python — ошибка с нейронной сетью Мариана/Теано

Я столкнулся с проблемой, когда я запускаю свой тренер, и я не могу понять причину.

Мои входные данные имеют размерность 42, и мой вывод должен быть одним значением из 4.

Это форма моего тренировочного и тестового набора:

Training set: 
input = (1152, 42) target = (1152,)

Training set: input = (1152, 42) target = (1152,)
Test set: input = (384, 42) target = (384,)

Это построение моей сети:

ls = MS.GradientDescent(lr=0.01)
cost = MC.CrossEntropy()

i = ML.Input(42, name='inp')
h = ML.Hidden(23, activation=MA.Sigmoid(), initializations=[MI.GlorotTanhInit()], name="hid")
o = ML.SoftmaxClassifier(4, learningScenario=ls, costObject=cost, name="out")

mlp = i > h > o

И это построение наборов данных, тренеров и регистраторов:

trainData = MDM.RandomSeries(distances = train_set[0], next_state = train_set[1])
trainMaps = MDM.DatasetMapper()
trainMaps.mapInput(i, trainData.distances)
trainMaps.mapOutput(o, trainData.next_state)

testData = MDM.RandomSeries(distances = test_set[0], next_state = test_set[1])
testMaps = MDM.DatasetMapper()
testMaps.mapInput(i, testData.distances)
testMaps.mapOutput(o, testData.next_state)

earlyStop = MSTOP.GeometricEarlyStopping(testMaps, patience=100, patienceIncreaseFactor=1.1, significantImprovement=0.00001, outputFunction="score", outputLayer=o)
epochWall = MSTOP.EpochWall(1000)

trainer = MT.DefaultTrainer(
        trainMaps=trainMaps,
        testMaps=testMaps,
        validationMaps=None,
        stopCriteria=[earlyStop, epochWall],
        testFunctionName="testAndAccuracy",
        trainMiniBatchSize=MT.DefaultTrainer.ALL_SET,
        saveIfMurdered=False
    )

recorder = MREC.GGPlot2("MLP", whenToSave = [MREC.SaveMin("test", o.name, "score")], printRate=1, writeRate=1)

trainer.start("MLP", mlp, recorder = recorder)

Но выдается следующая ошибка:

Traceback (most recent call last):
  File "nn-mariana.py", line 82, in <module>
    trainer.start("MLP", mlp, recorder = recorder)
  File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 226, in start
    Trainer_ABC.start( self, runName, model, recorder, trainingOrder, moreHyperParameters )
  File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 110, in start
    return self.run(runName, model, recorder, *args, **kwargs)
  File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 410, in run
    outputLayers
  File "SUPRESSED/Mariana/Mariana/training/trainers.py", line 269, in _trainTest
    res = modelFct(output, **kwargs)
  File "SUPRESSED/Mariana/Mariana/network.py", line 47, in __call__
    return self.callTheanoFct(outputLayer, **kwargs)
  File "SUPRESSED/Mariana/Mariana/network.py", line 44, in callTheanoFct
    return self.outputFcts[ol](**kwargs)
  File "SUPRESSED/Mariana/Mariana/wrappers.py", line 110, in __call__
    return self.run(**kwargs)
  File "SUPRESSED/Mariana/Mariana/wrappers.py", line 102, in run
    fres = iter(self.theano_fct(*self.fctInputs.values()))
  File "SUPRESSED/Theano/theano/compile/function_module.py", line 871, in __call__
    storage_map=getattr(self.fn, 'storage_map', None))
  File "SUPRESSED/Theano/theano/gof/link.py", line 314, in raise_with_op
    reraise(exc_type, exc_value, exc_trace)
  File "SUPRESSED/Theano/theano/compile/function_module.py", line 859, in __call__
    outputs = self.fn()
ValueError: Input dimension mis-match. (input[0].shape[1] = 1152, input[1].shape[1] = 4)
Apply node that caused the error: Elemwise{Composite{((i0 * i1) + (i2 * log(i3)))}}[(0, 1)](InplaceDimShuffle{x,0}.0, LogSoftmax.0, Elemwise{sub,no_inplace}.0, Elemwise{sub,no_inplace}.0)
Toposort index: 18
Inputs types: [TensorType(int32, row), TensorType(float64, matrix), TensorType(int32, row), TensorType(float64, matrix)]
Inputs shapes: [(1, 1152), (1152, 4), (1, 1152), (1152, 4)]
Inputs strides: [(4608, 4), (32, 8), (4608, 4), (32, 8)]
Inputs values: ['not shown', 'not shown', 'not shown', 'not shown']
Outputs clients: [[Sum{axis=[1], acc_dtype=float64}(Elemwise{Composite{((i0 * i1) + (i2 * log(i3)))}}[(0, 1)].0)]]

Версии:

Mariana (1.0.1rc1, /media/guilhermevrs/Data/Documentos/Academico/TCC-code/Mariana)
Theano (0.8.0.dev0, SUPRESSED/Theano)

Этот код был создан на основе учебного кода из примера mnist.

Не могли бы вы помочь мне понять, что происходит?

заранее спасибо