# Usage
In this visualizer you can train an MLP to fit the data points. Initially, you will have the predictions of an untrained model.

---

### Dataset
Generate or upload a dataset to train on. If generating, enter a function and the visualizer will sample points uniformly in the domain based on the settings. 

---
### Model
This allows you to specify the MLP architecture. You can choose one of the presets or use a custom one by editing the text. Each layer must be in square brackets and the last layer must always be `[output_units: 1]`. Supported activation functions are: `{relu, sigmoid, tanh, leaky_relu, elu, gelu, identity}`. 

---
### Train
This allows you to train the MLP on the dataset. You can specify the optimizer options, and click **Train Step** to do make one gradient step. If you want to train faster, you can increase the **Step increment** to do multiple gradient steps each time you click **Train Step**.