loss值高，acc为0

[yisampi]

参数： AUG_NBR = 100

11262/11262 [==============================] - 4133s 367ms/step - loss: 3.9718 - acc: 1.1099e-05 - val_loss: 2.8851 - val_acc: 0.0000e+00 11262/11262 [==============================] - 4147s 368ms/step - loss: 2.8700 - acc: 1.1099e-05 - val_loss: 2.8474 - val_acc: 0.0000e+00 11262/11262 [==============================] - 4099s 364ms/step - loss: 2.8316 - acc: 0.0000e+00 - val_loss: 2.8033 - val_acc: 0.0000e+00 11262/11262 [==============================] - 4043s 359ms/step - loss: 2.8115 - acc: 2.2199e-05 - val_loss: 2.7982 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3998s 355ms/step - loss: 2.8044 - acc: 0.0000e+00 - val_loss: 2.7776 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3987s 354ms/step - loss: 2.7995 - acc: 0.0000e+00 - val_loss: 2.7764 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3989s 354ms/step - loss: 2.7964 - acc: 0.0000e+00 - val_loss: 2.7756 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3990s 354ms/step - loss: 2.7916 - acc: 3.3298e-05 - val_loss: 2.8062 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3991s 354ms/step - loss: 2.7950 - acc: 0.0000e+00 - val_loss: 2.7778 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3989s 354ms/step - loss: 2.7866 - acc: 1.1099e-05 - val_loss: 2.8157 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3990s 354ms/step - loss: 2.7643 - acc: 0.0000e+00 - val_loss: 2.7624 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3987s 354ms/step - loss: 2.7613 - acc: 0.0000e+00 - val_loss: 2.7621 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3986s 354ms/step - loss: 2.7620 - acc: 0.0000e+00 - val_loss: 2.7604 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3988s 354ms/step - loss: 2.7616 - acc: 0.0000e+00 - val_loss: 2.7653 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3988s 354ms/step - loss: 2.7613 - acc: 0.0000e+00 - val_loss: 2.7633 - val_acc: 0.0000e+00 11262/11262 [==============================] - 3991s 354ms/step - loss: 2.7617 - acc: 0.0000e+00 - val_loss: 2.7618 - val_acc: 0.0000e+00 11262/11262 [==============================] - 4154s 369ms/step - loss: 2.7606 - acc: 0.0000e+00 - val_loss: 2.7605 - val_acc: 0.0000e+00 11262/11262 [==============================] - 4092s 363ms/step - loss: 2.7599 - acc: 0.0000e+00 - val_loss: 2.7601 - val_acc: 0.0000e+00 11262/11262 [==============================] - 4089s 363ms/step - loss: 2.7602 - acc: 0.0000e+00 - val_loss: 2.7608 - val_acc: 0.0000e+00 11262/11262 [==============================] - 4079s 362ms/step - loss: 2.7597 - acc: 0.0000e+00 - val_loss: 2.7604 - val_acc: 0.0000e+00

[shawnh2]

不用在意acc，loss值可以通过增大epoch再降低，但降低速度微乎其微。若通过predict测试可以知道识别结果还算乐观。

[yisampi]

测试的结果 没有老版本你训练出来的效果好