# 6 examples of 'l2 regularization pytorch' in Python

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``56def get_regularization(self, l):57    y = []5859    for block, is_const in zip(self._blocks, self._const_params):60        if not is_const:61            y.append(block.get_regularization(l))6263    return sum(y)``
``17def regularization_loss(self):18    return self.regularization_loss``
``259def _L2_reg(self, lambda_, w1, w2):260    """Compute L2-regularization cost"""261    return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) + np.sum(w2[:, 1:] ** 2))``
``14def L2(tensor, wd=0.001):15    """ L2.1617    Computes half the L2 norm of a tensor without the `sqrt`:1819      output = sum(t ** 2) / 2 * wd2021    Arguments:22        tensor: `Tensor`. The tensor to apply regularization.23        wd: `float`. The decay.2425    Returns:26        The regularization `Tensor`.2728    """29    return tf.multiply(tf.nn.l2_loss(tensor), wd, name='L2-Loss')``
``29@property30def l2_loss(self):31    """ Compute l2 loss if weight_decay is desired """32    if self.l2_regularizer is not None:33        return tf.losses.get_regularization_loss(scope=self.name, name=self.name + 'l2_loss')``
``8def l21(parameter, bias=None, reg=0.01, lr=0.1):9    """L21 Regularization"""10    11    if bias is not None:12        w_and_b = torch.cat((parameter, bias.unfold(0,1,1)),1)13    else:14        w_and_b = parameter15    L21 = reg # lambda: regularization strength16    Norm = (lr*L21/w_and_b.norm(2, dim=1))17    if Norm.is_cuda:18        ones = torch.ones(w_and_b.size(0), device=torch.device("cuda"))19    else:20        ones = torch.ones(w_and_b.size(0), device=torch.device("cpu"))21    l21T = 1.0 - torch.min(ones, Norm)22    update = (parameter*(l21T.unsqueeze(1)))23    parameter.data = update24    # Update bias25    if bias is not None:26        update_b = (bias*l21T)27        bias.data = update_b``