Hi, bin absoluter Neuling und bekomme beim ausführen meines Codes immer die oben genannte Fehlermeldung. Beim googlen stoße ich immer wieder auf den Heilbringer np.argmax, aber dies löst mein Problem anscheinend nicht.
Anbei mal paar Auszüge des Codes + Fehlermeldung.
Code: Alles auswählen
class DataManager(object):
def __init__(self):
self.train = DataLoader("xyz/alltrain").load() # path for train dataset
self.test = DataLoader("xyz/alltest").load() # path for test dataset
data = DataManager()
input_train = data.train.sampledata
input_train = input_train.reshape(-1, 375, 1)
target_train = data.train.labeldata
target_train = target_train.reshape(-1, 1).astype('int32')
target_train = one_hot(target_train[:, -1], no_classes)
input_test = data.test.sampledata
input_test = input_test.reshape(-1, 375, 1)
target_test = data.test.labeldata
target_test = target_test.reshape(-1, 1).astype('int32')
target_test = one_hot(target_test[:, -1], no_classes)
print(input_train.shape)
model = models.Sequential()
pred_test = model.predict(input_test)
pred_test = np.argmax(pred_test, axis =1)
print("pred_test")
print(pred_test[:10])
print("pred_test-Shape")
print(pred_test.shape)
print("target_test")
print(target_test[:10])
print("target_test_shape")
print(target_test.shape)
f1score = sklearn.metrics.f1_score(target_test, pred_test)
print('f1score:' + {f1score})
Fehlermeldungen :
Traceback (most recent call last):
File "C:/Users/derg/PycharmProjects/ecgcnnmulticlass/main.py", line 160, in <module>
f1score = sklearn.metrics.f1_score(target_test, pred_test)
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\metrics\_classification.py", line 1044, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\metrics\_classification.py", line 1168, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\metrics\_classification.py", line 1433, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\metrics\_classification.py", line 1250, in _check_set_wise_labels
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\Users\derg\anaconda3\envs\Tensorflow2\lib\site-packages\sklearn\metrics\_classification.py", line 90, in _check_targets
raise ValueError("Classification metrics can't handle a mix of {0} "
ValueError: Classification metrics can't handle a mix of multilabel-indicator and multiclass targets