KerasClassifier TypeError：__call__() 在 cross_val

Question

我尝试在我的模型上进行 cross_val_score 并收到以下错误：Traceback (most recent call last):  File "/home/dinhnha1402/.local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.py", line 210, in fit    return super(KerasClassifier, self).fit(x, y, **kwargs)  File "/home/dinhnha1402/.local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.py", line 139, in fit    **self.filter_sk_params(self.build_fn.__call__))TypeError: __call__() takes exactly 2 arguments (1 given)这是我的模型：model = Sequential()model.add(LSTM(int(128), input_shape=(timesteps, int(128)),return_sequences=False))model.add(Dropout(0.2))model.add(Dense(20, activation='relu', input_shape=(128,),kernel_initializer=initializers.glorot_uniform(seed=0)))model.add(Dropout(0.2))model.add(Dense(20, activation='softmax'))model.add(Dense(2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])### fit modelmodel.fit(X_train, Y_train, batch_size=batch_size, epochs= epochs, verbose=1, validation_data=(X_test, Y_test))####Applying K-fold cross validationclassifier = KerasClassifier(build_fn=binary_classify_lstm_fc_model(), batch_size=10, epochs=100, verbose=0)accuracies = cross_val_score(estimator= classifier, X = X_train, y = Y_train, cv=10, scoring="accuracy")#n_jobs= -1print(accuracies)我在任何地方都找不到这个错误（在谷歌上）。有没有人对如何解决这个问题有任何想法？

Answer 1

只需将损失从 categorical_crossentropy 更改为 mean_squared_error。

Answer 2

只需删除“（）”

像那样：

分类器 = KerasClassifier（build_fn=binary_classify_lstm_fc_model（），batch_size=10，epochs=100，verbose=0）

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分类器 = KerasClassifier（build_fn=binary_classify_lstm_fc_model，batch_size=10，epochs=100，verbose=0）