训练完成后， history 会保存模型训练后的相关描述。 评估模型. 1. loss, accuracy = model.evaluate(X, y) ... ... <看更多>

For reference, the two relevant parts of the code: model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) score, ... ... <看更多>

Hi, everyone. I can use model.evaluate() to calculate the test accuracy for the last epoch, but how can I get both test accuracy and ... ... <看更多>

The problem lies in your first shuffle of the whole dataset. Can you inspect your test_data just before calling model.evaluate(test_data) by calling ... ... <看更多>

This guide covers training, evaluation, and prediction (inference) models when using built-in ... It also tracks classification accuracy via add_metric() . ... <看更多>