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
