python - Custom weighted loss in cntk -


i calculate weighted error below:

def calc_err(pred, targets, weights) :     nclass = np.size(pred, axis=0)       = [1.0 in range(nclass)]     nontargets = c.minus(is, targets)     wrongpred = c.minus(is, pred)     wcolumn = c.times(targets, weights)     wtarget = c.element_times(wcolumn, targets)     wnontarget = c.element_times(wcolumn, nontargets)     c1 = c.negate(c.reduce_sum(c.element_times(wtarget, c.log(pred)), axis = -1))     c2 = c.negate(c.reduce_sum(c.element_times(wnontarget, c.log(wrongpred)), axis = -1))     ce = c1 + c2      return ce.eval()

where pred prediction probabilities, targets expected one-hot array, , weights 2d array. i've created corresponding custom loss below:

def weightedcrossentropy(z, targets):     pred = c.softmax(z)     nclass = np.size(pred, axis=0)      = [1 in range(nclass)]      nontargets = c.minus(is, targets)     wrongpred = c.minus(is, pred)     wcolumn = c.times(targets, weights)     wtarget = c.element_times(wcolumn, targets)     wnontarget = c.element_times(wcolumn, nontargets)     c1 = c.negate(c.reduce_sum(c.element_times(wtarget, c.log(pred)), axis=-1))     c2 = c.negate(c.reduce_sum(c.element_times(wnontarget, c.log(wrongpred)), axis=-1))     ce = c1 + c2      return ce

when tried train, have noticed while custom loss indeed decreasing, test error calc_err(pred, targets, weights) decrease 1 or 2 epochs or not @ all. weightedcrossentropy(z, targets) ok or did wrong?

is weights constant or parameter? please make sure these 2 function take same inputs, parameters , constants.


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