LSTM multivariate input and multistep output

记录一下今天所学/复习，LSTM是个相当有用的东西。

1. multivariate input
multivariate simply set n_features, and size array accordingly

2. multistep output
2.1. for normal multistep

model = Sequential()
model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(n_steps_in, n_features)))
model.add(LSTM(100, activation='relu'))
model.add(Dense(n_steps_out)) #use a Dense layer with n output

[output with an array of size n_steps_out]

2.2. for encoder/decoder multistep

model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(n_steps_in, n_features)))
model.add(RepeatVector(n_steps_out)) # repeat n_features output as input to the following
model.add(LSTM(100, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.compile(optimizer='adam', loss='mse')
[has timedistributed output each with size 1]

3. side note
3.1. for cnn/lstm - time distributed cnn before pass in to lstm
(sample, subsequence, steps, features)

model = Sequential()
model.add(TimeDistributed(Conv1D(64, 1, activation='relu'), input_shape=
(None, n_steps, n_features)))
model.add(TimeDistributed(MaxPooling1D()))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(50, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
3.2. for convlstm
(sample, steps, row, columns, features)
model = Sequential()

model.add(ConvLSTM2D(64, (1,2), activation='relu', input_shape=(n_steps, 1,n_seq, n_features)))
model.add(Flatten())
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
we have 1 row here as it is easy to interpret time series as 1 dimension