Correcção CNN overfitting

Eu estou usando a CNN e MobileNet modelos para construir um modelo para classificar a linguagem de sinais para letras do alfabeto com base em imagens de um conjunto de dados. Então, ele é um multi-classe classificação do modelo. No entanto, depois de compilar e ajuste do modelo. Eu tenho uma alta precisão (98%). Mas quando eu quero visualizar a matriz de confusão eu tenho muita saudade da matriz. Isso significa que o modelo é overfitting? e como posso corrigi-lo para obter uma melhor matriz?

train_path = 'train'
test_path = 'test'

train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input).flow_from_directory(
    directory=train_path, target_size=(64,64), batch_size=10)


test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input).flow_from_directory(
    directory=test_path, target_size=(64,64), batch_size=10)


mobile = tf.keras.applications.mobilenet.MobileNet()

x = mobile.layers[-6].output
output = Dense(units=32, activation='softmax')(x)
model = Model(inputs=mobile.input, outputs=output)
for layer in model.layers[:-23]:
    layer.trainable = False
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])

class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self,epoch,logs={}):
        if(logs.get('val_accuracy')>=0.98):
            print('\n Reached to good accuracy')
            self.model.stop_training=True
callbacks=myCallback()


model.fit(train_batches,
            steps_per_epoch=len(train_batches), 
            validation_data=test_batches,
            validation_steps=len(test_batches),
            epochs=10,callbacks=[callbacks])




Epoch 1/10
4498/4498 [==============================] - 979s 217ms/step - loss: 1.3062 - accuracy: 0.6530 - val_loss: 0.1528 - val_accuracy: 0.9594
Epoch 2/10
4498/4498 [==============================] - 992s 221ms/step - loss: 0.1777 - accuracy: 0.9491 - val_loss: 0.1164 - val_accuracy: 0.9691
Epoch 3/10
4498/4498 [==============================] - 998s 222ms/step - loss: 0.1117 - accuracy: 0.9654 - val_loss: 0.0925 - val_accuracy: 0.9734
Epoch 4/10
4498/4498 [==============================] - 1000s 222ms/step - loss: 0.0789 - accuracy: 0.9758 - val_loss: 0.0992 - val_accuracy: 0.9750
Epoch 5/10
4498/4498 [==============================] - 1001s 223ms/step - loss: 0.0626 - accuracy: 0.9805 - val_loss: 0.0818 - val_accuracy: 0.9783
Epoch 6/10
4498/4498 [==============================] - 1007s 224ms/step - loss: 0.0521 - accuracy: 0.9834 - val_loss: 0.0944 - val_accuracy: 0.9789
Epoch 7/10
4498/4498 [==============================] - 1004s 223ms/step - loss: 0.0475 - accuracy: 0.9863 - val_loss: 0.0935 - val_accuracy: 0.9795
Epoch 8/10
4498/4498 [==============================] - 1013s 225ms/step - loss: 0.0371 - accuracy: 0.9880 - val_loss: 0.0854 - val_accuracy: 0.9781
Epoch 9/10
4498/4498 [==============================] - 896s 199ms/step - loss: 0.0365 - accuracy: 0.9879 - val_loss: 0.0766 - val_accuracy: 0.9806

 Reached to good accuracy


test_labels = test_batches.classes

predictions = model.predict(x=test_batches, steps=len(test_batches),verbose=0)

cm = confusion_matrix(y_true=test_labels, y_pred=predictions.argmax(axis=1))


cm_plot_labels = ['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16',
                  '17','18','19','20','21','22','23','24','25','26','27','28','29','30','31'
                 ]
plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')

o resultado da matriz de confusão