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:

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.