carpet/lista_base.py
def transform(self, x, lbda, output_layer=None):
        """ Compute the output of the network, given x and regularization lbda

        Parameters
        ----------
        x : ndarray, shape (n_samples, n_dim)
            input of the network.
        lbda: float
            Regularization level for the optimization problem.
        output_layer : int (default: None)
            Layer to output from. It should be smaller than the number of
            layers of the network. If set to None, output the last layer of the
            network.
        """
        x = check_tensor(x, device=self.device)
        lbda = check_tensor(lbda, device=self.device)
        with torch.no_grad():
            return self(
                x, lbda, output_layer=output_layer
            ).detach().cpu().numpy()
 Compute the output of the network, given x and regularization lbda

        Parameters
        ----------
        x : ndarray, shape (n_samples, n_dim)
            input of the network.
        lbda: float
            Regularization level for the optimization problem.
        output_layer : int (default: None)
            Layer to output from. It should be smaller than the number of
            layers of the network. If set to None, output the last layer of the
            network.
        
Compute the output of the network, given x and regularization lbda
Parameters

x : ndarray, shape (n_samples, n_dim)
input of the network.
lbda: float
Regularization level for the optimization problem.
output_layer : int (default: None)
Layer to output from. It should be smaller than the number of
layers of the network. If set to None, output the last layer of the
network.
Compute the output of the network given x and regularization lbda Parameters x : ndarray shape ( n_samples n_dim ) input of the network . lbda : float Regularization level for the optimization problem . output_layer : int ( default : None ) Layer to output from . It should be smaller than the number of layers of the network . If set to None output the last layer of the network .
def transform(self, x, lbda, output_layer=None):
        x = check_tensor(x, device=self.device)
        lbda = check_tensor(lbda, device=self.device)
        with torch.no_grad():
            return self(
                x, lbda, output_layer=output_layer
            ).detach().cpu().numpy()
def transform ( self , x , lbda , output_layer = None ) : x = check_tensor ( x , device = self . device ) lbda = check_tensor ( lbda , device = self . device ) with torch . no_grad ( ) : return self ( x , lbda , output_layer = output_layer ) . detach ( ) . cpu ( ) . numpy ( )
Compute the output of the network, given x and regularization lbda
Parameters
Compute the output of the network given x and regularization lbda Parameters
""" Compute the output of the network, given x and regularization lbda

        Parameters
        ----------
        x : ndarray, shape (n_samples, n_dim)
            input of the network.
        lbda: float
            Regularization level for the optimization problem.
        output_layer : int (default: None)
            Layer to output from. It should be smaller than the number of
            layers of the network. If set to None, output the last layer of the
            network.
        """
self
x
lbda
output_layer