Models¶
ModelContainer¶
A ModelContainer defines an interface to standardize the behavior of machine learning models. It stores the architecture and the parameters of the model. It provides methods to train and evaluate the model, and to save and load its architecture and weights.
ModelContainer([model, model_path, …]) |
Abstract base class to store and manage models. |
KerasModelContainer([model, model_path, …]) |
ModelContainer for keras models. |
Implemented models¶
Each implemented model has its own class that inherits from a specific ModelContainer, such as KerasModelContainer.
MLP([model, model_path, metrics, n_classes, …]) |
KerasModelContainer for a generic MLP model. |
SB_CNN([model, model_path, metrics, …]) |
KerasModelContainer for SB_CNN model. |
SB_CNN_SED([model, model_path, metrics, …]) |
KerasModelContainer for SB_CNN_SED model. |
A_CRNN([model, model_path, metrics, …]) |
KerasModelContainer for A_CRNN model. |
VGGish([model, model_path, metrics, …]) |
KerasModelContainer for VGGish model |
SMel([model, model_path, metrics, …]) |
KerasModelContainer for SMel model. |
MST([model, model_path, metrics, mel_bands, …]) |
KerasModelContainer for MST model. |