我试图调和我对LSTM的理解，并在克里斯托弗·奥拉（Christopher Olah）在Keras中实现的这篇文章中指出了这一点。我正在关注Jason Brownlee为Keras教程撰写的博客。我最困惑的是

1. 将数据系列重塑为[samples, time steps, features]和
2. 有状态的LSTM

让我们参考下面粘贴的代码专注于以上两个问题：

# reshape into X=t and Y=t+1
look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], look_back, 1))
testX = numpy.reshape(testX, (testX.shape[0], look_back, 1))
########################
# The IMPORTANT BIT
##########################
# create and fit the LSTM network
batch_size = 1
model = Sequential()
model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
for i in range(100):
    model.fit(trainX, trainY, nb_epoch=1, batch_size=batch_size, verbose=2, shuffle=False)
    model.reset_states()

注意：create_dataset接受一个长度为N的序列，并返回一个N-look_back数组，每个元素都是一个look_back长度序列。