深度學習 python 指令碼實現 keras mninst 數字識別 預測端 code

import numpyimport skimage.ioimport matplotlib.pyplot as pltfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import Dropoutfrom keras.layers import Flattenfrom keras.layers.convolutional import Conv2Dfrom keras.layers.convolutional import MaxPooling2Dfrom keras.models import load_model#if the picture is bigger than 28*28 will get below error#ValueError: cannot reshape array of size 775440 into shape (1,28,28,1)image = 'D:\\sthself\\ml\\reshape7.jpg'img2 = skimage.io.imread(image,as_grey=True)skimage.io.imshow(img2)plt.show()#img3 is a matriximg3 = numpy.reshape(img2,(1,28,28,1)).astype('float32')print(img3)# rebuild the model  ,do we need to add the layer ?  AttributeError: 'Sequential' object has no attribute 'load_model'#If you stored the complete model, not only the weights, in the HDF5 file, then it is as simple as#from keras.models import load_model#model = load_model('model.h5')# examples https://stackoverflow.com/questions/35074549/how-to-load-a-model-from-an-hdf5-file-in-kerasmodelTrained = load_model('D:\\works\\jetBrians\\PycharmProjects\\tryPicture\\my_model.h5')# we should get a correct answer is  2predict = modelTrained.predict(img3, verbose=0)#list of predicted labels and their probabilitiesprint(predict[0])#[ 0.04785086  0.02547075  0.06954221  0.03620625  0.01439319  0.03016909   0.03120618  0.00815302  0.70513636  0.03187207]# AttributeError: 'Sequential' object has no attribute 'prediect_classes'result = modelTrained.predict_proba(img3,batch_size=1, verbose=0)print(result)print("tensorflow hello word is done")

同事幫忙寫的數字

我自己寫的數字

程式列印log

重點說明： 我們自己的圖片應該是黑底白字 才能被識別

D:\applications\Anaconda3\python.exe D:/works/jetBrians/PycharmProjects/tryPicture/showPicture/ShowPicture.py
Using TensorFlow backend.

2018-03-08 20:43:29.102800: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-03-08 20:43:29.102800: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
[  1.17482814e-05   1.08457927e-03   2.43252050e-02   3.06303948e-02
   1.07244858e-04   1.54377140e-05   1.01265108e-07   9.38272536e-01
   4.20123106e-04   5.13266213e-03]
[[  1.17482814e-05   1.08457927e-03   2.43252050e-02   3.06303948e-02
    1.07244858e-04   1.54377140e-05   1.01265108e-07   9.38272536e-01
    4.20123106e-04   5.13266213e-03]]
tensorflow hello word is done

Process finished with exit code 0