我在编写代码时，无论尝试什么方法，都会收到支持的目标类型为：(‘binary’, ‘multiclass’)。但得到的是’continuous’的错误。你能看出我的代码中有什么问题吗？

df = pd.read_csv('drain.csv')values = df.valuesseed = 7numpy.random.seed(seed)X = df.iloc[:,:2]Y = df.iloc[:,2:]def create_model():# create model    model = Sequential()    model.add(Dense(12, input_dim=8, activation='relu'))    model.add(Dense(8, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    # Compile model    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])    return modelmodel = KerasClassifier(build_fn=create_model, epochs=10, batch_size=10, verbose=0)# evaluate using 10-fold cross validationkfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)results = cross_val_score(model, X, Y, cv=kfold)print(results.mean())

回答：

你需要将你的Y变量转换为二进制类型，如下所示：https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py

# convert class vectors to binary class matricesy_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)

然后

history = model.fit(x_train, y_train,                    batch_size=batch_size,                    epochs=epochs,                    verbose=1,                    validation_data=(x_test, y_test))

看起来你忘记了转换为分类数据的步骤。