ConvGeNBenchmarkResults/data_result/convGAN-proximary-full/kaggle_creditcard.log

///////////////////////////////////////////
// Running convGAN-proxymary-full on kaggle_creditcard
///////////////////////////////////////////

Load 'data_input/kaggle_creditcard'
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 338, in _rough_learning
    gan_loss_history = GAN.fit(concat_sample, y=labels, verbose=0)
  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_2/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 338, in _rough_learning
      gan_loss_history = GAN.fit(concat_sample, y=labels, verbose=0)
    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 1384, in fit
      tmp_logs = self.train_function(iterator)
    File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1021, in train_function
      return step_function(self, iterator)
    File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1010, 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 1000, in run_step
      outputs = model.train_step(data)
    File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 859, in train_step
      y_pred = self(x, training=True)
    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/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_2/model/tf.reshape/Reshape'
Input to reshape is a tensor with 840 values, but the requested shape has 900
	 [[{{node model_2/model/tf.reshape/Reshape}}]] [Op:__inference_train_function_275092]