我正在尝试使用keras-tuner优化1D CNN模型的超参数。在尝试获得最佳参数时，一切看起来都很完美。但当我尝试打印tuner.get_best_models（）[0].summary（）时，它给我以下错误：
raise ValueError（“图形%s和%s不兼容”%（自身，其他））ValueError：形状（78400，10）和（235680，10）不兼容

def build_model(hp):  # random search passes this hyperparameter() object
    nSNP = X_train.shape[1]
    kernel_size = 3  # stride between convolutions

    model = keras.models.Sequential()

    model.add(Conv1D(hp.Int('input_units',
                            min_value=32,
                            max_value=256,
                            step=32), kernel_size, input_shape=(nSNP, 1)))

    model.add(Activation('relu'))
    model.add(MaxPooling1D(pool_size=2))

    for i in range(hp.Int('n_layers', 1, 4)):  # adding variation of layers.
        model.add(Conv1D(hp.Int(f'conv_{i}_units',
                                min_value=32,
                                max_value=256,
                                step=32), kernel_size))
        model.add(Activation('relu'))

    model.add(Flatten())
    model.add(Dense(10, activation='linear'))
    model.add(Dense(1))
    # model.add(Activation(hp.Choice('Activation', values=['relu', 'sigmoid', 'linear'])))

    # Tune the learning rate for the optimizer
    # Choose an optimal value from 0.01, 0.001, or 0.0001
    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4, 1e-5,
                                                          1e-6, 1e-7])
    # hp_optimizer = hp.Choice('optimizer', values=['SGD', 'Adam'])
    adm = keras.optimizers.Adam(learning_rate=hp_learning_rate)
    model.compile(optimizer=adm, loss='mse')

    return model

tuner = keras_tuner.Hyperband(build_model,
                             objective='val_loss',
                             max_epochs=10,
                             factor=3)
stop_early = tensorflow.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)
    tuner.search(x=X2_train,
                 y=y_train,
                 verbose=2,     
                 epochs=50,
                 batch_size=64,
                 validation_data=(X2_valid, y_valid),
                 callbacks=[stop_early])
    
    # Get the optimal hyperparameters
    best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
    
    print(f"""
    The hyper-parameter search is complete.
    The optimal number of units in the first densely-connected
    layer is {best_hps.get('input_units')} and the optimal learning rate for the optimizer
    is {best_hps.get('learning_rate')}.
    """)
    
    print(tuner.get_best_hyperparameters()[0].values)
    tuner.get_best_models()[0].summary()

输入形状，训练数据形状如下图：

X shape (195, 4915)
Y shape (195, 3)
X_train.shape (156, 4915) 
y_train.shape (156,)
X_valid.shape (39, 4915) 
y_valid.shape (39,)

tuner.get_best_models（）[0].summary（）应该打印以下模型摘要：

我怎样才能解决这个错误呢？如果有人帮助我解决这个问题，我将不胜感激。