本文主要是介绍复现LLP-GAN代码（使用CIFAR-10数据集），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
代码地址
LLP-GAN
CIFAR-10数据集
CIFAR-10数据集
环境准备
python3.6
tensorflow1.14
keras2.2.5
其他的不用指定版本
数据集改为本地文件
环境准备好了以后，可以直接运行good-llp-gan.py，这里代码会去下载CIFAR-10数据集，会很慢，我们可以先下来来，然后本地直接加载。
下载好cifar-10-python.tar.gz，解压，放在dataset文件夹下。
load_local_cifar10.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_functionfrom tensorflow.keras import backend as K
import numpy as np
import osimport sys
from six.moves import cPickledef load_batch(fpath, label_key='labels'):"""Internal utility for parsing CIFAR data.# Argumentsfpath: path the file to parse.label_key: key for label data in the retrievedictionary.# ReturnsA tuple `(data, labels)`."""with open(fpath, 'rb') as f:if sys.version_info < (3,):d = cPickle.load(f)else:d = cPickle.load(f, encoding='bytes')# decode utf8d_decoded = {}for k, v in d.items():d_decoded[k.decode('utf8')] = vd = d_decodeddata = d['data']labels = d[label_key]data = data.reshape(data.shape[0], 3, 32, 32)return data, labelsdef load_data(ROOT):"""Loads CIFAR10 dataset.# ReturnsTuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`."""# dirname = 'cifar-10-batches-py'# origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'# path = get_file(dirname, origin=origin, untar=True)path = ROOTnum_train_samples = 50000x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')y_train = np.empty((num_train_samples,), dtype='uint8')for i in range(1, 6):fpath = os.path.join(path, 'data_batch_' + str(i))(x_train[(i - 1) * 10000: i * 10000, :, :, :],y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)fpath = os.path.join(path, 'test_batch')x_test, y_test = load_batch(fpath)y_train = np.reshape(y_train, (len(y_train), 1))y_test = np.reshape(y_test, (len(y_test), 1))if K.image_data_format() == 'channels_last':x_train = x_train.transpose(0, 2, 3, 1)x_test = x_test.transpose(0, 2, 3, 1)return (x_train, y_train), (x_test, y_test)
然后修改utils.py中的load_data()函数
def load_data():if hp.target_dataset == "CIFAR-10":if os.path.exists(hp.DATASET_DIR + hp.target_dataset):print("load data from pickle")with open(hp.DATASET_DIR + hp.target_dataset + "/train_X.pkl", 'rb') as f:train_X = pickle.load(f)with open(hp.DATASET_DIR + hp.target_dataset + "/train_y.pkl", 'rb') as f:train_y = pickle.load(f)with open(hp.DATASET_DIR + hp.target_dataset + "/valid_X.pkl", 'rb') as f:valid_X = pickle.load(f)with open(hp.DATASET_DIR + hp.target_dataset + "/valid_y.pkl", 'rb') as f:valid_y = pickle.load(f)with open(hp.DATASET_DIR + hp.target_dataset + "/test_X.pkl", 'rb') as f:test_X = pickle.load(f)with open(hp.DATASET_DIR + hp.target_dataset + "/test_y.pkl", 'rb') as f:test_y = pickle.load(f)else:# (cifar_X_1, cifar_y_1), (cifar_X_2, cifar_y_2) = cifar10.load_data() (cifar_X_1, cifar_y_1), (cifar_X_2, cifar_y_2) = load_local_cifar10.load_data('dataset/cifar-10-batches-py')cifar_X = np.r_[cifar_X_1, cifar_X_2]cifar_y = np.r_[cifar_y_1, cifar_y_2]cifar_X = cifar_X.astype('float32') / 255.0cifar_y = np.eye(10)[cifar_y.astype('int32').flatten()]train_X, test_X, train_y, test_y = train_test_split(cifar_X, cifar_y, test_size=5000,random_state=hp.RANDOM_STATE)train_X, valid_X, train_y, valid_y = train_test_split(train_X, train_y, test_size=5000,random_state=hp.RANDOM_STATE)os.mkdir(hp.DATASET_DIR + hp.target_dataset)with open(hp.DATASET_DIR + hp.target_dataset + "/train_X.pkl", 'wb') as f1:pickle.dump(train_X, f1)with open(hp.DATASET_DIR + hp.target_dataset + "/train_y.pkl", 'wb') as f1:pickle.dump(train_y, f1)with open(hp.DATASET_DIR + hp.target_dataset + "/valid_X.pkl", 'wb') as f1:pickle.dump(valid_X, f1)with open(hp.DATASET_DIR + hp.target_dataset + "/valid_y.pkl", 'wb') as f1:pickle.dump(valid_y, f1)with open(hp.DATASET_DIR + hp.target_dataset + "/test_X.pkl", 'wb') as f1:pickle.dump(test_X, f1)with open(hp.DATASET_DIR + hp.target_dataset + "/test_y.pkl", 'wb') as f1:pickle.dump(test_y, f1)return train_X, train_y, valid_X, valid_y, test_X, test_y
修改good-llp-gan.py
# 注掉
# trainx, trainy, validx, validy, testx, testy = utils.load_data()cifar10_dir = 'dataset/cifar-10-batches-py'
trainx, trainy, validx, validy, testx, testy = utils.load_data()
然后运行，也可以使用gpu加速，只要加入这两行(tensorflow1.0)
import keras.backend.tensorflow_backend as KTFKTF.set_session(tf.Session(config=tf.ConfigProto(device_count={'gpu': 0})))
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