Inheritance diagram for PriorModel:

Collaboration diagram for PriorModel:

Public Member Functions
	__init__ (self, int n_output)

torch.tensor	forward (self, torch.tensor x)

Public Attributes
	hidden1 = Linear(9, 128)
	Linear layer with 9 inputs and 128 outputs.

	act1 = ReLU()
	ReLU activation layer.

	hidden2 = BatchNorm1d(128)
	Batchnormalization layer.

	hidden3 = Linear(128, 64)
	Linear layer with 128 inputs and 64 outputs.

	act2 = ReLU()
	ReLU activation layer.

	hidden4 = BatchNorm1d(64)
	Batchnormalization layer.

	hidden5 = Linear(64, 32)
	Linear layer with 64 inputs and 32 outputs.

	act3 = ReLU()
	ReLU activation layer.

	hidden6 = BatchNorm1d(32)
	Batchnormalization layer.

	hidden7 = Linear(32, n_output)
	Linear layer with 32 inputs and outputs for each particle in the particlelist.

	act4 = Softmax(dim=1)
	Softmax activation layer.

Detailed Description

Pytorch model for PID prior probability calculation.

Attributes:
    hidden1: Linear layer with 9 inputs and 128 outputs.
    act1: An RELU activation layer.
    hidden2: A batch normalization layer.
    hidden3: Linear layer with 128 inputs and 64 outputs.
    act2: An RELU activation layer.
    hidden4: A batch normalization layer.
    hidden5: Linear layer with 64 inputs and 32 outputs.
    act3: An RELU activation layer.
    hidden6: A batch normalization layer.
    hidden7: Linear layer with 9 inputs and 128 outputs.
    act4: A softmax activation layer.

Definition at line 105 of file priorDataLoaderAndModel.py.

Constructor & Destructor Documentation

◆ init()

__init__	(		self,
		int	n_output )

Initialize the PID prior probability model.

Parameter:
    n_output (int): Number of output nodes.

Definition at line 124 of file priorDataLoaderAndModel.py.

    def __init__(self, n_output: int):
        """
        Initialize the PID prior probability model.
 
        Parameter:
            n_output (int): Number of output nodes.
 
        """
        super().__init__()
        
        self.hidden1 = Linear(9, 128)
        kaiming_uniform_(self.hidden1.weight, nonlinearity="relu")
        
        self.act1 = ReLU()
        
        self.hidden2 = BatchNorm1d(128)
        
        self.hidden3 = Linear(128, 64)
        kaiming_uniform_(self.hidden3.weight, nonlinearity="relu")
        
        self.act2 = ReLU()
        
        self.hidden4 = BatchNorm1d(64)
        
        self.hidden5 = Linear(64, 32)
        kaiming_uniform_(self.hidden5.weight, nonlinearity="relu")
        
        self.act3 = ReLU()
        
        self.hidden6 = BatchNorm1d(32)
        
        self.hidden7 = Linear(32, n_output)
        xavier_uniform_(self.hidden7.weight)
        
        self.act4 = Softmax(dim=1)
 

Member Function Documentation

◆ forward()

torch.tensor forward	(		self,
		torch.tensor	x )

Gives PID prior probabilities for the input features.

Parameter:
    x (torch.tensor): A 2D tensor containing features for a particle as a row.

Returns:
    A torch tensor containing PID prior probabilities for the provided features.

Definition at line 160 of file priorDataLoaderAndModel.py.

    def forward(self, x: torch.tensor) -> torch.tensor:
        """
        Gives PID prior probabilities for the input features.
 
        Parameter:
            x (torch.tensor): A 2D tensor containing features for a particle as a row.
 
        Returns:
            A torch tensor containing PID prior probabilities for the provided features.
        """
        x = self.hidden1(x)
        x = self.act1(x)
        x = self.hidden2(x)
        x = self.hidden3(x)
        x = self.act2(x)
        x = self.hidden4(x)
        x = self.hidden5(x)
        x = self.act3(x)
        x = self.hidden6(x)
        x = self.hidden7(x)
        x = self.act4(x)
        return x