本文使用anaconda的Spyder编译器绘制loss及accuracy曲线。在该编译环境下,建一个.py文件,点击run运行,可通过变量窗口查看各变量的值以及在命令行窗口生成曲线:
代码如下:
# -*- coding: utf-8 -*- """ Created on Tue Jul 19 16:22:22 2016 @author: root """ import numpy as np from numpy import * from numpy import zeros,arange from math import ceil import matplotlib.pyplot as plt import caffe #caffe.set_device(0) caffe.set_mode_cpu() # 使用SGDSolver,即随机梯度下降算法 solver = caffe.SGDSolver('D:/caffe/caffe-master/caffe-master/mnist/mnist/solver.prototxt') # 等价于solver文件中的max_iter,即最大解算次数 niter = 9380 # 每隔100次收集一次数据 display= 100 # 每次测试进行100次解算,10000/100 test_iter = 100 # 每500次训练进行一次测试(100次解算),60000/64 test_interval =938 #初始化 train_loss = np.zeros(int(np.ceil(niter * 1.0 / display))) test_loss = np.zeros( int(np.ceil(niter * 1.0 / test_interval))) test_acc = np.zeros( int(np.ceil(niter * 1.0 / test_interval))) # iteration 0,不计入 solver.step(1) # 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) # 每迭代一次,训练batch_size张图片 _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 * np.arange(len(train_loss)), train_loss, 'g') # test loss -> 黄色 ax1.plot(test_interval * np.arange(len(test_loss)), test_loss, 'y') # test accuracy -> 红色 ax2.plot(test_interval * np.arange(len(test_acc)), test_acc, 'r') ax1.set_xlabel('iteration') ax1.set_ylabel('loss') ax2.set_ylabel('accuracy') plt.show()