基于RegNet 卷积网络对手写阿拉伯数字的迁移学习图像识别【包含完整的python代码、数据集等】

[train hyper-parameters: Namespace(batch_size=8, epochs=100, lr=0.0002)] [epoch: 1] train loss:0.1871 train accuracy:0.6480 val loss:0.3105 val accuracy:0.9187 [epoch: 2] train loss:0.0854 train accuracy:0.8695 val loss:0.3296 val accuracy:0.9821 [epoch: 3] train loss:0.0631 train accuracy:0.9100 val loss:0.3232 val accuracy:0.9901 [epoch: 4] train loss:0.0613 train accuracy:0.9015 val loss:0.3237 val accuracy:0.9861 [epoch: 5] train loss:0.0509 train accuracy:0.9125 val loss:0.3337 val accuracy:0.9861 [epoch: 6] train loss:0.0528 train accuracy:0.9095 val loss:0.3291 val accuracy:0.9841 [epoch: 7] train loss:0.0439 train accuracy:0.9140 val loss:0.3247 val accuracy:0.9921 [epoch: 8] train loss:0.0417 train accuracy:0.9270 val loss:0.3440 val accuracy:0.9821 [epoch: 9] train loss:0.0437 train accuracy:0.9215 val loss:0.3372 val accuracy:0.9762 [epoch: 10] train loss:0.0376 train accuracy:0.9305 val loss:0.3387 val accuracy:0.9246 [epoch: 11] train loss:0.0377 train accuracy:0.9295 val loss:0.3409 val accuracy:0.9821 [epoch: 12] train loss:0.0363 train accuracy:0.9350 val loss:0.3416 val accuracy:0.9861 [epoch: 13] train loss:0.0387 train accuracy:0.9245 val loss:0.3497 val accuracy:0.9881 [epoch: 14] train loss:0.0315 train accuracy:0.9400 val loss:0.3512 val accuracy:0.9841 [epoch: 15] train loss:0.0366 train accuracy:0.9240 val loss:0.3503 val accuracy:0.9881 [epoch: 16] train loss:0.0384 train accuracy:0.9230 val loss:0.3448 val accuracy:0.9802 [epoch: 17] train loss:0.0341 train accuracy:0.9340 val loss:0.3511 val accuracy:0.9861 [epoch: 18] train loss:0.0386 train accuracy:0.9305 val loss:0.3609 val accuracy:0.9921 [epoch: 19] train loss:0.0373 train accuracy:0.9165 val loss:0.3445 val accuracy:0.9960 [epoch: 20] train loss:0.0326 train accuracy:0.9395 val loss:0.3543 val accuracy:0.9881 [epoch: 21] train loss:0.0311 train accuracy:0.9430 val loss:0.3497 val accuracy:0.9960 [epoch: 22] train loss:0.0316 train accuracy:0.9370 val loss:0.3540 val accuracy:0.9921 [epoch: 23] train loss:0.0309 train accuracy:0.9420 val loss:0.3459 val accuracy:0.9940 [epoch: 24] train loss:0.0309 train accuracy:0.9330 val loss:0.3395 val accuracy:0.9940 [epoch: 25] train loss:0.0310 train accuracy:0.9340 val loss:0.3469 val accuracy:0.9901 [epoch: 26] train loss:0.0287 train accuracy:0.9430 val loss:0.3542 val accuracy:0.9960 [epoch: 27] train loss:0.0309 train accuracy:0.9405 val loss:0.3659 val accuracy:0.9881 [epoch: 28] train loss:0.0304 train accuracy:0.9355 val loss:0.3548 val accuracy:0.9881 [epoch: 29] train loss:0.0294 train accuracy:0.9400 val loss:0.3378 val accuracy:0.9921 [epoch: 30] train loss:0.0300 train accuracy:0.9375 val loss:0.3501 val accuracy:0.9960 [epoch: 31] train loss:0.0296 train accuracy:0.9420 val loss:0.3519 val accuracy:0.9901 [epoch: 32] train loss:0.0284 train accuracy:0.9395 val loss:0.3498 val accuracy:0.9921 [epoch: 33] train loss:0.0301 train accuracy:0.9325 val loss:0.3555 val accuracy:0.9881 [epoch: 34] train loss:0.0284 train accuracy:0.9470 val loss:0.3515 val accuracy:0.9921 [epoch: 35] train loss:0.0253 train accuracy:0.9500 val loss:0.3611 val accuracy:0.9901 [epoch: 36] train loss:0.0288 train accuracy:0.9430 val loss:0.3512 val accuracy:0.9940 [epoch: 37] train loss:0.0272 train accuracy:0.9435 val loss:0.3451 val accuracy:0.9940 [epoch: 38] train loss:0.0258 train accuracy:0.9470 val loss:0.3585 val accuracy:0.9921 [epoch: 39] train loss:0.0290 train accuracy:0.9395 val loss:0.3756 val accuracy:0.9742 [epoch: 40] train loss:0.0259 train accuracy:0.9385 val loss:0.3675 val accuracy:0.9960 [epoch: 41] train loss:0.0258 train accuracy:0.9420 val loss:0.3635 val accuracy:0.9603 [epoch: 42] train loss:0.0270 train accuracy:0.9490 val loss:0.3855 val accuracy:0.9940 [epoch: 43] train loss:0.0278 train accuracy:0.9385 val loss:0.3517 val accuracy:0.9940 [epoch: 44] train loss:0.0271 train accuracy:0.9425 val loss:0.3578 val accuracy:0.9901 [epoch: 45] train loss:0.0236 train accuracy:0.9505 val loss:0.3661 val accuracy:0.9921 [epoch: 46] train loss:0.0273 train accuracy:0.9435 val loss:0.3602 val accuracy:0.9940 [epoch: 47] train loss:0.0226 train accuracy:0.9550 val loss:0.3650 val accuracy:0.9940 [epoch: 48] train loss:0.0256 train accuracy:0.9420 val loss:0.3648 val accuracy:0.9901 [epoch: 49] train loss:0.0235 train accuracy:0.9510 val loss:0.3565 val accuracy:0.9881 [epoch: 50] train loss:0.0264 train accuracy:0.9435 val loss:0.3701 val accuracy:0.9940 [epoch: 51] train loss:0.0269 train accuracy:0.9445 val loss:0.3770 val accuracy:0.9960 [epoch: 52] train loss:0.0238 train accuracy:0.9490 val loss:0.3865 val accuracy:0.9881 [epoch: 53] train loss:0.0260 train accuracy:0.9445 val loss:0.3698 val accuracy:0.9921 [epoch: 54] train loss:0.0227 train accuracy:0.9560 val loss:0.3687 val accuracy:0.9861 [epoch: 55] train loss:0.0226 train accuracy:0.9515 val loss:0.3743 val accuracy:0.9881 [epoch: 56] train loss:0.0262 train accuracy:0.9410 val loss:0.3617 val accuracy:0.9940 [epoch: 57] train loss:0.0250 train accuracy:0.9465 val loss:0.3686 val accuracy:0.9960 [epoch: 58] train loss:0.0262 train accuracy:0.9455 val loss:0.3578 val accuracy:0.9960 [epoch: 59] train loss:0.0258 train accuracy:0.9480 val loss:0.3811 val accuracy:0.9921 [epoch: 60] train loss:0.0224 train accuracy:0.9550 val loss:0.3568 val accuracy:0.9921 [epoch: 61] train loss:0.0206 train accuracy:0.9575 val loss:0.3577 val accuracy:0.9901 [epoch: 62] train loss:0.0229 train accuracy:0.9545 val loss:0.3628 val accuracy:0.9901 [epoch: 63] train loss:0.0193 train accuracy:0.9585 val loss:0.3779 val accuracy:0.9921 [epoch: 64] train loss:0.0247 train accuracy:0.9460 val loss:0.3792 val accuracy:0.9921 [epoch: 65] train loss:0.0243 train accuracy:0.9515 val loss:0.3829 val accuracy:0.9841 [epoch: 66] train loss:0.0241 train accuracy:0.9500 val loss:0.3849 val accuracy:0.9940 [epoch: 67] train loss:0.0235 train accuracy:0.9515 val loss:0.3871 val accuracy:0.9921 [epoch: 68] train loss:0.0254 train accuracy:0.9435 val loss:0.3846 val accuracy:0.9802 [epoch: 69] train loss:0.0227 train accuracy:0.9455 val loss:0.3853 val accuracy:0.9782 [epoch: 70] train loss:0.0216 train accuracy:0.9525 val loss:0.3904 val accuracy:0.9782 [epoch: 71] train loss:0.0250 train accuracy:0.9480 val loss:0.3776 val accuracy:0.9901 [epoch: 72] train loss:0.0233 train accuracy:0.9575 val loss:0.3575 val accuracy:0.9841 [epoch: 73] train loss:0.0218 train accuracy:0.9520 val loss:0.3820 val accuracy:0.9802 [epoch: 74] train loss:0.0233 train accuracy:0.9545 val loss:0.3783 val accuracy:0.9881 [epoch: 75] train loss:0.0216 train accuracy:0.9565 val loss:0.3868 val accuracy:0.9861 [epoch: 76] train loss:0.0230 train accuracy:0.9520 val loss:0.3793 val accuracy:0.9901 [epoch: 77] train loss:0.0222 train accuracy:0.9520 val loss:0.3759 val accuracy:0.9940 [epoch: 78] train loss:0.0219 train accuracy:0.9590 val loss:0.3695 val accuracy:0.9921 [epoch: 79] train loss:0.0223 train accuracy:0.9490 val loss:0.3705 val accuracy:0.9940 [epoch: 80] train loss:0.0248 train accuracy:0.9445 val loss:0.3702 val accuracy:0.9960 [epoch: 81] train loss:0.0224 train accuracy:0.9515 val loss:0.3600 val accuracy:0.9940 [epoch: 82] train loss:0.0260 train accuracy:0.9470 val loss:0.3862 val accuracy:0.9821 [epoch: 83] train loss:0.0186 train accuracy:0.9520 val loss:0.3664 val accuracy:0.9960 [epoch: 84] train loss:0.0227 train accuracy:0.9485 val loss:0.3728 val accuracy:0.9901 [epoch: 85] train loss:0.0218 train accuracy:0.9555 val loss:0.3871 val accuracy:0.9841 [epoch: 86] train loss:0.0212 train accuracy:0.9535 val loss:0.3691 val accuracy:0.9921 [epoch: 87] train loss:0.0224 train accuracy:0.9495 val loss:0.3932 val accuracy:0.9921 [epoch: 88] train loss:0.0216 train accuracy:0.9510 val loss:0.4047 val accuracy:0.9901 [epoch: 89] trai