【keras】cifar10数据集实现3

#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Time: 2018/8/15
# @Author: xfLi
#file: keras_EvaluateCIFAR10.py

import numpy as np
import scipy.misc
from keras.models import model_from_json
from keras.optimizers import SGD

#load model
model_architecture = 'cifar10_architecture.json'
model_weights = 'cifar10_weights.h5'
model = model_from_json(open(model_architecture).read())
model.load_weights(model_weights)

#load image
img_names = ['cat2.jpg', 'dog.jpg']
imgs = [np.transpose(scipy.misc.imresize(scipy.misc.imread(img_name), (32, 32)),
                     (1, 0, 2)).astype('float32')
           for img_name in img_names]
imgs = np.array(imgs) / 255

optim = SGD()
model.compile(loss='categorical_crossentropy',
              optimizer=optim,metrics=['accuracy'])
predictions = model.predict_classes(imgs)
print(predictions)