Recurrent Neural Network

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RNNs can have various architectures, and the choice depends on the specific problem you’re trying to solve. Let’s break down your questions:

  1. Feedback Connections: In a traditional RNN, each node typically has feedback connections only to itself. This means that a node collects inputs from the previous time step of its own layer. This simple feedback loop allows the network to maintain a hidden state that can capture sequential dependencies in the data. This type of RNN is sometimes called an “Elman network.”
  2. Layer-to-Layer Feedback: You mentioned architectures where nodes receive feedback from nodes in their own layer. This kind of architecture doesn’t align with the typical RNN structure and is less common. It might be a variation or a customized design for specific applications, but it’s not the standard RNN.
  3. Feedback from Future Layers: Nodes receiving feedback from layers after them would indeed represent a different type of architecture, often referred to as a “bidirectional” or “deep RNN.” In these architectures, information can flow both forward and backward in the network. These networks can capture dependencies in both directions, making them more capable but also potentially more complex and harder to train.
  4. Output Layer: In most RNN architectures, including those with feedback connections, the output layer doesn’t have feedback connections to itself. The output layer typically produces the network’s predictions based on the final hidden state of the previous layer (the last time step of the sequence). It collects inputs only from the last hidden layer.
  5. Choosing the Right Architecture: The choice of architecture depends on your specific problem. Simple RNNs with feedback connections to themselves (option 1) are a good starting point and work well for many sequence prediction tasks. If you find that they don’t converge or capture long-range dependencies, you can explore more complex architectures like LSTMs or GRUs, which are designed to mitigate the vanishing gradient problem and capture longer dependencies.

In summary, the architecture of an RNN depends on the problem you’re trying to solve. Feedback connections to self (option 1) are standard in most RNNs and a good starting point. More complex architectures with feedback connections to other layers (options 2 and 3) exist but are used in specific cases. The output layer typically doesn’t have feedback connections. Experimentation and tuning are often necessary to find the right architecture for your specific problem.

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