为什么我重新加载的模型会产生不同的预测？

Question

#train model

#here is one sample
sample = validation_X[0].reshape(1, -1)

#print the sample for reference
print(sample)

#show the weights for reference
print(model.get_weights())

#show prediction
print(model.predict(sample))

#another prediction that is the same as above
print(model.predict(sample))

#save model
model.save('mymodel.h5')

#reload model
model = load_model('mymodel.h5')

#sample looks to be the same as above
print(sample)

#weights also look to be the same as above
print(model.get_weights())

#prediction is different here?
print(model.predict(sample))

为什么我的模型在重新加载后会预测一个不同的值？我检查了一下，样品显然是一样的，而且从眼睛测试来看，重量看起来也一样。是什么导致模型在此处产生不同的预测？

Answer 1

如果在两个不同的实例中加载了模型，则必须始终保存模型权重并重新加载它们。由于实数较小，模型权重可能看起来相似，但是您需要保存并重新加载权重，以使学习的权重相同。