koshian2

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torchvision.datasets import MNIST, FashionMNIST, CIFAR10, STL10
import os
import pickle

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 matplotlib.pyplot as plt
import pickle
from keras.layers import Input, Conv2D, Activation, MaxPool2D, BatchNormalization, Flatten, Dense, Dropout
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator

(X_train, y_train), (X_val, y_val) = cifar10.load_data()

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

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Input, Concatenate, MaxPool2D, Conv2DTranspose, Add
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import Callback, History
import tensorflow.keras.backend as K

from keras.objectives import mean_squared_error
import os, tarfile, shutil, pickle
from PIL import Image

koshian2

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm


# 単回帰分析 #

# データ
X = np.array([6.1101,5.5277,8.5186,7.0032,5.8598,8.3829,7.4764,8.5781,6.4862,5.0546,5.7107,14.164,5.734,8.4084,5.6407,5.3794,6.3654,5.1301,6.4296,7.0708,6.1891,20.27,5.4901,6.3261,5.5649,18.945,12.828,10.957,13.176,22.203,5.2524,6.5894,9.2482,5.8918,8.2111,7.9334,8.0959,5.6063,12.836,6.3534,5.4069,6.8825,11.708,5.7737,7.8247,7.0931,5.0702,5.8014,11.7,5.5416,7.5402,5.3077,7.4239,7.6031,6.3328,6.3589,6.2742,5.6397,9.3102,9.4536,8.8254,5.1793,21.279,14.908,18.959,7.2182,8.2951,10.236,5.4994,20.341,10.136,7.3345,6.0062,7.2259,5.0269,6.5479,7.5386,5.0365,10.274,5.1077,5.7292,5.1884,6.3557,9.7687,6.5159,8.5172,9.1802,6.002,5.5204,5.0594,5.7077,7.6366,5.8707,5.3054,8.2934,13.394,5.4369])

koshian2

import tensorflow as tf
from tensorflow.keras.applications import InceptionV3, VGG16, MobileNet
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import History, Callback
import tensorflow.keras.backend as K
from tensorflow.contrib.tpu.python.tpu import keras_support

from keras.utils import to_categorical
from keras.datasets import cifar10

koshian2

import torch
from torch import nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from tqdm import tqdm
import os
import pickle
import statistics

koshian2

import tensorflow as tf
import tensorflow.python.keras as keras
import tensorflow.python.keras.layers as layers
from tensorflow.contrib.tpu.python.tpu import keras_support
import datetime
import time
import pickle
import os

def conv_bn_relu(input, ch, reps):