ground0state

import numpy as np
import tensorflow as tf
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import matplotlib.pyplot as plt


# シードを設定
np.random.seed(0)

ground0state

import tensorflow as tf

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data()

x_train_mean = x_train.mean(axis=0)
x_train_std = x_train.std(axis=0)
y_train_mean = y_train.mean()
y_train_std = y_train.std()

x_train = (x_train-x_train_mean)/x_train_std

ground0state

import tensorflow as tf
import keras
from tensorflow.python.keras.datasets import cifar10
from tensorflow.python.keras.utils import to_categorical
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
from tensorflow.python.keras.callbacks import TensorBoard


(x_train, y_train), (x_test, y_test) = cifar10.load_data()

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# サンプル画像を表示する関数
import glob
import os
import random
import math
import numpy as np
import pandas as pd

import matplotlib
# matplotlib.use('Agg')

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from tensorflow.python.keras.applications.vgg16 import VGG16
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dropout, Flatten, Dense
from tensorflow.python.keras.optimizers import SGD
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.applications.vgg16 import preprocess_input
from tensorflow.python.keras.callbacks import ModelCheckpoint, CSVLogger
import os
from datetime import datetime
import json

ground0state

import os
import glob
import math
import random

import numpy as np
import matplotlib.pyplot as plt

from tensorflow.python import keras
from tensorflow.python.keras import backend as K

ground0state

import os
import glob
import math
import random

import numpy as np
import matplotlib.pyplot as plt

from tensorflow.python import keras
from tensorflow.python.keras import backend as K

ground0state

import os
import glob
import math
import random

import numpy as np
import matplotlib.pyplot as plt

from tensorflow.python import keras
from tensorflow.python.keras import backend as K

ground0state

import os
import glob
import math
import random
import datetime
import pickle

import numpy as np
import matplotlib.pyplot as plt

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)