def test(self, backup_path='backup/cifar10-v16/', epoch=500, image_str=''):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# 读取模型
self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V2)
model_path = os.path.join(backup_path, 'model_%d.ckpt' % (epoch))
assert (os.path.exists(model_path + '.index'))
self.saver.restore(self.sess, model_path)
print('read model from %s' % (model_path))
image = cv2.imread(image_str)
image = cv2.resize(image, (32, 32), interpolation=cv2.INTER_CUBIC)
image = image.astype(float)
images = []
images.append(image)
images = np.array(images, dtype='float')
cifar10 = Corpus()
test_images = cifar10.data_augmentation(images,
flip=False,
crop=True,
crop_shape=(24, 24, 3),
whiten=True,
noise=False)
test_labels = np.array(range(10))
tst_list = []
for i in range(10):
# print("[+] ", test_labels[i])
[avg_accuracy] = self.sess.run(fetches=[self.accuracy],
feed_dict={
self.images: test_images,
self.labels: [test_labels[i]],
self.keep_prob: 1.0
})
# print(avg_accuracy)
tst_list.append(avg_accuracy)
# print(image_str)
# print(tst_list)
self.sess.close()
return tst_list
# -*- coding: utf8 -*-
# author: ronniecao
import os
from src.data.cifar10 import Corpus
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
cifar10 = Corpus()
def basic_cnn():
from src.model.basic_cnn import ConvNet
convnet = ConvNet(n_channel=3, n_classes=10, image_size=24, network_path='src/config/networks/basic.yaml')
# convnet.debug()
convnet.train(dataloader=cifar10, backup_path='backups/cifar10-v1/', batch_size=128, n_epoch=500)
# convnet.test(dataloader=cifar10, backup_path='backup/cifar10-v2/', epoch=5000, batch_size=128)
# convnet.observe_salience(batch_size=1, n_channel=3, num_test=10, epoch=2)
# convnet.observe_hidden_distribution(batch_size=128, n_channel=3, num_test=1, epoch=980)
def vgg_cnn():
from src.model.basic_cnn import ConvNet
convnet = ConvNet(n_channel=3, n_classes=10, image_size=24, network_path='src/config/networks/vgg.yaml')
# convnet.debug()
convnet.train(dataloader=cifar10, backup_path='backups/cifar10-v2/', batch_size=128, n_epoch=500)
# convnet.test(backup_path='backup/cifar10-v3/', epoch=0, batch_size=128)
# convnet.observe_salience(batch_size=1, n_channel=3, num_test=10, epoch=2)
# convnet.observe_hidden_distribution(batch_size=128, n_channel=3, num_test=1, epoch=980)
def resnet():
from src.model.resnet import ConvNet
convnet = ConvNet(n_channel=3, n_classes=10, image_size=24, network_path='src/config/networks/resnet.yaml')