ValueError：`predict_function` 的意外结果（空批处理输出）。请使用`Model.compile（...，run_eagerly=True）`，

我尝试使用 GridSearchCV（） 但我也遇到了错误，所以我编写了一个非常耗时的代码来返回我想要评估我的模型的指标。这里是 ：

def df_to_new(df,window_size): 
    df_as_np = df.to_numpy()
    X = []
    y = []
    for i in range(len(df_as_np)-window_size):
        row = [[a] for a in df_as_np[i:i+window_size]]
        X.append(row)
        label = df_as_np[i+window_size]
        y.append(label)
        
    return np.array(X),np.array(y)  

from tensorflow.keras.models import load_model
import os
checkpointpath = 'C:\\Users\\USER\\trainingFORLOOP_daily/cp.ckt'
cp = ModelCheckpoint(checkpointpath, save_best_only=True,verbose=1)     
EPOCH = [30,150,300]
learningRates = [0.0001,0.00001]
batchSize = [15,20,40]
win_size = [5,15,25]
dropout_rate = 0.2
num_features = 1
for i in learningRates:
    for j in EPOCH:
        for k in batchSize:
            for l in win_size:
                X,y = df_to_new(Ac13,l)

                #Split the data
                perc_train = 0.8
                limit_train = int(np.floor(len(Ac13)*perc_train))
                xtrain,ytrain = X[:limit_train],y[:limit_train]
                xval,yval = X[limit_train:],y[limit_train:]
                
                #create the model
                model1 = Sequential() 
                model1.add(InputLayer((l,1))) 
                model1.add(LSTM(128))
                model1.add(Dropout(dropout_rate))
                model1.add(Dense(86,'relu'))
                model1.add(Dropout(dropout_rate))
                model1.add(Dense(1,'linear'))
                model1.summary()
                model1.compile(loss=MeanSquaredError(),optimizer = 
                       Adam(learning_rate=i),
                       metrics=[RootMeanSquaredError()],run_eagerly=True)
                model1.fit(xtrain,ytrain,validation_data=(xval,yval),batch_size=k,epochs=j,callbacks=[cp],shuffle=False)
                model1.save("my_model")
                model1 = load_model("my_model")
                           
                train_predictions = model1.predict(xtrain).flatten() 
                train_results = pd.DataFrame(data={'TrainPredictions':train_predictions,'Actual values':ytrain})
                train_results
                scale = len(train_predictions)
                val_predictions = model1.predict(xval).flatten()
                val_results = pd.DataFrame(data='ValidatePredictions':val_predictions,'Validation values':yval}) 

                

我收到以下错误（完整回溯）：

    ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_19052/292377201.py in <module>
     51                 plt.legend(bbox_to_anchor =(0.75, 1.15), ncol = 2)
     52                 plt.show()
---> 53                 val_predictions = model1.predict(xval).flatten() # flatten() removes the brackets inside the data
     54                 val_results = pd.DataFrame(data={'Validate Predictions':val_predictions,'Validation values':yval}) #yval are the actual values
     55                 val_results

~\anaconda3\envs\tf-gpu-cuda8\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

~\anaconda3\envs\tf-gpu-cuda8\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1995             callbacks.on_predict_batch_end(end_step, {'outputs': batch_outputs})
   1996       if batch_outputs is None:
-> 1997         raise ValueError('Unexpected result of `predict_function` '
   1998                          '(Empty batch_outputs). Please use '
   1999                          '`Model.compile(..., run_eagerly=True)`, or '

ValueError: Unexpected result of `predict_function` (Empty batch_outputs). Please use `Model.compile(..., run_eagerly=True)`, or `tf.config.run_functions_eagerly(True)` for more information of where went wrong, or file a issue/bug to `tf.keras`.

有什么建议吗？我采用了相同的方法，但使用的是每小时数据，而且效果很好，没有任何错误。小时码和日码（这个）一样，唯一不同的是把小时码中的数据在一天内相加，得到日码数据。