我正在尝试使用神经网络来预测房价。以下是数据集顶部的样貌：
Price Beds SqFt Built Garage FullBaths HalfBaths LotSqFt 485000 3 2336 2004 2 2.0 1.0 2178.0 430000 4 2106 2005 2 2.0 1.0 2178.0 445000 3 1410 1999 1 2.0 0.0 3049.0...这是我编写神经网络的代码（使用Python的Keras）。
dataset = df.valuesX = dataset[:,1:8]Y = dataset[:,0]from sklearn import preprocessingfrom sklearn.model_selection import train_test_splitmin_max_scaler = preprocessing.MinMaxScaler()X_scale = min_max_scaler.fit_transform(X)X_scaleX_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X_scale, Y, test_size=0.3)X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5)print(X_train.shape, X_val.shape, X_test.shape, Y_train.shape, Y_val.shape, Y_test.shape)from keras.models import Sequentialfrom keras.layers import Densemodel = Sequential( Dense(32, activation='relu', input_shape=(7,)), Dense(1, activation='relu'))model.compile(optimizer='sgd', loss='mse', metrics=['mean_squared_error'])hist = model.fit(X_train, Y_train, batch_size=32, epochs=100, validation_data=(X_val, Y_val)) #Error here!model.evaluate(X_test, Y_test)[1] #Same Error here!在运行hist =行和model.evaluate行时，我得到了相同的错误。以下是错误信息：
TypeError Traceback (most recent call last)<ipython-input-19-522714a352ba> in <module>----> 1 hist = model.fit(X_train, Y_train, 2 batch_size=32, epochs=100, 3 validation_data=(X_val, Y_val))~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs) 106 def _method_wrapper(self, *args, **kwargs): 107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access--> 108 return method(self, *args, **kwargs) 109 110 # Running inside `run_distribute_coordinator` already....TypeError: in user code:...name_scope += '/' TypeError: unsupported operand type(s) for +=: 'Dense' and 'str'我不确定为什么会发生这种情况，因为当我在原始数据框上运行df.dtypes时，所有列都是整数或浮点数。
回答：
简单的修复！你似乎在模型构建中缺少了一个硬括号。试试这个：
model = Sequential([ Dense(32, activation='relu', input_shape=(7,)), Dense(1, activation='relu'),])希望这对你有帮助！如果你有更多问题，请告诉我！
