You now have until September 15th to submit your use case or success story to the 2022 Dataiku Frontrunner Awards!ENTER YOUR SUBMISSION

Error Using Retrain Recipe the "Deep Learning on images" Plugin

brian-walheim
Level 1
Level 1
Error Using Retrain Recipe the "Deep Learning on images" Plugin

Currently Running Dataiku 9.0.3 and trying to utilize the "Deep learning on images" version 2.0.2. We were able to successfully download a pretrained model however when we try to retrain the model we get the following error

Error in Python process: At line 54: <class 'dku_deeplearning_image.error_handler.DataikuPluginException'>: Unknown error Original error: <class 'ValueError'> in user code: /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:941 test_function * outputs = self.distribute_strategy.run( /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run ** return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_strategy.py:770 _call_for_each_replica fn, args, kwargs) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_strategy.py:201 _call_for_each_replica coord.join(threads) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/training/coordinator.py:389 join six.reraise(*self._exc_info_to_raise) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib/python3.6/site-packages/six.py:719 reraise raise value /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/training/coordinator.py:297 stop_on_exception yield /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_strategy.py:998 run self.main_result = self.main_fn(*self.main_args, **self.main_kwargs) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:912 test_step ** y, y_pred, sample_weight, regularization_losses=self.losses) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__ loss_value = loss_obj(y_t, y_p, sample_weight=sw) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:143 __call__ losses = self.call(y_true, y_pred) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:246 call return self.fn(y_true, y_pred, **self._fn_kwargs) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy target.shape.assert_is_compatible_with(output.shape) /home/dataiku/design/code-envs/python/plugin_deeplearning-image_managed/lib64/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (None, 55) and (None, 66) are incompatible )

Our images are stored on a local Managed Dataiku folder and our label dataset only has two columns (labels, filename). Unsure what might be causing this error.


Operating system used: Linux

0 Kudos
0 Replies