koshian2

# https://mail.python.org/pipermail/scipy-user/2011-May/029521.html

import numpy as np

def KLdivergence(x, y):
  """Compute the Kullback-Leibler divergence between two multivariate samples.

  Parameters
  ----------
  x : 2D array (n,d)

koshian2

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision
import torchmetrics
import pytorch_lightning as pl

class TenLayersModel(pl.LightningModule):
    def __init__(self):

koshian2

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision
import torchmetrics
import pytorch_lightning as pl
import time

class ResNet50(pl.LightningModule):

koshian2

こしあん
つぶあん
しろあん
ごまあん
うぐいすあん
栗あん

koshian2

第1章　タイトル

1.1　見出し1

ああああ

ここに本文が入る

# コメント
print("ほげほげ")
## コメント2
print("ふがふが")

koshian2

import tensorflow as tf
from tensorflow.keras import backend as K
import tensorflow.keras.layers as layers

# https://github.com/IShengFang/SpectralNormalizationKeras/blob/master/SpectralNormalizationKeras.py
class ConvSN2D(layers.Conv2D):
    def build(self, input_shape):
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:

koshian2

import torch
import torchvision
from models import TenLayersModel, ResNetLikeModel
from torchvision import transforms
from tqdm import tqdm
import numpy as np
import os
import pickle

def load_cifar():

koshian2

import torch
from torch import nn
import torch.nn.functional as F

class ResidualBlock(nn.Module):
    def __init__(self, ch):
        super().__init__()
        self.conv1 = self.conv_bn_relu(ch)
        self.conv2 = self.conv_bn_relu(ch)
        

koshian2

import torch
from torch import nn
import torch.nn.functional as F

class Generator(nn.Module):
    def __init__(self, n_classes):
        super().__init__()
        self.linear = nn.Sequential(
            nn.Linear(100 + n_classes, 768),            
            nn.ReLU(True)

koshian2

import torch
from torch import nn
import torch.nn.functional as F

class ResidualBlock(nn.Module):
    def __init__(self, ch):
        super().__init__()
        self.conv1 = self.conv_bn_relu(ch)
        self.conv2 = self.conv_bn_relu(ch)