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datatest.py
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datatest.py
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import time
import json
import torch
import pickle
import xlsxwriter
from datetime import datetime
import numpy as np
import torch.nn.functional as F
from scipy.io import loadmat
import matplotlib.pyplot as plt
from torch.autograd import Variable as V
from mnist_reader import load_mnist
X_train, y_train = load_mnist(kind='train')
X_test, y_test = load_mnist(kind='t10k')
X_train = X_train[np.any([y_train == 4, y_train == 6, y_train == 0], axis=0)]
y_train = y_train[np.any([y_train == 4, y_train == 6, y_train == 0], axis=0)]
X_test = X_test[np.any([y_test == 4, y_test == 6, y_test == 0], axis=0)]
y_test = y_test[np.any([y_test == 4, y_test == 6, y_test == 0], axis=0)]
X_train_tmp = {}
y_train_tmp = {}
X_train_tmp[4] = []
X_train_tmp[6] = []
X_train_tmp[0] = []
y_train_tmp[4] = []
y_train_tmp[6] = []
y_train_tmp[0] = []
for i in range(18000):
X_train_tmp[y_train[i]].append(X_train[i].tolist())
y_train_tmp[y_train[i]].append(y_train[i])
X_train2 = []
y_train2 = []
for i in range(30):
k = 200 * i
X_train2 = X_train2 + X_train_tmp[4][k: k + 200] + X_train_tmp[6][k: k + 200] + X_train_tmp[0][k: k + 200]
y_train2 = y_train2 + y_train_tmp[4][k: k + 200] + y_train_tmp[6][k: k + 200] + y_train_tmp[0][k: k + 200]
# X_train2 = X_train_tmp[4] + X_train_tmp[6] + X_train_tmp[0]
# y_train2 = y_train_tmp[4] + y_train_tmp[6] + y_train_tmp[0]
X_train_ = np.array(X_train2)
y_train_ = np.array(y_train2)
Data = []
Data.append(X_train_)
Data.append(y_train_)
Data.append(X_test)
Data.append(y_test)
with open('fashion_mnist_three.pkl', 'wb') as f:
pickle.dump(Data, f)