训练后，我注意到-如下所示-验证损失在增加。这正常吗？还有，它的上面一个。

下面是我的代码：

# I omit data loading 

from sklearn.utils import shuffle
# shuffle input 
class_image, class_label = shuffle(class_image, class_label , random_state=0)

inputs = tf.keras.layers.Input(shape=(IMAGE_SIZE_X, IMAGE_SIZE_Y, 3), name="input_image")
x = keras.applications.resnet50.preprocess_input(inputs)
base_model  = tf.keras.applications.ResNet50(input_tensor=x,  weights=None, include_top=False, 
                                             input_shape=(IMAGE_SIZE_X, IMAGE_SIZE_Y, 3) )

x=base_model.output
x = tf.keras.layers.GlobalAveragePooling2D( name="avg_pool")(x) 
x = tf.keras.layers.Dense(2, activation='softmax', name="predications")(x)

model =  keras.models.Model(inputs=base_model.input,  outputs=x)

base_learning_rate =  0.0001 
loss = tf.keras.losses.SparseCategoricalCrossentropy()
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),
             
            loss=loss,
         
              metrics=[   'accuracy'])

history = model.fit(x=class_image ,
                    y=class_label,
                    epochs=30 
                    ,batch_size=1
                    , validation_split=0.2
                  )



# evlaute 
import matplotlib.pyplot as plt
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')

plt.plot(epochs,loss, 'y', label='Training loss')
plt.plot(epochs, val_loss, 'g', label='Validation loss')

plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()