caffe训练过程中的可视化

import matplotlib.pyplot as plt  import caffe   caffe.set_device(0)  caffe.set_mode_gpu()   # 使用SGDSolver，即随机梯度下降算法  solver = caffe.SGDSolver('/home/xxx/mnist/solver.prototxt')    # 等价于solver文件中的max_iter，即最大解算次数  niter = 10000 # 每隔100次收集一次loss数据  display= 100    # 每次测试进行100次解算 test_iter = 100# 每500次训练进行一次测试test_interval =500  #初始化 train_loss = zeros(ceil(niter * 1.0 / display))   test_loss = zeros(ceil(niter * 1.0 / test_interval))  test_acc = zeros(ceil(niter * 1.0 / test_interval))    # 辅助变量  _train_loss = 0; _test_loss = 0; _accuracy = 0  # 进行解算  for it in range(niter):      # 进行一次解算      solver.step(1)      # 统计train loss      _train_loss += solver.net.blobs['SoftmaxWithLoss1'].data      if it % display == 0:          # 计算平均train loss          train_loss[it // display] = _train_loss / display          _train_loss = 0        if it % test_interval == 0:          for test_it in range(test_iter):              # 进行一次测试              solver.test_nets[0].forward()              # 计算test loss              _test_loss += solver.test_nets[0].blobs['SoftmaxWithLoss1'].data              # 计算test accuracy              _accuracy += solver.test_nets[0].blobs['Accuracy1'].data          # 计算平均test loss          test_loss[it / test_interval] = _test_loss / test_iter          # 计算平均test accuracy          test_acc[it / test_interval] = _accuracy / test_iter          _test_loss = 0          _accuracy = 0    # 绘制train loss、test loss和accuracy曲线  print '\nplot the train loss and test accuracy\n'  _, ax1 = plt.subplots()  ax2 = ax1.twinx()    # train loss -> 绿色  ax1.plot(display * arange(len(train_loss)), train_loss, 'g')  # test loss -> 黄色  ax1.plot(test_interval * arange(len(test_loss)), test_loss, 'y')  # test accuracy -> 红色  ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')    ax1.set_xlabel('iteration')  ax1.set_ylabel('loss')  ax2.set_ylabel('accuracy')  plt.show()