Python 3.x 人工神经网络的多类分类

我在Python中使用ANN进行多类分类（12个类）。然而，我得到了错误。以下是代码片段：

import keras
from keras.models import Sequential
from keras.layers import Dense

# Initialising the ANN
# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu', input_dim = 4))

# Adding the second hidden layer
classifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(units = 13, kernel_initializer = 'uniform', activation = 'softmax'))

# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])

# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size =200 , epochs = 100)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

   # Making the Confusion Matrix
   from sklearn.metrics import confusion_matrix
   cm = confusion_matrix(y_test, y_pred)

程序一直运行，直到运行神经代码，还找到了y_pred。之后，我得到了这个错误，即混淆矩阵没有形成

错误：

ValueError:分类指标无法处理多类和连续多输出目标的混合

from sklearn.metrics import confusion_matrix

y_pred = classifier.predict(X_test)

predictions = np.argmax(y_pred, axis=-1) 

cm = confusion_matrix(y_test, y_pred)

我希望它能解决你的问题

from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import LabelEncoder

y_pred = classifier.predict(X_test) 
predictions = np.argmax(y_pred, axis=-1) 

label_encoder = LabelEncoder().fit(y_test)
label_y = label_encoder.transform(y_test)

cm = confusion_matrix(label_y, predictions)