How does keras.evaluate() calculate the loss?

Question

I am building a MLP using TensorFlow 2.0. I am plotting the learning curve and also using keras.evaluate on both training and test data to see how well it performed. The code I'm using:

history = model.fit(X_train, y_train, batch_size=32, 
                    epochs=200, validation_split=0.2, verbose=0)
# evaluate the model
eval_result_tr = model.evaluate(X_train, y_train)
eval_result_te = model.evaluate(X_test, y_test)
print("[training loss, training accuracy]:", eval_result_tr)
print("[test loss, test accuracy]:", eval_result_te)
#[training loss, training accuracy]: [0.5734676122665405, 0.9770742654800415]
#[test loss, test accuracy]: [0.7273344397544861, 0.9563318490982056]


#plot the learning rate curve
import matplotlib.pyplot as plt
plt.plot(history.history["loss"], label='eğitim')
plt.plot(history.history['val_loss'], label='doğrulama')
plt.xlabel("Öğrenme ivmesi")
plt.ylabel("Hata payı")
plt.title("Temel modelin öğrenme eğrisi")
plt.legend()

The output is:

My question is: How keras.evaluate() calculates the training loss to be 0.5734676122665405? I take the average of history.history["loss"] bu it returns different (0.7975356701016426) value.

Or, am I mistaken to begin with by trying to evaluate the model performance on training data by eval_result_tr = model.evaluate(X_train, y_train)?

Answer 1

For community benefit, adding @Dr. Snoopy's answer here in the answer section.

This has been asked many times before, the loss you see is with changing weights during training, evaluating outside of training will use fixed weights, so you will always see a different loss value.