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- from keras.models import load_model
- import numpy as np
- from keras.utils import np_utils
- def read_data(path):
- with open(path) as f :
- lines=f.readlines()[0:3]
- lines=[eval(line.strip()) for line in lines]
- X,Y=zip(*lines)
- X=np.array(X)
- X=1.0*X/256
- X=X.reshape(-1,28*28)
- Y=np.array(Y)
- Y=np_utils.to_categorical(Y,num_classes)
- return X,Y
- num_classes=11
- X,Y=read_data("test_data")
- model = load_model('model')
- results=model.predict(X)
- for result,y in zip(results,Y):
- print(result)
- print(y)
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