Keras中实现模型加载与测试（以mnist为例）

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需要安装cv2

http://blog.csdn.net/gjq246/article/details/71554157

安装h5py的命令如下（模型加载模块）：
sudo pip install cython
sudo apt-get install libhdf5-dev

sudo pip install h5py

# -*- coding: UTF-8 -*-#mnist神经网络训练，采用LeNet-5模型import os  import cv2  import numpy as np from keras.models import Sequential  from keras.layers import Conv2D, MaxPooling2D, Flatten  from keras.layers.core import Dense, Dropout, Activation, Flatten  from keras.layers.advanced_activations import PReLU  from keras.optimizers import SGD, Adadelta, Adagrad  from keras.utils import np_utils  from keras.utils.vis_utils import plot_model  import h5py from keras.models import model_from_json#读取model  model = model_from_json(open('my_model_architecture.json').read())  model.load_weights('my_model_weights.h5')#读取2张图片测试testData =  np.empty((2,1,28,28),dtype="float32")imgfile='./mnisttest/0-71.bmp'print imgfileimgData=cv2.imread(imgfile, 0) #数据arr = np.asarray(imgData,dtype="float32")  cv2.namedWindow("Image1")   cv2.imshow("Image1", imgData)  testData[0,:,:,:] = arrimgfile='./mnisttest/1-1038.bmp'print imgfileimgData=cv2.imread(imgfile, 0) #数据arr = np.asarray(imgData,dtype="float32")cv2.namedWindow("Image2")   cv2.imshow("Image2", imgData)     testData[1,:,:,:] = arr#转为tensorflow格式testData = testData.reshape(testData.shape[0], 28, 28, 1)print model.predict_classes(testData, batch_size=1, verbose=0);cv2.waitKey (0)  cv2.destroyAllWindows()

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