I am new to machine learning, I working on regression neural network to prediction the outcomes of my experiments. I created the neural network with a hidden layer to predict my outcomes, now i have to tune the hyperparameter to optimize the NN.
You can find multiple ways but most of them are in add-ons, just download any app made for that
https://www.mathworks.com/help/stats/hyperparameter-optimization-in-regression-learner-app.html
I know I used youtube tutorial and app for my thesis but don't remmember which one. I might look for it when I'll have time.
Tuning hyperparameters is a critical step in optimizing a neural network, especially for regression tasks. Here are some key hyperparameters to consider and some general guidance on how to approach their tuning:
1. Number of Hidden Layers and Neurons
- Start with one or two hidden layers for a basic regression task. Increase the number if the problem is complex.
- The number of neurons in each layer typically starts with a number between the size of the input layer and the output layer. Experiment with increasing or decreasing to find the best configuration.
2. Activation Functions
- For hidden layers, 'ReLU' (Rectified Linear Unit) is commonly used due to its effectiveness and simplicity.
- For the output layer in a regression problem, a linear activation function (or no activation function) is typically used.
3. Learning Rate
- The learning rate determines how quickly or slowly a neural network updates its parameters.
- Start with a default value (e.g., 0.01), and if training is unstable or slow, adjust it. Consider using learning rate scheduling or adaptive learning rate methods like Adam.
4. Loss Function
- For regression, mean squared error (MSE) or mean absolute error (MAE) are commonly used loss functions.
5. Optimizer
- Common choices include SGD (Stochastic Gradient Descent), Adam, and RMSprop. Adam is often a good starting point due to its adaptive learning rate properties.
6. Batch Size
- The size of the batch determines how many samples the network sees before updating the weights. Smaller batches can provide a regularizing effect and larger batches offer computational efficiency.
- Common starting points are 32, 64, or 128. Adjust based on your dataset size and computational resources.
7. Epochs
- This represents the number of times the learning algorithm will work through the entire training dataset.
- Choose a value that allows the network to converge without overfitting. Use early stopping to halt training when the validation error begins to increase.
8. Regularization Techniques (If Needed)
- L1 or L2 regularization, dropout, or early stopping can help prevent overfitting.
Approach to Hyperparameter Tuning
- Start with a Baseline: Begin with a simple model and baseline hyperparameters.
- Iterative Process: Adjust one hyperparameter at a time and observe the impact.
- Validation Set: Use a validation set (or cross-validation) to evaluate the performance of your model.
- Automated Tools: Consider using hyperparameter optimization tools like Grid Search, Random Search, or Bayesian Optimization for a more systematic approach.
Remember, the optimal settings for these hyperparameters can vary widely depending on the specific characteristics of your data and the problem you are trying to solve. It often requires experimentation and iterative refinement to find the best combination.
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