加载和检查 Keras 序列模型的总损失/验证准确性

Question

I didn't find any answers to the following question:

Is there a way to print the trained model accuracy, total model loss and model evaluation accuracy after loading the saved trained Keras model?

from keras.models import load_model
m = load_model.load("lstm_model_01.hd5") 

I checked all the callable methods of m but didn't find what I was looking for.

Answer 1

Model is really a graph with weights and that's all that gets saved. You have to evaluate the restored model on data to get predictions and from that you'll obtain an accuracy.

Answer 2

Pleas save your model fit results as follows:

history = model.fit(input_train, y_train,
                    epochs=10,
                    batch_size=128,
                    validation_split=0.2)

And use pickle to dump it (ie history) out. There you will see the training loss or validation accuracy from the model. You can load it back anytime.