models.
+----------------------------------------------+
| Training model 3 |
+----------------------------------------------+
No loss decrease after 5 epochs.
Saving model.
+--------------------------------------------------------------------------+
| Model ID | Loss | Metric Value |
+--------------------------------------------------------------------------+
| 3 | 0.6291704177856445 | 0.6875 |
+--------------------------------------------------------------------------+
+----------------------------------------------+
| Training model 4 |
+----------------------------------------------+
No loss decrease after 5 epochs.
Saving model.
+--------------------------------------------------------------------------+
| Model ID | Loss | Metric Value |
+--------------------------------------------------------------------------+
| 4 | 0.6316376447677612 | 0.6875 |
+--------------------------------------------------------------------------+
+----------------------------------------------+
| Training model 5 |
+----------------------------------------------+
No loss decrease after 5 epochs.
Saving model.
+--------------------------------------------------------------------------+
| Model ID | Loss | Metric Value |
+--------------------------------------------------------------------------+
| 5 | 0.62800053358078 | 0.6875 |
+--------------------------------------------------------------------------+
+----------------------------------------------+
| Training model 6 |
+----------------------------------------------+
No loss decrease after 5 epochs.
Saving model.
+--------------------------------------------------------------------------+
| Model ID | Loss | Metric Value |
+--------------------------------------------------------------------------+
| 6 | 0.6313011765480041 | 0.6875 |
+--------------------------------------------------------------------------+
+----------------------------------------------+
| Training model 7 |
+----------------------------------------------+
No loss decrease after 5 epochs.
Saving model.
+--------------------------------------------------------------------------+
| Model ID | Loss | Metric Value |
+--------------------------------------------------------------------------+
| 7 | 0.632081127166748 | 0.6875 |
+--------------------------------------------------------------------------+
+----------------------------------------------+
| Training model 8 |
+----------------------------------------------+
Epoch-1, Current Metric - 0: 0%| | 0/2 [00:00<?, ? batch/s]Process ForkProcess-9:
Traceback (most recent call last):
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/autokeras/search.py", line 350, in train
raise e
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/autokeras/search.py", line 343, in train
verbose=verbose).train_model(**trainer_args)
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/autokeras/nn/model_trainer.py", line 137, in train_model
self._train()
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/autokeras/nn/model_trainer.py", line 173, in _train
outputs = self.model(inputs)
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
result = self.forward(*input, **kwargs)
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/autokeras/nn/graph.py", line 686, in forward
temp_tensor = torch_layer(edge_input_tensor)
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
result = self.forward(*input, **kwargs)
File "/home/rapsodo/workspace_mike3352/anaconda2/envs/ali_tf_py36/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 301, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [64, 192, 1, 1], expected input[128, 256, 64, 64] to have 192 channels, but got 256 channels instead
Я пытаюсь запустить код несколько раз, чтобы получить результаты.Я буду признателен всем, кто поможет мне найти решение этой проблемы.