我已经实现了一个带有图像作为输入和 101 个类作为输出的 CNN。在将输入作为网络输入之前，我已经对输入应用了均值减法和归一化。
我还尝试了多个优化器（ADAM 和 SGD），还改变了学习率（0.1,0.01,0,001）和批量大小（19,32,64,128,200）。但在所有这些案例中，我都观察到了类似的模式。
我不认为网络正在学习更多，更重要的是在几个值之间波动的准确性。
我最初认为这是因为批量大小，但我错了。我已经使用 caffe 构建并训练了具有相同输入的完全相同的网络。并达到了 55% 的准确率（检查网络模型的工作情况）。
我的猜测是实现在某个地方出错，但不确定在哪里。
我的代码
输出
这个特定的输出是 lr=0.001 batch_size=200 和 ADAM 优化器
Iter 2000, Minibatch Loss= 12560.197266, Training Accuracy= 0.01000
Testing Accuracy: 0.025
Iter 4000, Minibatch Loss= 4587.021484, Training Accuracy= 0.00500
Testing Accuracy: 0.005
Iter 6000, Minibatch Loss= 2196.316406, Training Accuracy= 0.02000
Testing Accuracy: 0.02
Iter 8000, Minibatch Loss= 918.784119, Training Accuracy= 0.01500
Testing Accuracy: 0.0
Iter 10000, Minibatch Loss= 315.327911, Training Accuracy= 0.01000
Testing Accuracy: 0.0
Iter 12000, Minibatch Loss= 102.744026, Training Accuracy= 0.01000
Testing Accuracy: 0.01
Iter 14000, Minibatch Loss= 59.417763, Training Accuracy= 0.00500
Testing Accuracy: 0.005
Iter 16000, Minibatch Loss= 33.026432, Training Accuracy= 0.00500
Testing Accuracy: 0.02
Iter 18000, Minibatch Loss= 30.361868, Training Accuracy= 0.02000
Testing Accuracy: 0.005
Iter 20000, Minibatch Loss= 33.944790, Training Accuracy= 0.01000
Testing Accuracy: 0.02
Iter 22000, Minibatch Loss= 15.029477, Training Accuracy= 0.02000
Testing Accuracy: 0.02
Iter 24000, Minibatch Loss= 13.751340, Training Accuracy= 0.02500
Testing Accuracy: 0.02
Iter 26000, Minibatch Loss= 9.085027, Training Accuracy= 0.01500
Testing Accuracy: 0.015
Iter 28000, Minibatch Loss= 14.407433, Training Accuracy= 0.02000
Testing Accuracy: 0.02
Iter 30000, Minibatch Loss= 11.090684, Training Accuracy= 0.02500
Testing Accuracy: 0.02
Iter 32000, Minibatch Loss= 9.385777, Training Accuracy= 0.02000
Testing Accuracy: 0.03
Iter 34000, Minibatch Loss= 12.902111, Training Accuracy= 0.01500
Testing Accuracy: 0.02
Iter 36000, Minibatch Loss= 5.580408, Training Accuracy= 0.02000
Testing Accuracy: 0.005
Iter 38000, Minibatch Loss= 11.641799, Training Accuracy= 0.02000
Testing Accuracy: 0.03
Iter 40000, Minibatch Loss= 6.941562, Training Accuracy= 0.01500
Testing Accuracy: 0.02
Iter 42000, Minibatch Loss= 6.781301, Training Accuracy= 0.01000
Testing Accuracy: 0.0
Iter 44000, Minibatch Loss= 6.840708, Training Accuracy= 0.01000
Testing Accuracy: 0.01
Iter 46000, Minibatch Loss= 5.501908, Training Accuracy= 0.01000
Testing Accuracy: 0.03
Iter 48000, Minibatch Loss= 4.667253, Training Accuracy= 0.03000
Testing Accuracy: 0.015
Iter 50000, Minibatch Loss= 4.613351, Training Accuracy= 0.01000
Testing Accuracy: 0.01
Iter 52000, Minibatch Loss= 7.234328, Training Accuracy= 0.02000
Testing Accuracy: 0.01
Iter 54000, Minibatch Loss= 8.120478, Training Accuracy= 0.02500
Testing Accuracy: 0.01
Iter 56000, Minibatch Loss= 6.804164, Training Accuracy= 0.01000
Testing Accuracy: 0.01
Iter 58000, Minibatch Loss= 5.060484, Training Accuracy= 0.03000
Testing Accuracy: 0.02
Iter 60000, Minibatch Loss= 4.937673, Training Accuracy= 0.02000
Testing Accuracy: 0.015
Iter 62000, Minibatch Loss= 8.758166, Training Accuracy= 0.01000
Testing Accuracy: 0.02
Iter 64000, Minibatch Loss= 4.616434, Training Accuracy= 0.01500
Testing Accuracy: 0.025