load.LSTM

<p>Loads the pre-trained Long Short Term Memory (LSTM) Network form <code>LSTM.onnx</code>.
The function uses the <code>reticulate</code> package to import the <code>onnxruntime</code> Python library
and create an inference session for the model.</p>

A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network,
which is originally developed by
Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided.
The sample size of the dataset used to train the LSTM model is 1,000,000.
Each sample is a batch of simulated response data with a specific latent factor structure.
The eigenvalues of these response data will be used as sequential data to train the LSTM.
The pre-trained LSTM is capable of factor retention for real response data with a
true latent factor number ranging from 1 to 10, that is, determining the number of factors.

Haijiang Qin

LSTMfactors

load.LSTM: Load the pre-trained Long Short Term Memory (LSTM) Network

Description

Usage

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See Also