The Vanishing Gradient Problem: Why Deep Learning is Becoming Significantly Harder

Understanding the Complexities of Adapting Neural Networks

Photo by Milad Fakurian on Unsplash

Introduction

The vanishing gradient problem is one of the biggest challenges faced by modern deep learning algorithms. It is a problem that arises when trying to train a neural network with large layers. In such cases, the gradients (or small changes) needed for learning become increasingly small as you move further away from the input layer. This makes it difficult for the neural network to learn from data as the error gradients become too small to affect the weights and biases of the network.

History

The concept of a vanishing gradient problem was first introduced in 1991 by Sepp Hochreiter and Jürgen Schmidhuber in their research paper titled “Long Short-Term Memory”. In it, they outlined the issues that arise when training a neural network with too many layers or too many neurons in each layer. The problem is that the gradients become too small to affect the weights and biases of the network, leading to very slow learning.

Smaller scale neural networks

In traditional algorithms, each layer contributes to the learning process. The inputs are weighted and combined using hidden layers. The outputs are then compared to the desired outputs to form an error gradient. The gradient is then used to adjust the weights and biases in the network. By making small changes to the weights and biases, the network can learn from the data.

When the neural networks become bigger

However, when the layers become too large, the gradients become too small to effect the weights and biases of the network, leading to what is known as the vanishing gradient problem. This can result in a significant decrease in the learning speed of the network, making it difficult to train.

Solutions

Fortunately, there are several techniques that can be used to overcome the vanishing gradient problem. The main techniques are weight initialization, normalization, and optimization.

1. Weight initialization involves initializing the weights of the network to be very small. This helps to keep the gradients from becoming too small.
2. Normalization involves scaling the inputs and outputs of the network. This helps to ensure that the gradients are normalized, making them easier to work with.
3. Optimization involves using algorithms to help optimize the parameters of the network. This can help to ensure that the gradients are kept stable and can be used to make small adjustments to the weights and biases.

Recurrent neural networks

The vanishing gradient problem is a common issue with recurrent neural networks (RNNs). As we discussed above, this occurs when the gradients of the weights in the network become so small that they are nearly impossible to update. This is a problem because RNNs rely on the gradients of their weights to learn new patterns. Without the gradients, the RNNs cannot learn new information.

The vanishing gradient problem is more of an issue with recurrent neural networks than other neural network algorithms because the unrolled RNN is composed of duplicates of the same network, making it more prone to instability and a decrease in gradient information.

Specific solutions to the problem include the following. By combining these techniques, a deep learning model can be optimized to perform at its best and avoid the vanishing gradient problem.

1. A technique known as “gradient clipping”. This technique helps to reduce the magnitude of the gradients and prevents them from becoming too small. It is also possible to use a combination of different activation functions to reduce the likelihood of the vanishing gradient
2. A combination of different activation functions can also be used to reduce the likelihood of the vanishing gradient problem. Activation functions such as ReLU, ELU, and PReLU can be used to help mitigate this issue.
3. Finally, regularization methods, such as dropout and L2 regularization, should be used to reduce overfitting and improve the model’s generalization ability. Dropout works by randomly selecting certain neuron connections and deactivating them during training, while L2 regularization helps to reduce model complexity by adding a penalty to the weights in the model.

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