我有一个回归数据集:
X_train (float64) Size = (1616, 3) -> 即3个预测变量Y_train (float64) Size = (1616, 2) -> 即2个目标变量我尝试使用函数式API进行Hyperas操作（我的主要目的是在编译时使用loss_weights选项）:
inputs1 = Input(shape=(X_train.shape[0], X_train.shape[1]))x = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(inputs1)x = Dropout({{uniform(0, 1)}})(x)x = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(x)x = Dropout({{uniform(0, 1)}})(x)x = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(x)x = Dropout({{uniform(0, 1)}})(x)if conditional({{choice(['three', 'four'])}}) == 'four': x = Dense({{choice([np.power(2,1),np.power(2,2),np.power(2,3),np.power(2,4),np.power(2,5)])}}, activation={{choice(['tanh','relu', 'sigmoid'])}})(x) x = Dropout({{uniform(0, 1)}})(x)output1 = Dense(1, activation='linear')(x)output2 = Dense(1, activation='linear')(x)model = Model(inputs = inputs1, outputs = [output1,output2])adam = keras.optimizers.Adam(lr={{choice([10**-3,10**-2, 10**-1])}})rmsprop = keras.optimizers.RMSprop(lr={{choice([10**-3,10**-2, 10**-1])}})sgd = keras.optimizers.SGD(lr={{choice([10**-3,10**-2, 10**-1])}})choiceval = {{choice(['adam', 'rmsprop','sgd'])}}if choiceval == 'adam': optimizer = adamelif choiceval == 'rmsprop': optimizer = rmspropelse: optimizer = sgdmodel.compile(loss='mae', metrics=['mae'],optimizer=optimizer, loss_weights=[0.5,0.5])earlyStopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=50, verbose=0, mode='auto')checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=2, save_best_only=True, mode='max')lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.5, cooldown=1, patience=10, min_lr=1e-4,verbose=2)callbacks_list = [earlyStopping, checkpoint, lr_reducer]history = model.fit(X_train, Y_train, batch_size={{choice([16,32,64,128])}}, epochs={{choice([20000])}}, verbose=2, validation_data=(X_val, Y_val), callbacks=callbacks_list)然而，运行时我得到了以下错误:
ValueError: Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (1616, 3)如果有人能指出这里出了什么问题，我将不胜感激。我怀疑输入（即X_train，Y_train）以及输入形状可能是问题的根源。希望能得到任何帮助。
更新
好的，确实是输入行出了问题:
我将其更改为: inputs1 = Input(shape=(X_train.shape[1],))。
然而，现在我收到了另一个错误:
ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [array([[0.19204772, 0.04878049], [0.20226056, 0. ], [0.12029842, 0.04878049], ..., [0.45188627, 0.14634146], [0.26942276, 0.02439024], [0.12942418, 0....回答：
由于您的模型有两个输出层，您需要在调用fit()方法时传递一个列表，包含两个数组作为真实目标（即y）。例如这样:
model.fit(X_train, [Y_train[:,0:1], Y_train[:,1:]], ...)