How do I increase the accuracy of my LSTM model?
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.
How can I improve my TensorFlow accuracy?
10 tips to improve your machine learning models with TensorFlow
- 1) Clean up your dataset.
- 2) Master the art of transfer learning.
- 3) Tune your hyperparameters early and often.
- 4) Learn about dropout, L2 regularization and batch normalization.
- 5) Print a confusion matrix.
How do you calculate accuracy in LSTM?
3 Answers. Accuracy in this sense is fairly subjective. RMSE means that on average your LSTM is off by 0.12, which is a lot better than random guessing. Usually accuracies are compared to a baseline accuracy of another (simple) algorithm, so that you can see whether the task is just very easy or your LSTM is very good.
Why is my training accuracy so low?
If the training accuracy of your model is low, it’s an indication that your current model configuration can’t capture the complexity of your data. Try adjusting the training parameters.
What is Overfitting in LSTM?
Overfit Example An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade. The example below demonstrates an overfit LSTM model.
Why is training accuracy decreasing?
The training (epoch) is organized with batches of data, so that optimization function is calculated within subset of whole dataset. The console output shows the accuracy of the full dataset, so the optimization of a single batch can decrease the accuracy of the other part of the dataset and decrease the global result.