thomasbrandon

thomasbrandon

thomasbrandon

thomasbrandon

thomasbrandon

from fastai.basics import *

class ErrorCallback(LearnerCallback):
    def __init__(self, lrn:Learner):
        super().__init__(lrn)
        self.err_loss,self.err_input,self.err_output = None,None,None
        
    def on_train_begin(self, **kwargs):
        def hook(mod, inps, outs):
            nfs = []

thomasbrandon

thomasbrandon

import torch
from torch import Tensor
from typing import Iterable
from fastprogress import progress_bar

class RunningStatistics:
    '''Records mean and variance of the final `n_dims` dimension over other dimensions across items. So collecting across `(l,m,n,o)` sized
       items with `n_dims=1` will collect `(l,m,n)` sized statistics while with `n_dims=2` the collected statistics will be of size `(l,m)`.

       Uses the algorithm from Chan, Golub, and LeVeque in "Algorithms for computing the sample variance: analysis and recommendations":

thomasbrandon

thomasbrandon

thomasbrandon