RNN输入类似于:(NumberOfSequences, TimeSteps, ElementsPerStep)

RNN inputs are like: (NumberOfSequences, TimeSteps, ElementsPerStep)

• 每个序列在您的输入数组中都是一行.这也称为批量大小"，示例数量，样本等.

• Each sequence is a row in your input array. This is also called "batch size", number of examples, samples, etc.

时间步长是每个序列的步数

Time steps are the amount of steps for each sequence

每步元素是您在序列的每个步骤中拥有的信息

Elements per step is how much info you have in each step of a sequence

我假设27个要素是输入并与ElementsPerStep相关，而1个目标是预期输出，每步具有1个输出. 因此，我还假设您的输出是一个有55万步的序列.

I'm assuming the 27 features are inputs and relate to ElementsPerStep, while the 1 target is the expected output having 1 output per step. So I'm also assuming that your output is a sequence with also 550k steps.

调整数组的形状:

由于数组中只有一个序列，并且此序列有550k步骤，因此您必须像这样重塑数组:

Since you have only one sequence in the array, and this sequence has 550k steps, then you must reshape your array like this:

(1, 550000, 28) 
    #1 sequence
    #550000 steps per sequence    
    #28 data elements per step

#PS: this sequence is too long, if it creates memory problems to you, maybe it will be a good idea to use a `stateful=True` RNN, but I'm explaining the non stateful method first. 

现在，您必须将此数组拆分为输入和目标:

Now you must split this array for inputs and targets:

X_train = thisArray[:, :, :27] #inputs
Y_train = thisArray[:, :,  27] #targets

塑造keras层:

Keras图层在定义它们时将忽略批处理大小(序列数)，因此将使用input_shape=(550000,27).

Keras layers will ignore the batch size (number of sequences) when you define them, so you will use input_shape=(550000,27).

由于您想要的结果是长度相同的序列，因此我们将使用return_sequences=True. (否则，您只会得到一个结果).

Since your desired result is a sequence with same length, we will use return_sequences=True. (Else, you'd get only one result).

 LSTM(numberOfCells, input_shape=(550000,27), return_sequences=True)

这将输出(BatchSize, 550000, numberOfCells)

您可以使用具有1个单元的单层来实现输出，也可以堆叠更多的层，因为考虑到最后一层应该具有1个单元来匹配输出的形状. (当然，如果您仅使用循环图层)

You may use a single layer with 1 cell to achieve your output, or you could stack more layers, considering that the last one should have 1 cell to match the shape of your output. (If you're using only recurrent layers, of course)

状态=真:

当序列太长而导致内存无法很好地处理它们时，必须使用stateful=True定义图层.

When you have sequences so long that your memory can't handle them well, you must define the layer with stateful=True.

在这种情况下，您将必须将X_train分成较小长度的序列*.系统将理解，每个新批次都是先前批次的续集.

In that case, you will have to divide X_train in smaller length sequences*. The system will understand that every new batch is a sequel of the previous batches.

然后，您将需要定义batch_input_shape=(BatchSize,ReducedTimeSteps,Elements).在这种情况下，批处理大小不应像其他情况一样被忽略.

Then you will need to define batch_input_shape=(BatchSize,ReducedTimeSteps,Elements). In this case, the batch size should not be ignored like in the other case.

*不幸的是，我没有使用stateful=True的经验.我不确定是否必须手动划分数组(我猜不太可能)，或者系统是否在内部自动划分数组(可能性更大).

* Unfortunately I have no experience with stateful=True. I'm not sure about whether you must manually divide your array (less likely, I guess), or if the system automatically divides it internally (more likely).

在这种情况下，我经常看到人们像这样划分输入数据:

In this case, what I often see is people dividing the input data like this:

从550k步骤开始，获得具有50个步骤的较小阵列:

From the 550k steps, get smaller arrays with 50 steps:

X = []

for i in range(550000-49):
    X.append(originalX[i:i+50]) #then take care of the 28th element

Y = #it seems you just exclude the first 49 ones from the original