为什么是重量仅用于培训？

后叫的适应功能我可以看到模型聚光的培训，但之后我去叫评估方法，它的行为，如果该模型没有配件。 最好的例子是，下面我使用的培训，发电机用于培训和验证，并获得不同的结果。

import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint

from ImageGenerator import ImageGenerator

if __name__== "__main__":

    batch_size=64

    train_gen = ImageGenerator('synthetic3/train/open/*.png', 'synthetic3/train/closed/*.png', batch_size=batch_size)

    model = tf.keras.applications.mobilenet_v2.MobileNetV2(weights=None, classes=2, input_shape=(256, 256, 3))

    model.compile(optimizer='adam', 
                loss=tf.keras.losses.CategoricalCrossentropy(),
                metrics=['accuracy'])

    history = model.fit(
        train_gen,
        validation_data=train_gen,
        epochs=5,
        verbose=1
    )
    
    model.evaluate(train_gen)

结果，

Epoch 1/5
19/19 [==============================] - 11s 600ms/step - loss: 0.7707 - accuracy: 0.5016 - val_loss: 0.6932 - val_accuracy: 0.5016
Epoch 2/5
19/19 [==============================] - 10s 533ms/step - loss: 0.6991 - accuracy: 0.5855 - val_loss: 0.6935 - val_accuracy: 0.4975
Epoch 3/5
19/19 [==============================] - 10s 509ms/step - loss: 0.6213 - accuracy: 0.6637 - val_loss: 0.6932 - val_accuracy: 0.4992
Epoch 4/5
19/19 [==============================] - 10s 514ms/step - loss: 0.4407 - accuracy: 0.8158 - val_loss: 0.6934 - val_accuracy: 0.5008
Epoch 5/5
19/19 [==============================] - 10s 504ms/step - loss: 0.3200 - accuracy: 0.8643 - val_loss: 0.6949 - val_accuracy: 0.5000
19/19 [==============================] - 3s 159ms/step - loss: 0.6953 - accuracy: 0.4967

这是有问题的，因为即使在节约重量节省，如果该模型还没有完成装修。