wassname

from keras import backend as K

def jaccard_distance_loss(y_true, y_pred, smooth=100):
    """
    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
    
    The jaccard distance loss is usefull for unbalanced datasets. This has been
    shifted so it converges on 0 and is smoothed to avoid exploding or disapearing
    gradient.

rtqichen

## Weight norm is now added to pytorch as a pre-hook, so use that instead :)

import torch
import torch.nn as nn
from torch.nn import Parameter
from functools import wraps

class WeightNorm(nn.Module):
    append_g = '_g'
    append_v = '_v'

wassname

"""
Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss.
It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy
"""

# define custom loss and metric functions 

from keras import backend as K

def dice_coef(y_true, y_pred, smooth=1):

Jaza

*

brendanzab

NOTE: This was first authored on 26 Feb 2014. Things may have changed since then.

C++'s Templates

C++'s templates could be seen as forming a duck typed, purely functional code generation program that is run at compile time. Types are not checked at the initial invocation stage, rather the template continues to expand until it is either successful, or runs into an operation that is not supported by that specific type – in that case the compiler spits out a 'stack trace' of the state of the template expansion.

To see this in action, lets look at a very simple example:

template