经典CNN网络之VGG16图像分类网络实战项目：常见102花图像分类迁移项目

[train hyper-parameters: Namespace(batch_size=16, lr=0.001, lrf=0.01)] [epoch: 1] train loss:0.1681 test accuracy:0.7298 [epoch: 2] train loss:0.0536 test accuracy:0.8252 [epoch: 3] train loss:0.0280 test accuracy:0.9071 [epoch: 4] train loss:0.0175 test accuracy:0.9022 [epoch: 5] train loss:0.0127 test accuracy:0.9071 [epoch: 6] train loss:0.0090 test accuracy:0.9230 [epoch: 7] train loss:0.0071 test accuracy:0.9108 [epoch: 8] train loss:0.0062 test accuracy:0.9242 [epoch: 9] train loss:0.0043 test accuracy:0.9413 [epoch: 10] train loss:0.0031 test accuracy:0.9205 [epoch: 11] train loss:0.0042 test accuracy:0.9389 [epoch: 12] train loss:0.0037 test accuracy:0.9377 [epoch: 13] train loss:0.0026 test accuracy:0.9462 [epoch: 14] train loss:0.0017 test accuracy:0.9401 [epoch: 15] train loss:0.0019 test accuracy:0.9548 [epoch: 16] train loss:0.0011 test accuracy:0.9352 [epoch: 17] train loss:0.0017 test accuracy:0.9438 [epoch: 18] train loss:0.0014 test accuracy:0.9438 [epoch: 19] train loss:0.0019 test accuracy:0.9523 [epoch: 20] train loss:0.0014 test accuracy:0.9254 [epoch: 21] train loss:0.0012 test accuracy:0.9389 [epoch: 22] train loss:0.0012 test accuracy:0.9450 [epoch: 23] train loss:0.0010 test accuracy:0.9462 [epoch: 24] train loss:0.0010 test accuracy:0.9535 [epoch: 25] train loss:0.0011 test accuracy:0.9364 [epoch: 26] train loss:0.0010 test accuracy:0.9389 [epoch: 27] train loss:0.0007 test accuracy:0.9474 [epoch: 28] train loss:0.0004 test accuracy:0.9633 [epoch: 29] train loss:0.0001 test accuracy:0.9584 [epoch: 30] train loss:0.0009 test accuracy:0.9438 [epoch: 31] train loss:0.0002 test accuracy:0.9499 [epoch: 32] train loss:0.0002 test accuracy:0.9487 [epoch: 33] train loss:0.0001 test accuracy:0.9535 [epoch: 34] train loss:0.0002 test accuracy:0.9511 [epoch: 35] train loss:0.0002 test accuracy:0.9425 [epoch: 36] train loss:0.0006 test accuracy:0.9413 [epoch: 37] train loss:0.0002 test accuracy:0.9499 [epoch: 38] train loss:0.0001 test accuracy:0.9499 [epoch: 39] train loss:0.0004 test accuracy:0.9487 [epoch: 40] train loss:0.0001 test accuracy:0.9584 [epoch: 41] train loss:0.0002 test accuracy:0.9511 [epoch: 42] train loss:0.0002 test accuracy:0.9499 [epoch: 43] train loss:0.0003 test accuracy:0.9633 [epoch: 44] train loss:0.0001 test accuracy:0.9609 [epoch: 45] train loss:0.0001 test accuracy:0.9584 [epoch: 46] train loss:0.0001 test accuracy:0.9633 [epoch: 47] train loss:0.0000 test accuracy:0.9682 [epoch: 48] train loss:0.0001 test accuracy:0.9609 [epoch: 49] train loss:0.0002 test accuracy:0.9548 [epoch: 50] train loss:0.0000 test accuracy:0.9670 [epoch: 51] train loss:0.0000 test accuracy:0.9658 [epoch: 52] train loss:0.0000 test accuracy:0.9707 [epoch: 53] train loss:0.0000 test accuracy:0.9633 [epoch: 54] train loss:0.0001 test accuracy:0.9645 [epoch: 55] train loss:0.0001 test accuracy:0.9633 [epoch: 56] train loss:0.0001 test accuracy:0.9597 [epoch: 57] train loss:0.0000 test accuracy:0.9633 [epoch: 58] train loss:0.0000 test accuracy:0.9670 [epoch: 59] train loss:0.0000 test accuracy:0.9658 [epoch: 60] train loss:0.0000 test accuracy:0.9682 [epoch: 61] train loss:0.0000 test accuracy:0.9645 [epoch: 62] train loss:0.0000 test accuracy:0.9694 [epoch: 63] train loss:0.0000 test accuracy:0.9694 [epoch: 64] train loss:0.0000 test accuracy:0.9707 [epoch: 65] train loss:0.0000 test accuracy:0.9682 [epoch: 66] train loss:0.0000 test accuracy:0.9694 [epoch: 67] train loss:0.0000 test accuracy:0.9694 [epoch: 68] train loss:0.0000 test accuracy:0.9694 [epoch: 69] train loss:0.0000 test accuracy:0.9682 [epoch: 70] train loss:0.0000 test accuracy:0.9682 [epoch: 71] train loss:0.0000 test accuracy:0.9694 [epoch: 72] train loss:0.0000 test accuracy:0.9670 [epoch: 73] train loss:0.0000 test accuracy:0.9682 [epoch: 74] train loss:0.0000 test accuracy:0.9719 [epoch: 75] train loss:0.0000 test accuracy:0.9719 [epoch: 76] train loss:0.0000 test accuracy:0.9731 [epoch: 77] train loss:0.0000 test accuracy:0.9719 [epoch: 78] train loss:0.0000 test accuracy:0.9743 [epoch: 79] train loss:0.0000 test accuracy:0.9719 [epoch: 80] train loss:0.0000 test accuracy:0.9719 [epoch: 81] train loss:0.0000 test accuracy:0.9719 [epoch: 82] train loss:0.0000 test accuracy:0.9731 [epoch: 83] train loss:0.0000 test accuracy:0.9731 [epoch: 84] train loss:0.0000 test accuracy:0.9743 [epoch: 85] train loss:0.0000 test accuracy:0.9743 [epoch: 86] train loss:0.0000 test accuracy:0.9743 [epoch: 87] train loss:0.0000 test accuracy:0.9743 [epoch: 88] train loss:0.0000 test accuracy:0.9731 [epoch: 89] train loss:0.0000 test accuracy:0.9719 [epoch: 90] train loss:0.0000 test accuracy:0.9719 [epoch: 91] train loss:0.0000 test accuracy:0.9719 [epoch: 92] train loss:0.0000 test accuracy:0.9731 [epoch: 93] train loss:0.0000 test accuracy:0.9719 [epoch: 94] train loss:0.0000 test accuracy:0.9719 [epoch: 95] train loss:0.0000 test accuracy:0.9719 [epoch: 96] train loss:0.0000 test accuracy:0.9719 [epoch: 97] train loss:0.0000 test accuracy:0.9719 [epoch: 98] train loss:0.0000 test accuracy:0.9719 [epoch: 99] train loss:0.0000 test accuracy:0.9719 [epoch: 100] train loss:0.0000 test accuracy:0.9707