from keras.datasets import mnist
import matplotlib.pyplot as plt
from keras.models import load_model

def show_images_labels_predictions(images,labels,predictions,start_id,num=10):
   plt.gcf().set_size_inches(12, 14)
   if num>25: num=25 
   for i in range(0, num):
       ax=plt.subplot(5,5, i+1)
       ax.imshow(images[start_id], cmap='binary')  #顯示黑白圖片
       if( len(predictions) > 0 ) :  #有傳入預測資料
           title = 'ai = ' + str(predictions[start_id])
           title += (' (o)' if predictions[start_id]==labels[start_id] else ' (x)') 
           title += '\nlabel = ' + str(labels[start_id])
       else :  #沒有傳入預測資料
           title = 'label = ' + str(labels[start_id])
       ax.set_title(title,fontsize=12)  #X,Y軸不顯示刻度
       ax.set_xticks([]);ax.set_yticks([])        
       start_id+=1 
   plt.show()

(train_feature, train_label), (test_feature, test_label) = mnist.load_data()
test_feature_vector = test_feature.reshape(len( test_feature), 784).astype('float32')
test_feature_normalize = test_feature_vector/255
model = load_model('Mnist_mlp_model.h5')

prediction=model.predict_classes(test_feature_normalize)  #預測
show_images_labels_predictions(test_feature,test_label,prediction,0)



#***************************
#錯誤訊息如下
# model = load_model('Mnist_mlp_model.h5')
# UnicodeDecodeError: 'utf-8' codec can't decode byte 0xae in position 7: invalid start byte