Kim Seonghyeon rosinality

## logtest.py
from vislog import Logger
from time import sleep
import numpy as np
import shutil

log = Logger('test')

brown1 = log.line('brown1')
brown2 = log.line('brown2')
image1 = log.image('image1')

## perceptual_loss.py
import torch
from torch import nn
from torchvision.models import vgg16, vgg16_bn, vgg19, vgg19_bn


class PerceptualLoss(nn.Module):
    def __init__(self, arch, indices, weights, normalize=True, min_max=(-1, 1)):
        super().__init__()

        vgg = (

## perceptual_loss.py
import torch
from torch import nn
from torchvision.models import vgg16, vgg16_bn, vgg19, vgg19_bn


class PerceptualLoss(nn.Module):
    def __init__(self, arch, indices, weights, normalize=True, min_max=(-1, 1)):
        super().__init__()

        vgg = (

## mathology.html
<!DOCTYPE html>
<html lang="en">
  <head>
	<meta charset="UTF-8">
	<title>Mathology</title>
	<style>
	 body {
		 font-family: Arial, Helvetica, sans-serif;
	 }

## adasoft.py
import torch
from torch import nn
from torch.autograd import Variable

class AdaptiveSoftmax(nn.Module):
    def __init__(self, input_size, cutoff):
        super().__init__()

        self.input_size = input_size
        self.cutoff = cutoff

## discriminator_example.py
from torch import nn
from torch.nn import init
from torch.nn import functional as F

def init_conv(conv, glu=True):
    init.kaiming_normal(conv.weight)
    if conv.bias is not None:
        conv.bias.data.zero_()

class ConvBlock(nn.Module):

## mhsampler-in-pytorch.ipynb

      
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                rosinality
                / mhsampler-in-pytorch.ipynb
            
            
              Created
              April 15, 2017 02:40
            
              
                Metropolis-Hastings sampler in PyTorch. Made to explore possibility of bayesian computation in PyTorch.
              
          
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	from vislog import Logger
	from time import sleep
	import numpy as np
	import shutil

	log = Logger('test')

	brown1 = log.line('brown1')
	brown2 = log.line('brown2')
	image1 = log.image('image1')
	import torch
	from torch import nn
	from torchvision.models import vgg16, vgg16_bn, vgg19, vgg19_bn


	class PerceptualLoss(nn.Module):
	def __init__(self, arch, indices, weights, normalize=True, min_max=(-1, 1)):
	super().__init__()

	vgg = (
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<title>Mathology</title>
	<style>
	body {
	font-family: Arial, Helvetica, sans-serif;
	}
	import torch
	from torch import nn
	from torch.autograd import Variable

	class AdaptiveSoftmax(nn.Module):
	def __init__(self, input_size, cutoff):
	super().__init__()

	self.input_size = input_size
	self.cutoff = cutoff
	from torch import nn
	from torch.nn import init
	from torch.nn import functional as F

	def init_conv(conv, glu=True):
	init.kaiming_normal(conv.weight)
	if conv.bias is not None:
	conv.bias.data.zero_()

	class ConvBlock(nn.Module):