nasimrahaman

import torch
import torch.nn as nn


class ContinoulliWithLogitsLoss(nn.BCEWithLogitsLoss):
    """
    Numerically stable implementation of the objective function defined in [1].
    [1] https://arxiv.org/abs/1907.06845
    """

nasimrahaman

import torch
import torch.nn as nn


def log_sum_exp(x):
    # See implementation detail in
    # http://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/
    # b is a shift factor. see link.
    # x.size() = [N, C]:
    b, _ = torch.max(x, 1)

nasimrahaman

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

# Elastic transform
def elastic_transformations(alpha, sigma, rng=np.random.RandomState(42), 
                            interpolation_order=1):
    """Returns a function to elastically transform multiple images."""
    # Good values for:
    #   alpha: 2000

nasimrahaman

import yaml
import numpy as np
import os

from theano import config


class relay(object):
    def __init__(self, switches, ymlfile, callevery=1):

nasimrahaman

__author__ = "nasim.rahaman at iwr.uni-heidelberg.de"
__doc__ = """A few bells and whistles for the theano function callable. 

             Examples: 
                import theano.tensor as T
                x = T.scalar()
                y = T.scalar()
                
                f1 = function(inputs={'x': x, 'y': y}, outputs={'z1': x + y, 'z2': x + 2*y})
                f1(x=2, y=3)

nasimrahaman

# (CC-NC-SA) Nasim Rahaman 

import theano as th
import theano.tensor as T
import numpy as np
import time

# Weights
W = th.shared(value=np.random.uniform(size=(3, 3)))
# Input