Planar-data-classification-with-one-hidden-layer
Aim is to build a simple neural network, which will have a hidden layer.
Here:
- Implement a 2-class classification neural network with a single hidden layer
- Use units with a non-linear activation function, such as tanh
- Compute the cross entropy loss
- Implement forward and backward propagation
Result: Accuracy of 90% achieved.
Interpretation:
- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data.
- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to fits the data well without also incurring noticeable overfitting.
- Regularization, lets you use very large models (such as n_h = 50) without much overfitting.