如何修复ValueError：分类度量无法处理模型的多类和多标签指示器目标的混合？

Question

我已经为多类分类创建了一个模型，其中输出变量有6个类。当我试图获得准确度分数时，我遇到了一个错误。我尝试过其他答案，但答案没有帮助

代码

#Converting Target Variable to Numeric
lang = {'US':1, 'UK':2, 'GE':3, 'IT':4, 'FR':5, 'ES':6} 
df.language = [lang[item] for item in df.language] 

#Creating Input Features and Target Variables
X= df.iloc[:,1:13]
y= df.iloc[:,0]

#Standardizing the Input Features
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X = scaler.fit_transform(X)

#Train Test Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

#Model
model = Sequential()

model.add(Dense(12, activation='relu', kernel_initializer='random_normal', input_dim=12))
model.add(Dense(10, activation='relu', kernel_initializer='random_normal'))
model.add(Dense(8, activation='relu', kernel_initializer='random_normal'))

#Output Layer
model.add(Dense(7, activation = 'softmax', kernel_initializer='random_normal'))

#Compiling the neural network
model.compile(optimizer ='adam',loss='sparse_categorical_crossentropy', metrics =['accuracy'])

#Fitting the data to the training dataset
model.fit(X_train,y_train, batch_size=5, epochs=100)

#Make predictions
pred_train = model.predict(X_train)
pred_test = model.predict(X_test)  
   
print('Train Accuracy = ',accuracy_score(y_train,pred_train.round()))
print('Test Accuracy = ',accuracy_score(y_test,pred_test.round()))

错误

ValueError: Classification metrics can't handle a mix of multiclass and multilabel-indicator targets

变量持有的值 我正在添加所需变量所持有的值。我认为我接收的输出变量的数量不正确，因为1个值有多个输出

y\u列车

101    4
250    1
130    2
277    1
157    2
      ..
18     6
47     5
180    1
131    2
104    4

pred_train

array([[0.13525778, 0.15400752, 0.14303789, ..., 0.14364597, 0.14196989,
        0.14313765],
       ...,
       [0.13389133, 0.15622397, 0.14272076, ..., 0.14345258, 0.142379  ,
        0.14322434]], dtype=float32)

y_检验

57     5
283    1
162    2
237    1
107    4
      ..
182    1
173    1
75     3
251    1
55     5

预测试

array([[0.13440262, 0.15538406, 0.14284912, 0.13841757, 0.14352694,
        0.14221355, 0.14320615],
       .....,
       [0.13503768, 0.1543666 , 0.14298101, 0.13881107, 0.14361957,
        0.14203095, 0.14315312]], dtype=float32)

Answer 1

predict返回样本属于每个类的概率，但是accuracy_score需要类标签。您必须从预测中获取类标签。使用

accuracy_score(y, np.argmax(pred_train, axis=1))

np.argmax返回概率最高的类的标签，由于您对一批数据而不是单个样本进行了预测，因此必须使用axis=1