caffe訓練過程中的視覺化
阿新 • • 發佈:2019-01-04
import matplotlib.pyplot as plt import caffe caffe.set_device(0) caffe.set_mode_gpu() # 使用SGDSolver,即隨機梯度下降演算法 solver = caffe.SGDSolver('/home/xxx/mnist/solver.prototxt') # 等價於solver檔案中的max_iter,即最大解算次數 niter = 10000 # 每隔100次收集一次loss資料 display= 100 # 每次測試進行100次解算 test_iter = 100 # 每500次訓練進行一次測試 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)) # 輔助變數 _train_loss = 0; _test_loss = 0; _accuracy = 0 # 進行解算 for it in range(niter): # 進行一次解算 solver.step(1) # 統計train loss _train_loss += solver.net.blobs['SoftmaxWithLoss1'].data if it % display == 0: # 計算平均train loss train_loss[it // display] = _train_loss / display _train_loss = 0 if it % test_interval == 0: for test_it in range(test_iter): # 進行一次測試 solver.test_nets[0].forward() # 計算test loss _test_loss += solver.test_nets[0].blobs['SoftmaxWithLoss1'].data # 計算test accuracy _accuracy += solver.test_nets[0].blobs['Accuracy1'].data # 計算平均test loss test_loss[it / test_interval] = _test_loss / test_iter # 計算平均test accuracy test_acc[it / test_interval] = _accuracy / test_iter _test_loss = 0 _accuracy = 0 # 繪製train loss、test loss和accuracy曲線 print '\nplot the train loss and test accuracy\n' _, ax1 = plt.subplots() ax2 = ax1.twinx() # train loss -> 綠色 ax1.plot(display * arange(len(train_loss)), train_loss, 'g') # test loss -> 黃色 ax1.plot(test_interval * arange(len(test_loss)), test_loss, 'y') # test accuracy -> 紅色 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()