python-caffe接口学习（Solving in Python with LeNet）

参考官方链接：
http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/01-learning-lenet.ipynb

[(k, v.data.shape) for k, v in solver.net.blobs.items()]  //  利用此命令，可查看featuremap/* //       solver.net.blobs数据类型为字典类型，其中value值为blob类型，其中data存放featuremap数据[('data', (64, 1, 28, 28)), ('label', (64,)), ('conv1', (64, 20, 24, 24)), ('pool1', (64, 20, 12, 12)), ('conv2', (64, 50, 8, 8)), ('pool2', (64, 50, 4, 4)), ('fc1', (64, 500)), ('score', (64, 10)), ('loss', ())]*/

[(k, v[0].data.shape) for k, v in solver.net.params.items()]//同样，类型为字典/*[('conv1', (20, 1, 5, 5)), ('conv2', (50, 20, 5, 5)), ('fc1', (500, 800)), ('score', (10, 500))]*/

可视化滤波器或滤波器梯度参数：

solver.step(1)imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')

1、通过python可视化输入数据来看，训练、测试过程中的图像是顺序导入caffe中的，不是随机生成的样本，因此，后续在制作训练集的时候，要先打乱数据在最做lmdb数据。2、训练过程中的test是如何共享训练过程中的权重的？在step函数中，调用TestAll()，TestAll()--》Test()---> ShareTrainedLayersWith(net_.get());3、step(1) 其中，1表示迭代次数，而test在首次肯定会被调用，因此step(1) test和train都会调用

void Solver<Dtype>::Step(int iters) {  const int start_iter = iter_;  const int stop_iter = iter_ + iters;  int average_loss = this->param_.average_loss();  losses_.clear();  smoothed_loss_ = 0;  while (iter_ < stop_iter) {    // zero-init the params    net_->ClearParamDiffs();   /////Net 的成员变量param_ 中的diff开辟空间，并初始化为0     if (param_.test_interval() && iter_ % param_.test_interval() == 0        && (iter_ > 0 || param_.test_initialization())        && Caffe::root_solver()) {      TestAll();//-->Test()----> ShareTrainedLayersWith(net_.get());////////////      if (requested_early_exit_) {        // Break out of the while loop because stop was requested while testing.        break;      }    }    for (int i = 0; i < callbacks_.size(); ++i) {      callbacks_[i]->on_start();    }    const bool display = param_.display() && iter_ % param_.display() == 0;    net_->set_debug_info(display && param_.debug_info());    // accumulate the loss and gradient    Dtype loss = 0;    for (int i = 0; i < param_.iter_size(); ++i) {      loss += net_->ForwardBackward();    }    loss /= param_.iter_size();    // average the loss across iterations for smoothed reporting    UpdateSmoothedLoss(loss, start_iter, average_loss);    if (display) {      LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_   //////////////// Iteration, loss=          << ", loss = " << smoothed_loss_;      const vector<Blob<Dtype>*>& result = net_->output_blobs();      int score_index = 0;      for (int j = 0; j < result.size(); ++j) {        const Dtype* result_vec = result[j]->cpu_data();        const string& output_name =            net_->blob_names()[net_->output_blob_indices()[j]];        const Dtype loss_weight =            net_->blob_loss_weights()[net_->output_blob_indices()[j]];        for (int k = 0; k < result[j]->count(); ++k) {      //////Train net out///////////////          ostringstream loss_msg_stream;          if (loss_weight) {            loss_msg_stream << " (* " << loss_weight                            << " = " << loss_weight * result_vec[k] << " loss)";          }          LOG_IF(INFO, Caffe::root_solver()) << "    Train net output #"              << score_index++ << ": " << output_name << " = "              << result_vec[k] << loss_msg_stream.str();        }      }    }//if(display)    for (int i = 0; i < callbacks_.size(); ++i) {      callbacks_[i]->on_gradients_ready();    }    ApplyUpdate();  ////////////////////////////////////// Iteration 0, lr==XXXX    // Increment the internal iter_ counter -- its value should always indicate    // the number of times the weights have been updated.    ++iter_;    SolverAction::Enum request = GetRequestedAction();    // Save a snapshot if needed.    if ((param_.snapshot()         && iter_ % param_.snapshot() == 0         && Caffe::root_solver()) ||         (request == SolverAction::SNAPSHOT)) {      Snapshot();    }    if (SolverAction::STOP == request) {      requested_early_exit_ = true;      // Break out of training loop.      break;    }// if()  }// end while} // end step()

结果分析