How can I improve the classification accuracy of LSTM?

How can I improve the classification accuracy of LSTM?

One of the best ways to increase your model performance is to get more data; is it possible to get more data? Hyperparameter optimization is a good way to proceed; I would try playing with different learning rates, different numbers of hidden layers, and different sizes of hidden layers.

How to improve classification accuracy for machine learning?

But, some methods to enhance a classification accuracy, talking generally, are: 1 – Cross Validation : Separe your train dataset in groups, always separe a group for prediction and change the groups in each execution. Then you will know what data is better to train a more accurate model.

How to improve the accuracy of train classification?

There’s no way to help you properly without knowing the real problem you are treating. But, some methods to enhance a classification accuracy, talking generally, are: 1 – Cross Validation : Separe your train dataset in groups, always separe a group for prediction and change the groups in each execution.

How to improve classification accuracy for neural network?

I have used the extreme learning machine for classification purpose and found that my classification accuracy is only at 70+% which leads me to use the ensemble method by creating more classification model and testing data will be classified based on the majority of the models’ classification.

How is the activation function used in LSTM?

The next layer is the LSTM layer with 100 memory units. The output layer must create 13 output values, one for each class. Activation function is softmax for multi-class classification. Because it is a multi-class classification problem, categorical_crossentropy is used as the loss function.

Which is the next layer in the LSTM?

The next layer is the LSTM layer with 100 memory units. The output layer must create 13 output values, one for each class. Activation function is softmax for multi-class classification.

Which is Softmax for multi-class text classification?

Activation function is softmax for multi-class classification. Because it is a multi-class classification problem, categorical_crossentropy is used as the loss function. The plots suggest that the model has a little over fitting problem, more data may help, but more epochs will not help using the current data.