Keras 的验证均方误差输出总是与 1 非常相似，有什么原因吗？谢谢你。我所有的训练结果如下：

155/155 [==============================] - 0s - loss: 6062.6136 - mean_absolute_error: 0.8344 - mean_squared_error: 1.0271 - val_loss: 0.8252 - val_mean_absolute_error: 0.8252 - val_mean_squared_error: 1.0164
Epoch 29/1000
155/155 [==============================] - 0s - loss: 5870.5280 - mean_absolute_error: 0.8324 - mean_squared_error: 1.0211 - val_loss: 0.8246 - val_mean_absolute_error: 0.8246 - val_mean_squared_error: 1.0130
Epoch 30/1000
155/155 [==============================] - 0s - loss: 5668.5083 - mean_absolute_error: 0.8311 - mean_squared_error: 1.0134 - val_loss: 0.8244 - val_mean_absolute_error: 0.8244 - val_mean_squared_error: 1.0106
Epoch 31/1000
155/155 [==============================] - 0s - loss: 5530.8119 - mean_absolute_error: 0.8288 - mean_squared_error: 1.0115 - val_loss: 0.8243 - val_mean_absolute_error: 0.8243 - val_mean_squared_error: 1.0089
Epoch 32/1000
155/155 [==============================] - 0s - loss: 5222.6773 - mean_absolute_error: 0.8283 - mean_squared_error: 1.0119 - val_loss: 0.8245 - val_mean_absolute_error: 0.8245 - val_mean_squared_error: 1.0071
Epoch 33/1000
155/155 [==============================] - 0s - loss: 5090.0273 - mean_absolute_error: 0.8273 - mean_squared_error: 1.0078 - val_loss: 0.8247 - val_mean_absolute_error: 0.8247 - val_mean_squared_error: 1.0060
Epoch 34/1000
155/155 [==============================] - 0s - loss: 4878.2420 - mean_absolute_error: 0.8272 - mean_squared_error: 1.0093 - val_loss: 0.8245 - val_mean_absolute_error: 0.8245 - val_mean_squared_error: 1.0046

注意：我已经使用 sklearn 标准化来标准化我的输入和输出：

from sklearn import preprocessing
    X_scaler = preprocessing.StandardScaler().fit(X_list_total)
    X_list_total_standardized = X_scaler.transform(X_list_total)
    Y_scaler = preprocessing.StandardScaler().fit(Y_list_total)
    Y_list_total_standardized = Y_scaler.transform(Y_list_total)

这是否意味着根本无法从数据中学到任何东西？