regression - 即使 val_loss 较低，我使用 LSTM 的回归 NN 的输出也是错误的

Question

该模型

我目前正在研究一堆 LSTM 并试图解决回归问题。该模型的架构如下：

comp_lstm = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(64, return_sequences = True),
    tf.keras.layers.LSTM(64, return_sequences = True),
    tf.keras.layers.LSTM(64),
    tf.keras.layers.Dense(units=128),
    tf.keras.layers.Dense(units=64),
    tf.keras.layers.Dense(units=32),
    tf.keras.layers.Dense(units=1)
])

comp_lstm.compile(optimizer='adam', loss='mae')

当我训练模型时，它显示了一些不错的损失和 val_loss 数据：

Epoch 6/20
200/200 [==============================] - 463s 2s/step - loss: 1.3793 - val_loss: 1.3578
Epoch 7/20
200/200 [==============================] - 461s 2s/step - loss: 1.3791 - val_loss: 1.3602

现在我运行代码以使用以下代码检查输出：

idx = np.random.randint(len(val_X))
sample_X, sample_y = [[val_X[idx,:]]], [[val_y[idx]]]
test = tf.data.Dataset.from_tensor_slices(([sample_X], [sample_y]))
prediction = comp_lstm.predict(test)
print(f'The actual value was {sample_y} and the model predicted {prediction}')

输出是：

The actual value was [[21.3]] and the model predicted [[2.7479606]]

接下来的几次我运行它，我得到了值：

The actual value was [[23.1]] and the model predicted [[0.8445232]]
The actual value was [[21.2]] and the model predicted [[2.5449793]]
The actual value was [[22.5]] and the model predicted [[1.2662419]]

我不确定为什么会这样。val_loss 非常低，但输出却大不相同。

---

数据争吵

为了获取等而进行的数据争吵train_X如下val_X所示：

hist2 = 128 

features2 = np.array(list(map(list,[df["scaled_temp"].shift(x) for x in range(1, hist2+1)]))).T.tolist()
df_feat2 = pd.DataFrame([pd.Series(x) for x in features2], index = df.index)
df_trans2 = df.join(df_feat2).drop(columns=['scaled_temp']).iloc[hist2:]
df_trans2 = df_trans2.sample(frac=1)
target = df_trans2['T (degC)'].values
feat2 = df_trans2.drop(columns = ['T (degC)']).values

的形状feat2是(44435, 128)，而 的形状target是(44435,)

作为该列的数据框df["scaled_temp"]如下所示（已使用标准缩放器进行了缩放）：

Date Time
2020-04-23T21:14:07.546476Z   -0.377905
2020-04-23T21:17:32.406111Z   -0.377905
2020-04-23T21:17:52.670373Z   -0.377905
2020-04-23T21:18:55.010392Z   -0.377905
2020-04-23T21:19:57.327291Z   -0.377905
                                 ...   
2020-06-08T09:13:06.718934Z   -0.889968
2020-06-08T09:14:09.170193Z   -0.889968
2020-06-08T09:15:11.634954Z   -0.889968
2020-06-08T09:16:14.087139Z   -0.889968
2020-06-08T09:17:16.549216Z   -0.889968
Name: scaled_temp, Length: 44563, dtype: float64

df['T (degC)'] 的数据框如下所示：

Date Time
2020-05-09T07:30:30.621001Z    24.0
2020-05-11T15:56:30.856851Z    21.3
2020-05-27T05:02:09.407266Z    28.3
2020-05-02T09:33:03.219329Z    20.5
2020-05-31T03:20:04.326902Z    22.4
                               ... 
2020-05-31T01:47:45.982819Z    23.1
2020-05-27T08:03:21.456607Z    27.2
2020-05-04T21:58:36.652251Z    20.9
2020-05-17T18:42:39.681050Z    22.5
2020-05-04T22:07:58.350329Z    21.1
Name: T (degC), Length: 44435, dtype: float64

数据集创建过程如下：

train_X, val_X = feat2[:int(feat2.shape[0]*0.95), :], feat2[int(feat2.shape[0]*0.95):, :]
train_y, val_y = target[:int(target.shape[0]*0.95)], target[int(target.shape[0]*0.95):]
train = tf.data.Dataset.from_tensor_slices(([train_X], [train_y])).batch(BATCH_SIZE).repeat()
val = tf.data.Dataset.from_tensor_slices(([val_X], [val_y])).batch(BATCH_SIZE).repeat()

所以我不确定为什么会这样。

标签： regressionlstmtensorflow2.0