Fei_dataset.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from keras.optimizers import SGD
from scipy.misc import imsave as ims

from keras.layers import Dense, Input
from keras.layers import Conv2D, Flatten, Lambda
from keras.layers import Reshape, Conv2DTranspose
from keras.models import Model
from keras.datasets import mnist
from keras.losses import mse, binary_crossentropy
from keras.utils import plot_model
from keras import backend as K
from keras.models import Sequential, Model

from keras.layers import Dropout,Activation

import numpy as np
import matplotlib.pyplot as plt
import argparse
import os
import  tensorflow as tf
from utils import *

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
#from tensorflow.compat.v1.examples.tutorials.mnist import input_data


import keras
import scipy.io as sio
from keras import utils as np_utils

def GetMNIST_DataSet():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    image_size = x_train.shape[1]
    x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
    x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
    x_train = x_train.astype('float32') / 255
    x_test = x_test.astype('float32') / 255
    return x_train,y_train,x_test,y_test

def GetSVHN_DataSet(isResize = False):
    file1 = 'data/svhn_train.mat'
    file2 = 'data/svhn_test.mat'
    train_data = sio.loadmat(file1)
    test_data = sio.loadmat(file2)

    x_train_hv = train_data['X']
    y_train_hv = train_data['y']
    x_test_hv = test_data["X"]
    y_test_hv = test_data["y"]
    
    x_train_hv = x_train_hv.transpose(3, 0, 1, 2)
    x_test_hv = x_test_hv.transpose(3, 0, 1, 2)

    if isResize:
        x_train_hv = tf.image.resize_images(x_train_hv, (28, 28))
        x_test_hv = tf.image.resize_images(x_test_hv, (28, 28))
        x_train_hv = tf.image.rgb_to_grayscale(x_train_hv)
        x_test_hv = tf.image.rgb_to_grayscale(x_test_hv)

        x_train_hv = tf.Session().run(x_train_hv)
        x_test_hv = tf.Session().run(x_test_hv)

    for h1 in range(np.shape(y_test_hv)[0]):
        y_test_hv[h1] = y_test_hv[h1]-1
    for h1 in range(np.shape(y_train_hv)[0]):
        y_train_hv[h1] = y_train_hv[h1]-1

    x_train_hv = x_train_hv.astype('float32') / 255
    x_test_hv = x_test_hv.astype('float32') / 255

    #y_test_hv = keras.utils.to_categorical(y_test_hv)
    return x_train_hv,y_train_hv,x_test_hv,y_test_hv