LSTM数据门循环权重选型：两种主流结构该如何抉择？

Great question—this is such a common point of confusion when you start digging into the nitty-gritty of LSTM implementations, and it’s awesome you’re testing variants to see their impact firsthand!

Let’s break this down clearly:

First, the two core implementations you mentioned #

• 3-gate recurrent weight setup (no dataGate recurrence)：This is the simplified version often taught in introductory materials, where only the Input (i), Forget (f), and Output (o) gates get recurrent weights. The dataGate (z, the candidate cell state) only uses input weights, with no connection to previous hidden states. Your observation makes total sense here: while it can converge, its limited ability to carry forward long-term context means it’s more prone to getting stuck in local minima—like you said, it behaves a lot like having a too-high learning rate because the model can’t "remember" enough to navigate the loss landscape effectively.

• 4-gate full recurrent weight setup：This is the more expressive, robust variant where all components (dataGate included) have recurrent weights. This lets the model use information from previous time steps to shape the current candidate cell state, drastically improving its ability to model long-term dependencies. This is the version I’d recommend for most real-world tasks, and it’s actually the default in many modern deep learning frameworks (though always double-check the docs—some still use the simplified version as a baseline). Your preference for this variant is spot-on.

Adding context from the LSTM Peephole paper #

You’re right that the Peephole paper adds another layer of complexity with its three connection patterns on page 121. These all revolve around peephole connections—giving the gates direct access to the cell state (not just the hidden state) to make better decisions:

1. Peephole connections only for the Forget gate
2. Peephole connections for both Forget and Input gates
3. Peephole connections for all three gates (Input, Forget, Output)

These are extensions to the core LSTM structure, and they can further boost performance on tasks where precise cell state tracking matters (like time series forecasting or speech recognition).

So how do you choose? #

Here’s a quick decision framework:

• Stick with the 3-gate simplified version if you’re doing introductory experiments, teaching, or working with very short sequences where long-term context isn’t critical. It trains faster and is easier to debug.
• Go with the 4-gate full recurrent version (and consider adding peephole connections) for real-world tasks, especially those involving long sequences. It has stronger expressive power and will be less likely to get stuck in the local minima you encountered.
• If you’re still hitting optimization issues with the 4-gate setup, try tuning your learning rate down slightly or adding dropout regularization to help the model generalize better.

内容的提问来源于stack exchange，提问作者Kari