> For the complete documentation index, see [llms.txt](https://docs.binaexperts.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.binaexperts.com/train/advanced-hyperparameter/yaml.md).

# YAML

A hyperparameter file in YAML format is a configuration file that contains the hyperparameters and settings used to train a neural network model. YAML (YAML Ain't Markup Language) is a human-readable data serialization language that is commonly used for configuration files due to its simplicity and readability.

Here's an example of what a hyperparameter file in YAML format might look like:

```yaml
model:
  type: "resnet"
  num_layers: 18
  num_classes: 10

optimizer:
  type: "adam"
  learning_rate: 0.001
  weight_decay: 0.0001

training:
  batch_size: 32
  num_epochs: 100
  early_stopping:
    patience: 5
    min_delta: 0.001

data:
  train_data_path: "/path/to/train_data"
  val_data_path: "/path/to/val_data"
```

In this example:

* Under the `model` section, hyperparameters related to the neural network architecture are specified, such as the type of model (e.g., ResNet), the number of layers, and the number of output classes.
* Under the `optimizer` section, hyperparameters related to the optimizer used for training are specified, such as the type of optimizer (e.g., Adam), the learning rate, and weight decay.
* Under the `training` section, hyperparameters related to the training process are specified, such as the batch size, number of epochs, and early stopping criteria.
* Under the `data` section, paths to the training and validation datasets are specified.

Using a YAML file to store hyperparameters allows for easy modification and experimentation with different settings without having to modify the source code of the training script. Additionally, it provides a clear and organized way to document the hyperparameters used for training a model.


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