python-3.x - 预测单个/多个时间步长的多行 lstm

Question

我有 250 天的数据，训练样本的 72 个特征和一列目标变量。并且想要为具有 72 个特征的 21351 行中的每一行预测接下来的 30 天。我将如何 reshape 我的数据输入和输出。似乎我有点困惑，图书馆给了我关于形状不兼容的错误。

我正在 reshape 为:

trainX.reshape(1, len(trainX), trainX.shape[1])

trainY.reshape(1, len(trainX))

但给了我错误:

ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 250 target samples.

同样的错误:

trainX.reshape(1, len(trainX), trainX.shape[1])

trainY.reshape(len(trainX), )

和同样的错误:

trainX.reshape(1, len(trainX), trainX.shape[1])

trainY.reshape(len(trainX), 1)

目前，trainX 被 reshape 为:

trainX.reshape(trainX.shape[0], 1, trainX.shape[1])

array([[[  4.49027601e+00,  -3.71848297e-01,  -3.71848297e-01, ...,
           1.06175239e+17,   1.24734085e+06,   5.16668131e+00]],

       [[  2.05921386e+00,  -3.71848297e-01,  -3.71848297e-01, ...,
           8.44426594e+17,   1.39098642e+06,   4.01803817e+00]],

       [[  9.25515792e+00,  -3.71848297e-01,  -3.71848297e-01, ...,
           4.08800518e+17,   1.24441013e+06,   3.69129399e+00]],

       ..., 
       [[  3.80037999e+00,  -3.71848297e-01,  -3.71848297e-01, ...,
           1.35414902e+18,   1.23823291e+06,   3.54601899e+00]],

       [[  3.73994822e+00,  -3.71848297e-01,   8.40698741e+00, ...,
           3.93863169e+17,   1.25693299e+06,   3.29993440e+00]],

       [[  3.56843035e+00,  -3.71848297e-01,   1.53710656e+00, ...,
           3.28306336e+17,   1.22667253e+06,   3.36569960e+00]]])

trainY reshape 为:

trainY.reshape(trainY.shape[0], )

array([[-0.7238661 ],

       [-0.43128777],

       [-0.31542821],

       [-0.35185375],

       ...,

       [-0.28319519],

       [-0.28740503],

       [-0.24209411],

       [-0.3202021 ]])

和 testX reshape 为:

testX.reshape(1, testX.shape[0], testX.shape[1])

array([[[ -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01, ...,
          -3.71848297e-01,   2.73982042e+06,  -3.71848297e-01],

        [ -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01, ...,
          -3.71848297e-01,   2.73982042e+06,  -3.71848297e-01],

        [ -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01, ...,
           2.00988794e+18,   1.05992636e+06,   2.49920150e+01],

       ..., 

        [ -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01, ...,
          -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01],

        [ -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01, ...,
          -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01],

        [ -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01, ...,
          -3.71848297e-01,  -3.71848297e-01,  -3.71848297e-01]]])

错误是:

ValueError: Error when checking : expected lstm_25_input to have shape (None, 1, 72) but got array with shape (1, 2895067, 72)

编辑 1:

这是我的模型的代码:

trainX = trainX.reshape(trainX.shape[0], 1, trainX.shape[1])
trainY = trainY.reshape(trainY.shape[0], )
testX = testX.reshape(1, testX.shape[0], testX.shape[1])

model = Sequential()

model.add(LSTM(100, return_sequences=True, input_shape = trainX.shape[0], trainX.shape[2])))
model.add(LSTM(100))
model.add(Dense(1, activation='linear'))

model.compile(loss='mse', optimizer='adam')

model.fit(trainX, trainY, epochs=500, shuffle=False, verbose=1)

model.save('model_lstm.h5')

model = load_model('model_lstm.h5')

prediction = model.predict(testX, verbose=0)

---

ValueError Traceback (most recent call last) in () 43 model.compile(loss='mse', optimizer='adam') 44 ---> 45 model.fit(exog, endog, epochs=50, shuffle=False, verbose=1) 46 47 start_date = endog_end + timedelta(days = 1)

D:\AnacondaIDE\lib\site-packages\keras\models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs) 865 class_weight=class_weight, 866 sample_weight=sample_weight, --> 867 initial_epoch=initial_epoch) 868 869 def evaluate(self, x, y, batch_size=32, verbose=1,

D:\AnacondaIDE\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs) 1520
class_weight=class_weight, 1521 check_batch_axis=False, -> 1522 batch_size=batch_size) 1523 # Prepare validation data. 1524 do_validation = False

D:\AnacondaIDE\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size) 1376
self._feed_input_shapes, 1377
check_batch_axis=False, -> 1378 exception_prefix='input') 1379 y = _standardize_input_data(y, self._feed_output_names,
1380 output_shapes,

D:\AnacondaIDE\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 142 ' to have shape ' + str(shapes[i]) + 143 ' but got array with shape ' + --> 144 str(array.shape)) 145 return arrays 146

ValueError: Error when checking input: expected lstm_31_input to have shape (None, 250, 72) but got array with shape (21351, 1, 72)

编辑 2:

从@Paddy 尝试更新的解决方案后，我在调用 predict() 时遇到此错误:

---

ValueError Traceback (most recent call last) in () 1 model = load_model('model_lstm.h5') 2 ----> 3 prediction = model.predict(exog_test, verbose=0) 4 # for x in range(0, len(exog_test)):

D:\AnacondaIDE\lib\site-packages\keras\models.py in predict(self, x, batch_size, verbose) 911 if not self.built: 912 self.build() --> 913 return self.model.predict(x, batch_size=batch_size, verbose=verbose) 914 915 def predict_on_batch(self, x):

D:\AnacondaIDE\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps) 1693 x = _standardize_input_data(x, self._feed_input_names, 1694 self._feed_input_shapes, -> 1695 check_batch_axis=False) 1696 if self.stateful: 1697 if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:

D:\AnacondaIDE\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 130 ' to have ' + str(len(shapes[i])) + 131 ' dimensions, but got array with shape ' + --> 132 str(array.shape)) 133 for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])): 134 if not j and not check_batch_axis:

ValueError: Error when checking : expected lstm_64_input to have 3 dimensions, but got array with shape (2895067, 72)

Answer 1

你有:

trainX = trainX.reshape(trainX.shape[0], 1, trainX.shape[1])
trainY = trainY.reshape(trainY.shape[0], )
testX = testX.reshape(1, testX.shape[0], testX.shape[1])

你要:

trainX = trainX.reshape(trainX.shape[0], 1, trainX.shape[1])
trainY = trainY.reshape(trainY.shape[0], )
testX = testX.reshape(testX.shape[0],1, testX.shape[1])

您在 testX 中混淆了样本和时间步长维度