我为什么得到“支持的目标类型为:(“二进制"，“多类").取而代之的是“连续的".错误?

问题描述

我正在编写此代码，并不断获取支持的目标类型:("binary"，"multiclass").取而代之的是连续的".错误，无论我尝试什么.您在我的代码中看到问题了吗?

I am writing this code and keep getting the Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead. error no matter what I try. Do you see the problem within my code?

df = pd.read_csv('drain.csv')
values = df.values
seed = 7
numpy.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 model
model = KerasClassifier(build_fn=create_model, epochs=10, batch_size=10, verbose=0)
# evaluate using 10-fold cross validation
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())

推荐答案

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

You need to convert your Y variables to binary, as specified here :https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py

# convert class vectors to binary class matrices
y_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))

似乎您忘记了转换为绝对步骤.

Seems like you forgot the conversion to categorical step.

这篇关于我为什么得到“支持的目标类型为:(“二进制"，“多类").取而代之的是“连续的".错误?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！