我声明我完全不熟悉神经网络，这是我第一次尝试开发神经网络。 问题在于根据上个月预测一周的污染预报。 具有 15 个特征的非结构化数据是： Start data
要预测的数据是'gas'，下周一共168小时，是一周的小时数。
MinMaxScaler(feature_range (0,1)) 应用于数据。然后将数据拆分为训练数据和测试数据。由于只有一年的每小时测量可用，因此数据以 672 个每小时样本的系列重新采样，每个样本从一年中的每一天午夜开始。因此，从大约 8000 个开始的每小时调查中，获得了大约 600 个系列的 672 个样本。
'date' 从初始数据中移除，train_x 和 train_y 的形式为：
Shape of train_x and train_y
在 train_x[0] 中，数据集的前 4 周有 672 小时读数，包括所有特征，包括 'gas'。
另一方面，在 train_y [0] 中，从月份在 train_x [0] 结束时开始的下一周有 168 小时读数。
Train_X[0] where column 0 is 'gas' 和 Train_y[0] with only gas column for the next week after train_x[0]
火车 X 形状 = (631, 672, 14)
训练 Y 形 = (631, 168, 1)
以这种方式组织数据后（如果有错请告诉我），我构建的神经网络如下：
![Train_X[0] where column 0 is 'gas'](https://i.stack.imgur.com/DSO0M.png){kind=link}
![Train_y[0] with only gas column for the next week after train_x[0]](https://i.stack.imgur.com/F4iwb.png){kind=link}
train_x, train_y = to_supervised(train, n_input)
train_x = train_x.astype(float)
train_y = train_y.astype(float)
# define parameters
verbose, epochs, batch_size = 1, 200, 50
n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]
# define model
model = Sequential()
opt = optimizers.RMSprop(learning_rate=1e-3)
model.add(layers.GRU(14, activation='relu', input_shape=(n_timesteps, n_features),return_sequences=False, stateful=False))
model.add(layers.Dense(1, activation='relu'))
#model.add(layers.Dense(14, activation='linear'))
model.add(layers.Dense(n_outputs, activation='sigmoid'))
model.summary()
model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])
train_y = np.concatenate(train_y).reshape(len(train_y), 168)
callback_early_stopping = EarlyStopping(monitor='val_loss',
patience=5, verbose=1)
callback_tensorboard = TensorBoard(log_dir='./23_logs/',
histogram_freq=0,
write_graph=False)
callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
min_lr=1e-4,
patience=0,
verbose=1)
callbacks = [callback_early_stopping,
callback_tensorboard,
callback_reduce_lr]
history = model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose, shuffle=False
, validation_split=0.2, callbacks=callbacks)
当我适应网络时，我得到：
11/11 [==============================] - 5s 305ms/step - loss: 0.1625 - accuracy: 0.0207 - val_loss: 0.1905 - val_accuracy: 0.0157
Epoch 2/200
11/11 [==============================] - 2s 179ms/step - loss: 0.1594 - accuracy: 0.0037 - val_loss: 0.1879 - val_accuracy: 0.0157
Epoch 3/200
11/11 [==============================] - 2s 169ms/step - loss: 0.1571 - accuracy: 0.0040 - val_loss: 0.1855 - val_accuracy: 0.0079
Epoch 4/200
11/11 [==============================] - 2s 165ms/step - loss: 0.1550 - accuracy: 0.0092 - val_loss: 0.1832 - val_accuracy: 0.0079
Epoch 5/200
11/11 [==============================] - 2s 162ms/step - loss: 0.1529 - accuracy: 0.0102 - val_loss: 0.1809 - val_accuracy: 0.0079
Epoch 6/200
11/11 [==============================] - 2s 160ms/step - loss: 0.1508 - accuracy: 0.0085 - val_loss: 0.1786 - val_accuracy: 0.0079
Epoch 7/200
11/11 [==============================] - 2s 160ms/step - loss: 0.1487 - accuracy: 0.0023 - val_loss: 0.1763 - val_accuracy: 0.0079
Epoch 8/200
11/11 [==============================] - 2s 158ms/step - loss: 0.1467 - accuracy: 0.0023 - val_loss: 0.1740 - val_accuracy: 0.0079
Epoch 9/200
11/11 [==============================] - 2s 159ms/step - loss: 0.1446 - accuracy: 0.0034 - val_loss: 0.1718 - val_accuracy: 0.0000e+00
Epoch 10/200
11/11 [==============================] - 2s 160ms/step - loss: 0.1426 - accuracy: 0.0034 - val_loss: 0.1695 - val_accuracy: 0.0000e+00
Epoch 11/200
11/11 [==============================] - 2s 162ms/step - loss: 0.1406 - accuracy: 0.0034 - val_loss: 0.1673 - val_accuracy: 0.0000e+00
Epoch 12/200
11/11 [==============================] - 2s 159ms/step - loss: 0.1387 - accuracy: 0.0034 - val_loss: 0.1651 - val_accuracy: 0.0000e+00
Epoch 13/200
11/11 [==============================] - 2s 159ms/step - loss: 0.1367 - accuracy: 0.0052 - val_loss: 0.1629 - val_accuracy: 0.0000e+00
Epoch 14/200
11/11 [==============================] - 2s 159ms/step - loss: 0.1348 - accuracy: 0.0052 - val_loss: 0.1608 - val_accuracy: 0.0000e+00
Epoch 15/200
11/11 [==============================] - 2s 161ms/step - loss: 0.1328 - accuracy: 0.0052 - val_loss: 0.1586 - val_accuracy: 0.0000e+00
Epoch 16/200
11/11 [==============================] - 2s 162ms/step - loss: 0.1309 - accuracy: 0.0052 - val_loss: 0.1565 - val_accuracy: 0.0000e+00
Epoch 17/200
11/11 [==============================] - 2s 171ms/step - loss: 0.1290 - accuracy: 0.0052 - val_loss: 0.1544 - val_accuracy: 0.0000e+00
Epoch 18/200
11/11 [==============================] - 2s 174ms/step - loss: 0.1271 - accuracy: 0.0052 - val_loss: 0.1523 - val_accuracy: 0.0000e+00
Epoch 19/200
11/11 [==============================] - 2s 161ms/step - loss: 0.1253 - accuracy: 0.0052 - val_loss: 0.1502 - val_accuracy: 0.0000e+00
Epoch 20/200
11/11 [==============================] - 2s 161ms/step - loss: 0.1234 - accuracy: 0.0052 - val_loss: 0.1482 - val_accuracy: 0.0000e+00
Epoch 21/200
11/11 [==============================] - 2s 159ms/step - loss: 0.1216 - accuracy: 0.0052 - val_loss: 0.1461 - val_accuracy: 0.0000e+00
Epoch 22/200
11/11 [==============================] - 2s 164ms/step - loss: 0.1198 - accuracy: 0.0052 - val_loss: 0.1441 - val_accuracy: 0.0000e+00
Epoch 23/200
11/11 [==============================] - 2s 164ms/step - loss: 0.1180 - accuracy: 0.0052 - val_loss: 0.1421 - val_accuracy: 0.0000e+00
Epoch 24/200
11/11 [==============================] - 2s 163ms/step - loss: 0.1162 - accuracy: 0.0052 - val_loss: 0.1401 - val_accuracy: 0.0000e+00
Epoch 25/200
11/11 [==============================] - 2s 167ms/step - loss: 0.1145 - accuracy: 0.0052 - val_loss: 0.1381 - val_accuracy: 0.0000e+00
Epoch 26/200
11/11 [==============================] - 2s 188ms/step - loss: 0.1127 - accuracy: 0.0052 - val_loss: 0.1361 - val_accuracy: 0.0000e+00
Epoch 27/200
11/11 [==============================] - 2s 169ms/step - loss: 0.1110 - accuracy: 0.0052 - val_loss: 0.1342 - val_accuracy: 0.0000e+00
Epoch 28/200
11/11 [==============================] - 2s 189ms/step - loss: 0.1093 - accuracy: 0.0052 - val_loss: 0.1323 - val_accuracy: 0.0000e+00
Epoch 29/200
11/11 [==============================] - 2s 183ms/step - loss: 0.1076 - accuracy: 0.0079 - val_loss: 0.1304 - val_accuracy: 0.0000e+00
Epoch 30/200
11/11 [==============================] - 2s 172ms/step - loss: 0.1059 - accuracy: 0.0079 - val_loss: 0.1285 - val_accuracy: 0.0000e+00
Epoch 31/200
11/11 [==============================] - 2s 164ms/step - loss: 0.1042 - accuracy: 0.0079 - val_loss: 0.1266 - val_accuracy: 0.0000e+00
Epoch 32/200
准确度始终很低，有时（如本例）val_accuracy 变为 0 并且永远不会改变。而 loss 和 val_loss 并没有很好地收敛而是减小。我意识到我肯定做错了很多事情，我无法理解如何解决它。我显然尝试过其他超参数以及其他网络（如 LSTM），但没有得到满意的结果。
我怎样才能改进模型以使准确度至少是不错的？欢迎任何建议，非常感谢！