WeightNet Class Reference

Inheritance diagram for WeightNet:

Collaboration diagram for WeightNet:

Public Member Functions
def	__init__ (self, n_class=6, n_detector=6, const_init=1)
def	forward (self, x)
def	get_weights (self, to_numpy=True)
def	const_init (self, const)
def	random_init (self, mean=1.0, std=0.5)
def	kill_unused (self, only)

Public Attributes
	n_class
	number of particle types
	n_detector
	number of detectors
	fcs
	linear layers for each particle type

Detailed Description

PyTorch architecture for training calibration weights.

Definition at line 62 of file pidTrainWeights.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			n_class = 6,
			n_detector = 6,
			const_init = 1
	)

Initialize the network for training.

Args:
    n_class (int, optional): Number of classification classes (particle
        types). Defaults to 6.
    n_detector (int, optional): Number of detectors. Defaults to 6.
    const_init (int, optional): Constant value to initialize all
        weights. If None, PyTorch's default random initialization is
        used instead. Defaults to 1.

Definition at line 65 of file pidTrainWeights.py.

    def __init__(self, n_class=6, n_detector=6, const_init=1):
        """Initialize the network for training.
 
        Args:
            n_class (int, optional): Number of classification classes (particle
                types). Defaults to 6.
            n_detector (int, optional): Number of detectors. Defaults to 6.
            const_init (int, optional): Constant value to initialize all
                weights. If None, PyTorch's default random initialization is
                used instead. Defaults to 1.
        """
        super().__init__()
 
        
        self.n_class = n_class
 
        
        self.n_detector = n_detector
 
        
        self.fcs = nn.ModuleList(
            [nn.Linear(self.n_detector, 1, bias=False) for _ in range(self.n_class)]
        )
 
        if const_init is not None:
            self.const_init(const_init)
 

Member Function Documentation

const_init()

def const_init	(		self,
			const
	)

Fill all the weights with the given value.

Args:
    const (float): Constant value to fill all weights with.

Definition at line 125 of file pidTrainWeights.py.

    def const_init(self, const):
        """Fill all the weights with the given value.
 
        Args:
            const (float): Constant value to fill all weights with.
        """
        with torch.no_grad():
            for fc in self.fcs:
                fc.weight.fill_(const)
 

forward()

def forward	(		self,
			x
	)

Network's forward methods. Sums the detector log-likelihoods for each particle
type, then computes the likelihood ratios. Uses the weights.

Args:
    x (torch.Tensor): Input detector log-likelihood data. Should be of
        shape (N, n_detector * n_class), where N is the number of samples.

Returns:
    torch.Tensor: Weighted likelihood ratios.

Definition at line 92 of file pidTrainWeights.py.

    def forward(self, x):
        """Network's forward methods. Sums the detector log-likelihoods for each particle
        type, then computes the likelihood ratios. Uses the weights.
 
        Args:
            x (torch.Tensor): Input detector log-likelihood data. Should be of
                shape (N, n_detector * n_class), where N is the number of samples.
 
        Returns:
            torch.Tensor: Weighted likelihood ratios.
        """
        n = self.n_detector
        outs = [self.fcs[i](x[:, i * n: (i + 1) * n]) for i in range(self.n_class)]
        out = torch.cat(outs, dim=1)
        return F.softmax(out, dim=1)
 

get_weights()

def get_weights	(		self,
			to_numpy = True
	)

Returns the weights as a six-by-six array or tensor.

Args:
    to_numpy (bool, optional): Whether to return the weights as a numpy
        array (True) or torch tensor (False). Defaults to True.

Returns:
    np.array or torch.Tensor: The six-by-six matrix containing the
        weights.

Definition at line 108 of file pidTrainWeights.py.

    def get_weights(self, to_numpy=True):
        """Returns the weights as a six-by-six array or tensor.
 
        Args:
            to_numpy (bool, optional): Whether to return the weights as a numpy
                array (True) or torch tensor (False). Defaults to True.
 
        Returns:
            np.array or torch.Tensor: The six-by-six matrix containing the
                weights.
        """
        weights = torch.cat([fc.weight.detach() for fc in self.fcs])
        if to_numpy:
            return weights.cpu().numpy()
        else:
            return weights
 

kill_unused()

def kill_unused	(		self,
			only
	)

Kills weights corresponding to unused particle types.

Args:
    only (list(str) or None): List of allowed particle types. The
        weights corresponding to any particle types that are _not_ in
        this list will be filled with zero and be frozen (e.g. gradients
        will not be computed/updated).

Definition at line 150 of file pidTrainWeights.py.

    def kill_unused(self, only):
        """Kills weights corresponding to unused particle types.
 
        Args:
            only (list(str) or None): List of allowed particle types. The
                weights corresponding to any particle types that are _not_ in
                this list will be filled with zero and be frozen (e.g. gradients
                will not be computed/updated).
        """
        if only is not None:
            # particle types that are not being trained...
            # set to zero and freeze
            for i, pdg in enumerate(PDG_CODES):
                if pdg in only:
                    continue
                self.fcs[i].weight.requires_grad = False
                self.fcs[i].weight.fill_(1)
 
 

random_init()

def random_init	(		self,
			mean = 1.0,
			std = 0.5
	)

Fill all the weights with values sampled from a Normal distribution
with given mean and standard deviation.

Args:
    mean (float, optional): The mean of the Normal distribution.
        Defaults to 1.0.
    std (float, optional): The standard deviation of the Normal
        distribution. Defaults to 0.5.

Definition at line 135 of file pidTrainWeights.py.

    def random_init(self, mean=1.0, std=0.5):
        """Fill all the weights with values sampled from a Normal distribution
        with given mean and standard deviation.
 
        Args:
            mean (float, optional): The mean of the Normal distribution.
                Defaults to 1.0.
            std (float, optional): The standard deviation of the Normal
                distribution. Defaults to 0.5.
        """
        with torch.no_grad():
            for fc in self.fcs:
                fc.weight.fill_(0)
                fc.weight.add_(torch.normal(mean=mean, std=std, size=fc.weight.size()))
 

Member Data Documentation

fcs

fcs

linear layers for each particle type

Definition at line 85 of file pidTrainWeights.py.

n_class

n_class

number of particle types

Definition at line 79 of file pidTrainWeights.py.

n_detector

n_detector

number of detectors

Definition at line 82 of file pidTrainWeights.py.

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The documentation for this class was generated from the following file:

• analysis/scripts/pidTrainWeights.py