mlp

mlp

Module for ownmlp for keras model

Classes

OwnMLP

OwnMLP(
    hidden_layers,
    activation="relu",
    final_activation="linear",
    do_summary=True,
)

Bases: ModelFactory

Modelfactory for a mlp

Initializes the OwnMLP model factory

Source code in niceml/dlframeworks/keras/models/mlp.py

def __init__(
    self,
    hidden_layers: List[int],
    activation: str = "relu",
    final_activation: str = "linear",
    do_summary: bool = True,
):
    """Initializes the OwnMLP model factory"""
    self.hidden_layers = hidden_layers
    self.activation = activation
    self.do_summary = do_summary
    self.final_activation = final_activation

Functions

create_model

create_model(data_description)

Creates the mlp model

Source code in niceml/dlframeworks/keras/models/mlp.py

def create_model(self, data_description: DataDescription) -> Any:
    """Creates the mlp model"""
    input_dd: InputVectorDataDescription = check_instance(
        data_description, InputVectorDataDescription
    )
    output_dd: OutputVectorDataDescription = check_instance(
        data_description, OutputVectorDataDescription
    )
    model = Sequential()
    input_size = input_dd.get_input_size()
    # first hidden layer
    count = self.hidden_layers.pop(0)
    model.add(
        layers.Dense(count, activation=self.activation, input_shape=(input_size,))
    )
    for count in self.hidden_layers:
        model.add(layers.Dense(count, activation=self.activation))

    # Outputs from dense layer are projected onto output layer
    target_size = output_dd.get_output_size()
    model.add(layers.Dense(target_size, activation=self.final_activation))
    if self.do_summary:
        model.summary()

    return model

Functions