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Improve Validate NN model file

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didier
2021-03-25 21:26:45 +00:00
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ROADMAP.md
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@@ -97,10 +97,12 @@
- [x] Add several exponential smoothing techniques (@didier) - [PR #132](https://github.com/DidierRLopes/GamestonkTerminal/pull/132)
- [x] Allow backtesting (@didier) - [PR #169](https://github.com/DidierRLopes/GamestonkTerminal/pull/169)
- [x] Add averaging around multiple predictions (@didier) - [PR #252](https://github.com/DidierRLopes/GamestonkTerminal/pull/252)
- [ ] CNN 1D (@didier)
### Long term
- [ ] Combine Sentiment Analysis with Chart data - [IS #240](https://github.com/DidierRLopes/GamestonkTerminal/issues/240)
- [ ] Cross-Validation methods (Forward Chaining, K-Fold, Group K-Fold) (@didier)
---

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gamestonk_terminal/prediction_techniques/VALIDATE-NN-model.md
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# Validate/Tweaking Neural Network Model
Note: Modeling a NN this way is intended for learning (fun some may say), a user guest or YOLOing on GME may be more accurate - No financial advisor tho.
1. **Select the stock that you want to attempt to predict share price**
```
load MSFT
@@ -84,12 +86,12 @@ This model is set in [config_neural_network_models.py](/config_neural_network_mo
8. **Model compilation with loss function and optimization technique**
**Loss function**: Often chosen having the activation functions of the hidden layers in mind. See https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/
**Loss function**: Often chosen having the activation functions of the hidden layers in mind. See https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/, and https://www.tensorflow.org/api_docs/python/tf/keras/losses.
```
-l/--loss : loss function. Default mae.
```
**Optimizer technique**: Adaptive Moment Estimation (_adam_) is usually the default choice for this type of problems.
**Optimizer technique**: Adaptive Moment Estimation (_adam_) is usually the default choice for this type of problems. See https://www.tensorflow.org/api_docs/python/tf/keras/optimizers.
```
-o/--optimizer : optimization technique. Default adam.
```
@@ -113,9 +115,16 @@ Select the number of loops to - using the defined model - train and predict. Thi
Interpret the following metrics: MAPE, R2, MAE, MSE and RMSE.
12. **Advanced User**
```
--batch_size: batch size for model training, should not be used unless advanced user. Default None.
```
This can be set to `floor(log2(input_layer_size))` for much faster training if a large amount of input samples (assuming you have the RAM for it). It shouldn't have much effect on the results.
```
--xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
--xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
--force_allow_gpu_growth: if true, will force TensorFlow to allow GPU memory usage to grow as needed. Otherwise will allocate 100% of available GPU memory when CUDA is set up. Default true.
```
See https://www.tensorflow.org/xla
In the Roadmap: Cross-Validation (Forward Chaining, K-Fold, Group K-fold), NN to use sentiment analysis