Deep Learning(CS7015): Lec 14.3 How LSTMs avoid the problem of vanishing gradients

Summary

In this transcript, the speaker explains the vanishing gradient problem faced by Recurrent Neural Networks (RNNs) due to their recurrent connections and weight matrices that can cause gradients to explode or vanish during backpropagation. The speaker contrasts this with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) networks, which introduce gates that regulate the flow of information both during forward propagation and backpropagation. These gates selectively control how much of the previous state should contribute to the current state and gradient, addressing the vanishing gradient problem by ensuring that only relevant information is carried forward and backward through the network.

Takeaways

• 😀 LSTMs and GRUs are designed to handle the vanishing gradient problem, which occurs due to recurrent connections in traditional RNNs.
• 😀 The vanishing gradient problem happens when the gradients either explode or vanish due to the repeated multiplication of the recurrent weight matrix W.
• 😀 LSTM and GRU models use gates (such as forget, input, and output gates) to regulate the flow of information during both the forward and backward passes.
• 😀 In RNNs, the recurrent connections cause gradients to be repeatedly multiplied, which can lead to either exploding or vanishing gradients.
• 😀 LSTM and GRU models still have recurrent connections, but their gates selectively regulate the flow of information, reducing the risk of gradient issues.
• 😀 The gates in LSTMs and GRUs ensure that gradients are controlled during backpropagation, so only relevant information contributes to updates.
• 😀 Vanishing gradients can be acceptable in LSTMs and GRUs when they occur due to information not contributing significantly during the forward pass.
• 😀 Exploding gradients are easier to manage compared to vanishing gradients because they can be handled with techniques like gradient clipping.
• 😀 If a state does not contribute significantly to the forward pass, its gradient will vanish in the backward pass, which is a fair and expected behavior.
• 😀 LSTMs and GRUs regulate the gradient flow by using the same gates in both forward and backward passes, ensuring a controlled learning process.

Q & A

What is the main problem with traditional RNNs that LSTMs and GRUs aim to solve?

The main problem with traditional RNNs is the vanishing and exploding gradient issue, which occurs due to recurrent connections. These recurrent connections can cause gradients to either shrink (vanish) or grow uncontrollably (explode) during backpropagation, making it difficult to train the network effectively.

How do the recurrent connections in traditional RNNs contribute to the vanishing gradient problem?

In traditional RNNs, the recurrent connections involve multiplying gradients by a weight matrix repeatedly across time steps. If the weight matrix has large values, gradients can explode, and if the matrix has small values, gradients can vanish, both leading to learning difficulties.

Do LSTMs and GRUs have recurrent connections, and how do they avoid the vanishing gradient problem?

Yes, LSTMs and GRUs still have recurrent connections. However, they use gates to control the flow of information and gradients. These gates regulate how much information from the past should be passed forward and how gradients are propagated backward, helping to avoid the vanishing gradient problem.

What role do gates play in LSTMs and GRUs during the forward pass?

During the forward pass, gates in LSTMs and GRUs control how much of the previous state information should be passed forward to the current state. They ensure that only relevant information is carried forward, which prevents unnecessary information from diluting the network’s performance.

How does the gradient flow during backpropagation in LSTMs and GRUs?

During backpropagation, the gradients are regulated by the same gates that control information flow in the forward pass. If a state did not contribute significantly to the output (because its gate value was small), its gradient will also be reduced in the backward pass, preventing unnecessary or harmful gradient updates.

Why is vanishing gradient a more challenging problem than exploding gradients?

Vanishing gradients are more challenging because once the gradients shrink to near zero, they cannot be recovered, making it impossible for the network to learn. On the other hand, exploding gradients can be mitigated using techniques like gradient clipping, making them easier to handle.

What happens when a gate value in LSTMs or GRUs is very small?

When a gate value is very small, it means that the information from the previous state contributes very little to the current state during the forward pass. In the backward pass, the corresponding gradient will also be very small, which can help prevent the vanishing gradient problem by ensuring that unnecessary gradients don't get propagated.

How does the regulation of gradient flow by gates help in training deep networks?

The regulation of gradient flow by gates helps in training deep networks by ensuring that only relevant gradients are propagated back through the network. This prevents gradients from vanishing or exploding across many layers, allowing deep networks to learn more effectively over time.

What is the significance of the forget gates in LSTMs and GRUs during the backward pass?

The forget gates in LSTMs and GRUs play a crucial role during the backward pass by controlling how much of the previous state’s gradient should be propagated. If the state did not contribute much to the output, the forget gate ensures that the gradient for that state is also diminished, maintaining a fair learning process.

What does the speaker mean by 'this kind of vanishing gradient is fine'?

The speaker refers to the case where a state’s contribution to the current state is negligible (due to small gate values), and thus the gradient for that state is also small during backpropagation. This is considered 'fine' because it is a fair reflection of the state’s minimal contribution during the forward pass, ensuring that the network learns responsibly.