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Fei_dataset.py
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Fei_dataset.py
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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 tensorflow.examples.tutorials.mnist import input_data
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 as tf
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 = 'F:/variational-autoencoder-master/dataset/svhn_train.mat'
file2 = 'F:/variational-autoencoder-master/dataset/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