愉快的学习就从翻译开始吧_Multi-step Time Series Forecasting_10_Multi-Step LSTM Network_Make LSTM Forecasts
Make LSTM Forecasts/让LSTM预测
The next step is to use the fit LSTM network to make forecasts.
下一步是用拟合的LSTM网络来做预测
A single forecast can be made with the fit LSTM network by calling model.predict(). Again, the data must be formatted into a 3D array with the format [samples, timesteps, features].
通过调用model.predict()，拟合的LSTM网络可以做单个预测，再次，数据必须被格式化为【samples，timesteps，features】的3D数组。
We can wrap this up into a function called forecast_lstm().
我们可以把这打包到一个名为forecast_lstm()的函数。
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# make one forecast with an LSTM,
def
forecast_lstm
(
model
,
X
,
n_batch
)
:
# reshape input pattern to [samples, timesteps, features]
X
=
X
.
reshape
(
1
,
1
,
len
(
X
)
)
# make forecast
forecast
=
model
.
predict
(
X
,
batch_size
=
n_batch
)
# convert to array
return
[
x
for
x
in
forecast
[
0
,
:
]
]
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We can call this function from the make_forecasts() function and update it to accept the model as an argument. The updated version is listed below.
我们可以从make_forecasts()中调用这个函数，并将其更新为接受模型作为参数，下面列出了更新版本
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# evaluate the persistence model
def
make_forecasts
(
model
,
n_batch
,
train
,
test
,
n_lag
,
n_seq
)
:
forecasts
=
list
(
)
for
i
in
range
(
len
(
test
)
)
:
X
,
y
=
test
[
i
,
0
:
n_lag
]
,
test
[
i
,
n_lag
:
]
# make forecast
forecast
=
forecast_lstm
(
model
,
X
,
n_batch
)
# store the forecast
forecasts
.
append
(
forecast
)
return
forecasts
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This updated version of the make_forecasts() function can be called as follows:
make_forecasts（）函数的更新版本可以调用如下：
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# make forecasts
forecasts
=
make_forecasts
(
model
,
1
,
train
,
test
,
1
,
3
)
|