ground0state

from torch import optim
from torch.utils.data import TensorDataset, DataLoader
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits


X = digits.data
Y = digits.target

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)

ground0state

import torch
from torch import nn, optim
from torch.utils.data import TensorDataset, Dataset, DataLoader
import tqdm

from torchvision.datasets import FashionMNIST
from torchvision import transforms


fashion_mnist_train = FashionMNIST("data/FashionMNIST", train=True, download=True, transform=transforms.ToTensor())

ground0state

import torch
from torch import nn, optim
from torch.utils.data import TensorDataset, Dataset, DataLoader
import tqdm
from torchvision.datasets import ImageFolder
from torchvision import transforms


!wget https://github.com/lucidfrontier45/PyTorch-Book/raw/master/data/taco_and_burrito.tar.gz
!tar -zxvf './taco_and_burrito.tar.gz'

ground0state

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from keras.models import Sequential
from keras.layers import *
from keras.layers.recurrent import SimpleRNN
from keras.optimizers import *
from keras.callbacks import *

ground0state

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import lightgbm as lgb
from sklearn.model_selection import *


!wget "https://www.analyticsvidhya.com/wp-content/uploads/2016/02/AirPassengers.csv"

ground0state

!pip install -U torchvision

# http://vis-www.cs.umass.edu/lfw/
!wget http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz
!tar -xzvf lfw-deepfunneled.tgz
!mkdir ./lfw-deepfunneled/train
!mv ./lfw-deepfunneled/[A-W]* ./lfw-deepfunneled/train
!mkdir ./lfw-deepfunneled/test
!mv ./lfw-deepfunneled/[X-Z]* ./lfw-deepfunneled/test

ground0state

!pip install -U torchvision

!wget http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz
!mkdir oxford-102
!tar -xzvf 102flowers.tgz -C ./oxford-102


import math

import numpy as np

ground0state

import math

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import *
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from torch import nn, optim

ground0state

import numpy as np
import pystan
import matplotlib.pyplot as plt
import pickle


# Stanモデル
model = """
data { 
  int<lower=0> N; // 学習データの数

ground0state

from __future__ import print_function, division
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import pystan


model = """
data {
  int<lower=1> N;