我的cnn模型有问题。

我有89张原始眼底图像和5张正常类别的图像，以及84张非正常类别的图像。然后，我用OpenCV扩充了普通班级，所以我有85张普通班级的图像和84张非普通班级的图像。

我用Resnet-50对其进行了培训。然后我得到了：

Train on 118 samples,validate on 51 samples
Epoch 1/200
118/118 [==============================] - 9s 80ms/sample - loss: 0.3268 - accuracy: 0.8475 - val_loss: 1260226.0000 - val_accuracy: 0.7549
Epoch 2/200
118/118 [==============================] - 1s 8ms/sample - loss: 0.2474 - accuracy: 0.8771 - val_loss: 114804.7059 - val_accuracy: 0.7549
Epoch 3/200
118/118 [==============================] - 1s 8ms/sample - loss: 0.2740 - accuracy: 0.8390 - val_loss: 6039848.1569 - val_accuracy: 0.7549
Epoch 4/200
118/118 [==============================] - 1s 8ms/sample - loss: 0.1889 - accuracy: 0.9110 - val_loss: 3247823.3725 - val_accuracy: 0.7549
.
.
.
Epoch 32/200
118/118 [==============================] - 1s 8ms/sample - loss: 7.9578e-04 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9314
Epoch 33/200
112/118 [===========================>..] - eta: 0s - loss: 2.0889e-04 - accuracy: 1.0000

Epoch 34/200
112/118 [===========================>..] - eta: 0s - loss: 0.0019 - accuracy: 1.0000
Epoch 00034: val_accuracy did not improve from 0.96078

Epoch 00034: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.
Restoring model weights from the end of the best epoch.
118/118 [==============================] - 1s 9ms/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9314
Epoch 00034: early stopping

• Loss and Accuracy图。

• Classification result。

您对此有何看法？我很困惑，因为在第一个时期val_loss如此之高，而在最后一个时期val_loss如此之低。可以吗还是我的模型过拟合？

如果我的模型是错误的，我应该怎么做才能获得更好的结果？