Contents

- 1 How can the LSTM model be improved?
- 2 What is regularization in LSTM?
- 3 How do you regularize in keras?
- 4 What is a good regularization value?
- 5 Is weight decay same as L2 regularization?
- 6 Why is weight regularization important in a LSTM?
- 7 How to reduce the number of units in the LSTM?
- 8 When to use a LSTM model for walk foward?
- 9 How to use features in LSTM networks for time series?

## How can the LSTM model be improved?

More layers can be better but also harder to train. As a general rule of thumb — 1 hidden layer work with simple problems, like this, and two are enough to find reasonably complex features. In our case, adding a second layer only improves the accuracy by ~0.2% (0.9807 vs. 0.9819) after 10 epochs.

## What is regularization in LSTM?

Long Short-Term Memory (LSTM) models are a recurrent neural network capable of learning sequences of observations. Weight regularization is a technique for imposing constraints (such as L1 or L2) on the weights within LSTM nodes. This has the effect of reducing overfitting and improving model performance.

## How do you regularize in keras?

Activity regularization is specified on a layer in Keras. This can be achieved by setting the activity_regularizer argument on the layer to an instantiated and configured regularizer class. The regularizer is applied to the output of the layer, but you have control over what the “output” of the layer actually means.

## What is a good regularization value?

Examples of MLP Weight Regularization The most common type of regularization is L2, also called simply “weight decay,” with values often on a logarithmic scale between 0 and 0.1, such as 0.1, 0.001, 0.0001, etc. Reasonable values of lambda [regularization hyperparameter] range between 0 and 0.1.

## Is weight decay same as L2 regularization?

L2 regularization is often referred to as weight decay since it makes the weights smaller. It is also known as Ridge regression and it is a technique where the sum of squared parameters, or weights of a model (multiplied by some coefficient) is added into the loss function as a penalty term to be minimized.

## Why is weight regularization important in a LSTM?

An issue with LSTMs is that they can easily overfit training data, reducing their predictive skill. Weight regularization is a technique for imposing constraints (such as L1 or L2) on the weights within LSTM nodes. This has the effect of reducing overfitting and improving model performance.

## How to reduce the number of units in the LSTM?

Reduce the number of units in your LSTM. Start from there. Reach a point where your model stops overfitting. Then, add dropout if required. After that, the next step is to add the tf.keras.Bidirectional. If still, you are not satfisfied then, increase number of layers.

## When to use a LSTM model for walk foward?

Specifically, to rescale the data to values between -1 and 1. These transforms are inverted on forecasts to return them into their original scale before calculating and error score. We will use a base stateful LSTM model with 1 neuron fit for 1000 epochs. Ideally a batch size of 1 would be used for walk-foward validation.

## How to use features in LSTM networks for time series?

A rolling-forecast scenario will be used, also called walk-forward model validation. Each time step of the test dataset will be walked one at a time. A model will be used to make a forecast for the time step, then the actual expected value from the test set will be taken and made available to the model for the forecast on the next time step.