目前，我正在使用两类数据训练网络…但在第一次迭代后准确率恒定为1！

输入数据是灰度图像。在创建HDF5Data时，两类图像都是随机选择的。

为什么会发生这种情况？哪里出了问题或有什么错误！

network.prototxt :

name: "brainMRI"layer {  name: "data"  type: "HDF5Data"  top: "data"  top: "label"  include: {    phase: TRAIN  }  hdf5_data_param {    source: "/home/shivangpatel/caffe/brainMRI1/train_file_location.txt"    batch_size: 10  }}layer {  name: "data"  type: "HDF5Data"  top: "data"  top: "label"  include: {    phase: TEST  }  hdf5_data_param {    source: "/home/shivangpatel/caffe/brainMRI1/test_file_location.txt"    batch_size: 10  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 20    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 50    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool2"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 2    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "softmax"  type: "Softmax"  bottom: "ip2"  top: "smip2"}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "ip2"  bottom: "label"  top: "loss"}layer {  name: "accuracy"  type: "Accuracy"  bottom: "smip2"  bottom: "label"  top: "accuracy"  include {    phase: TEST  }}

输出 :

I0217 17:41:07.912580  2913 net.cpp:270] This network produces output lossI0217 17:41:07.912607  2913 net.cpp:283] Network initialization done.I0217 17:41:07.912739  2913 solver.cpp:60] Solver scaffolding done.I0217 17:41:07.912789  2913 caffe.cpp:212] Starting OptimizationI0217 17:41:07.912813  2913 solver.cpp:288] Solving brainMRII0217 17:41:07.912832  2913 solver.cpp:289] Learning Rate Policy: invI0217 17:41:07.920737  2913 solver.cpp:341] Iteration 0, Testing net (#0)I0217 17:41:08.235076  2913 solver.cpp:409]     Test net output #0: accuracy = 0.98I0217 17:41:08.235194  2913 solver.cpp:409]     Test net output #1: loss = 0.0560832 (* 1 = 0.0560832 loss)I0217 17:41:35.831647  2913 solver.cpp:341] Iteration 100, Testing net (#0)I0217 17:41:36.140849  2913 solver.cpp:409]     Test net output #0: accuracy = 1I0217 17:41:36.140949  2913 solver.cpp:409]     Test net output #1: loss = 0.00757247 (* 1 = 0.00757247 loss)I0217 17:42:05.465395  2913 solver.cpp:341] Iteration 200, Testing net (#0)I0217 17:42:05.775877  2913 solver.cpp:409]     Test net output #0: accuracy = 1I0217 17:42:05.776000  2913 solver.cpp:409]     Test net output #1: loss = 0.0144996 (* 1 = 0.0144996 loss)..........................

回答：

总结评论中的一些信息：
– 您每隔test_interval:100次迭代进行一次测试。
– 每次测试间隔处理test_iter:5 * batch_size:10 = 50个样本。
– 您的训练集和测试集似乎非常整齐：所有负样本（标签=0）在所有正样本之前被分组在一起。

考虑您的SGD迭代求解器，您在训练期间以batch_size:10的批次喂入数据。您的训练集有14,746个负样本（即1474个批次），在任何正样本之前。因此，在前1474次迭代中，您的求解器只“看到”负样本，而没有正样本。
您期望这个求解器学到什么？

问题

您的求解器只看到负样本，因此学会了无论输入是什么都应该输出“0”。您的测试集也是按相同方式排序的，因此在每次test_interval只测试50个样本时，您只在测试集中的负样本上进行测试，结果准确率为完美的1。
但正如您所指出的，您的网络实际上什么也没学到。

解决方案

我想您现在已经猜到解决方案是什么了。您需要打乱您的训练集，并在您的整个测试集上测试您的网络。