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- ///////////////////////////////////////////
- // Running convGAN-majority-full on imblearn_webpage
- ///////////////////////////////////////////
- Load 'data_input/imblearn_webpage'
- from imblearn
- non empty cut in data_input/imblearn_webpage! (76 points)
- Data loaded.
- -> Shuffling data
- ### Start exercise for synthetic point generator
- ====== Step 1/5 =======
- -> Shuffling data
- -> Spliting data to slices
- ------ Step 1/5: Slice 1/5 -------
- -> Reset the GAN
- -> Train generator for synthetic samples
- Traceback (most recent call last):
- File "/benchmark/data/run_all_exercises.py", line 13, in <module>
- runExercise(dataset, None, name, generators[name])
- File "/benchmark/data/library/analysis.py", line 169, in runExercise
- avg = exercise.run(gan, data, resultsFileName=resultsFileName)
- File "/benchmark/data/library/exercise.py", line 127, in run
- self._exerciseWithDataSlice(gan, sliceData, imageFileName=imageFileName)
- File "/benchmark/data/library/exercise.py", line 170, in _exerciseWithDataSlice
- gan.train(dataSlice.train)
- File "/benchmark/data/library/generators/convGAN.py", line 101, in train
- self._rough_learning(dataSet.data1, dataSet.data0, discTrainCount)
- File "/benchmark/data/library/generators/convGAN.py", line 304, in _rough_learning
- conv_samples = generator.predict(min_batch)
- File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
- raise e.with_traceback(filtered_tb) from None
- File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 54, in quick_execute
- tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
- tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
- Detected at node 'model/tf.reshape/Reshape' defined at (most recent call last):
- File "/benchmark/data/run_all_exercises.py", line 13, in <module>
- runExercise(dataset, None, name, generators[name])
- File "/benchmark/data/library/analysis.py", line 169, in runExercise
- avg = exercise.run(gan, data, resultsFileName=resultsFileName)
- File "/benchmark/data/library/exercise.py", line 127, in run
- self._exerciseWithDataSlice(gan, sliceData, imageFileName=imageFileName)
- File "/benchmark/data/library/exercise.py", line 170, in _exerciseWithDataSlice
- gan.train(dataSlice.train)
- File "/benchmark/data/library/generators/convGAN.py", line 101, in train
- self._rough_learning(dataSet.data1, dataSet.data0, discTrainCount)
- File "/benchmark/data/library/generators/convGAN.py", line 304, in _rough_learning
- conv_samples = generator.predict(min_batch)
- File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
- return fn(*args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1982, in predict
- tmp_batch_outputs = self.predict_function(iterator)
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1801, in predict_function
- return step_function(self, iterator)
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1790, in step_function
- outputs = model.distribute_strategy.run(run_step, args=(data,))
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1783, in run_step
- outputs = model.predict_step(data)
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1751, in predict_step
- return self(x, training=False)
- File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
- return fn(*args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
- outputs = call_fn(inputs, *args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
- return fn(*args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 451, in call
- return self._run_internal_graph(
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py", line 589, in _run_internal_graph
- outputs = node.layer(*args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
- return fn(*args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
- outputs = call_fn(inputs, *args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
- return fn(*args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/layers/core/tf_op_layer.py", line 226, in _call_wrapper
- return self._call_wrapper(*args, **kwargs)
- File "/usr/local/lib/python3.8/dist-packages/keras/layers/core/tf_op_layer.py", line 261, in _call_wrapper
- result = self.function(*args, **kwargs)
- Node: 'model/tf.reshape/Reshape'
- Input to reshape is a tensor with 9600 values, but the requested shape has 90000
- [[{{node model/tf.reshape/Reshape}}]] [Op:__inference_predict_function_485]
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