machine learning - Why does model.evalute() return incorrect accuracy in Keras?

Question

I trained a model with tensorflow.keras.applications.Xception. The model after training got loss less than 0.2 and accuracy more than 0.9. I predicted with the test dataset, its result matches the accuracy, more than 90% are correct. But when I called model.evaluate() with the same test dataset, its loss decreased from high to low until less than 0.2, and its accuracy grows from 0 to just more than 0.02. I repeated this function with just one sample each time, its result kept as the first time called, with loss quite high, and accuracy of 0. Why is the accuracy so low? Is it the correct behavior that the loss decreases the same as in training? Thanks!

This is the last result of the batch-evaluate:

loss: 0.6401 - dense_loss: 0.1283 - dense_1_loss: 0.1789 - dense_2_loss: 0.1638 - dense_3_loss: 0.1691 - dense_accuracy: 0.0137 - dense_1_accuracy: 0.0265 - dense_2_accuracy: 0.0261 - dense_3_accuracy: 0.0205

snapshot of model.evalute

question from:https://stackoverflow.com/questions/65844619/why-does-model-evalute-return-incorrect-accuracy-in-keras

Answer 1

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