Keras.evaluate（）如何计算损失？
我正在使用TensorFlow 2.0构建MLP。我正在绘制学习曲线，并使用keras.对培训和测试数据进行评估，以查看其表现的表现。我正在使用的代码：
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()
输出为：
我的问题是：keras.evaluate（）如何计算训练损失为0.5734676122665405？我以history.thistory [“损失”] bu的平均值返回不同的（0.7975356701016426）值。
或者，我是否误认为是尝试通过ever_result_tr = model.evaluate（x_train，y_train）来评估培训数据上的模型性能？
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)?
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