前天残差中加不进去bn层昨天残差中加不进去两个bn层昨夜残差中可以用两个bn层而且训练正常一测试就闪退什么鬼测试函数如下int last[10000];float test( LeNet net, cudaStream_t stream, cifar10DatasetTEST Dataset) {//使用10000float *d_inputs;unsigned *d_labels;for (int x 0; x10000; x){last[x] 0;}//输出的预测概率softmax后的结果损失预测概率的梯度作为LeNet反向传播的输入float *d_logits, *d_prob, *loss, *dlogits;int num_classes 10;cudaMalloc(d_inputs, 5 * 32 * 32 * sizeof(float));/*cudaMalloc(d_inputs, 3* 32*32 * sizeof(float));*/cudaMalloc(d_labels, 1*sizeof(unsigned));cudaMalloc(d_logits, 1*10*sizeof(float));cudaMalloc(d_prob, 1*10*sizeof(float));cudaMalloc(loss, 1*sizeof(float));cudaMalloc(dlogits, 1*10*sizeof(float));std::vectorfloat h_loss(1); std::vectorfloat h_prob(10);for (int epoch 0; epoch10000; epoch) {auto batch Dataset.next_batch(1);std::vectorunsigned h_labels(1);//取了一个图像一个标签也可以是train数据取10000for (int batch_idx 0; batch_idx1; batch_idx) {cudaMemcpy(d_inputs batch_idx * 32 * 32 * 5, batch[batch_idx].image.data(), 32 * 32 * 5 * sizeof(float), cudaMemcpyHostToDevice);/*cudaMemcpy(d_inputs batch_idx * 32 * 32 * 3, batch[batch_idx].image.data(), 32 * 32 * 3 * sizeof(float), cudaMemcpyHostToDevice);*/h_labels[batch_idx] batch[batch_idx].label;}cudaMemcpy(d_labels, h_labels.data(),1*sizeof(unsigned), cudaMemcpyHostToDevice);/* net.forward(d_inputs);*/net.forward2(d_inputs);cudaMemcpy(d_logits, net.get_output(), 1*10*sizeof(float), cudaMemcpyDeviceToDevice);softmax_forward_loss_batch 1, 1 (d_logits, d_labels, d_prob, loss, 1, num_classes);cudaStreamSynchronize(stream);// ---- 打印中间输出 ----cudaMemcpy(h_loss.data(), loss, 1*sizeof(float), cudaMemcpyDeviceToHost);cudaMemcpy(h_prob.data(), d_prob, 1 * 10 * sizeof(float), cudaMemcpyDeviceToHost);int ans 0;for (int i 1; i 10; i)if (h_prob[i] h_prob[ans]) ans i;//找到最大概率标号if (h_labels[0] ans)last[epoch] 1;//找出最大概率是不是与标签同同则ok//否则失败///* float avg_loss 0.f;// for (float l : h_loss) avg_loss l;// avg_loss / 1;*/// if (epoch % 1 0) { // 每个 epoch 打印一次// std::cout Count epoch//// 损失 h_loss[0]// max概率 h_prob[ans]// max响应标签 ans// (实际标签 h_labels[0] )// std::endl;// }}cudaFree(d_inputs);cudaFree(d_labels);cudaFree(d_logits);cudaFree(d_prob);cudaFree(loss);cudaFree(dlogits);float junzhi 0;int score 0;for (int i 0; i 10000; i){if (last[i] 1)score;}junzhi score / (float)100;std::cout test score junzhi std::endl;return junzhi;}因为是x64release实现很难debug最后改的一团糟而且上面程序一直正常运行当然刚开始我不敢想其他问题只能闷着头皮找自己的问题春天出去溜达一圈想了想可不可以像训练一样批处理出成绩这个注意好立马改void test2(int epochs, int batch_size, LeNet net, cudaStream_t stream, cifar10DatasetTEST Dataset) {float *d_inputs;unsigned *d_labels;//输出的预测概率softmax后的结果损失预测概率的梯度作为LeNet反向传播的输入float *d_logits, *d_prob, *loss, *dlogits;int num_classes 10;//cudaMalloc(d_inputs, batch_size * 32*32 *3 * sizeof(float));cudaMalloc(d_inputs, batch_size * 32 * 32 * 5 * sizeof(float));cudaMalloc(d_labels, batch_size*sizeof(unsigned));cudaMalloc(d_logits, batch_size*num_classes*sizeof(float));cudaMalloc(d_prob, batch_size*num_classes*sizeof(float));cudaMalloc(loss, batch_size*sizeof(float));cudaMalloc(dlogits, batch_size*num_classes*sizeof(float));std::vectorfloat h_loss(batch_size);int num_errorsTRain 0;auto t1 std::chrono::high_resolution_clock::now();for (int epoch 0; epochepochs; epoch) {auto batch Dataset.next_batch(batch_size);std::vectorunsigned h_labels(batch_size);for (int batch_idx 0; batch_idxbatch_size; batch_idx) {/*cudaMemcpy(d_inputs batch_idx * 32*32 * 3, batch[batch_idx].image.data(), 32 * 32 * 3 * sizeof(float), cudaMemcpyHostToDevice);*/cudaMemcpy(d_inputs batch_idx * 32 * 32 * 5, batch[batch_idx].image.data(), 32 * 32 * 5 * sizeof(float), cudaMemcpyHostToDevice);h_labels[batch_idx] batch[batch_idx].label;}cudaMemcpy(d_labels, h_labels.data(),batch_size*sizeof(unsigned), cudaMemcpyHostToDevice);net.forward2(d_inputs);cudaMemcpy(d_logits, net.get_output(), batch_size*num_classes*sizeof(float), cudaMemcpyDeviceToDevice);softmax_forward_loss_batch batch_size, 1 (d_logits, d_labels, d_prob, loss, batch_size, num_classes);cudaStreamSynchronize(stream);// ---- 打印中间输出 ----std::vectorfloat h_prob(10 * batch_size);cudaMemcpy(h_prob.data(), d_prob, batch_size * 10 * sizeof(float), cudaMemcpyDeviceToHost);for (int b 0; b batch_size; b){int ans 0;for (int i 1; i 10; i)if (h_prob[i b * 10] h_prob[ans b * 10]) ans i;//找到最大概率标号if (h_labels[0 b] ans)// last[epoch] 1;num_errorsTRain;}//// ---- 打印中间输出 ----//cudaMemcpy(h_loss.data(), loss, batch_size*sizeof(float), cudaMemcpyDeviceToHost);//float avg_loss 0.f;//for (float l : h_loss) avg_loss l;//avg_loss / batch_size;////if (epoch % 1 0) { // 每个 epoch 打印一次//// std::cout [Rank rank ] Epoch epoch//// Avg Loss avg_loss//// (first label h_labels[0] )//// std::endl;////}}auto t2 std::chrono::high_resolution_clock::now();printf(时间: %f ms\n, std::chrono::duration_caststd::chrono::microseconds(t2 - t1).count() / 1000.0f);printf(Test Classification result: %.2f%% ok (used %d images)\n, ((float)num_errorsTRain / (batch_size*epochs)) * 100.0f, (int)batch_size*epochs);//float *Junzhi get_u();//float *fangcha get_var();//运行输出值//for (int jf 0; jf 32; jf)// printf(均值: %f,方差:%f \n, Junzhi[jf], fangcha[jf]);cudaFree(d_inputs);cudaFree(d_labels);cudaFree(d_logits);cudaFree(d_prob);cudaFree(loss);cudaFree(dlogits);}调用如下test2(156, batch_size, LeNet_net, stream, DatasetsTest);//样本10000/64batch156轮竟然测试ok了这个是再mx550显卡上这是一种办法另外我把源程序在另一台电脑1060显卡上运行也是win10cuda9.0cudnn7.1.4老办法也成功了但1060显卡用不了batch64训练时改成batch32测试仍然用单样本说明什么问题方法总比问题多