Caffe——绘制loss和accuracy曲线

这里我们在ipython notebook中绘制曲线

#加载必要的库
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import sys,os,caffe
#设置当前目录
caffe_root = '/home/bnu/caffe/' 
sys.path.insert(0, caffe_root + 'python')
os.chdir(caffe_root)

设置求解其，和C++/caffe一样，需要一个solver配置文件

# set the solver prototxt
caffe.set_device(0)
caffe.set_mode_gpu()
solver = caffe.SGDSolver('examples/cifar10/cifar10_quick_solver.prototxt')

如果不需要绘制曲线，只需要训练出一个caffemodel，直接调用solver.solve()就可以了。如果要绘制曲线，就需要把迭代过程中的值保存下来，因此不能直接调用solver.solve()，需要迭代。在迭代过程中，每迭代200次测试一次。

%%time
niter =4000
test_interval = 200
train_loss = np.zeros(niter)
test_acc = np.zeros(int(np.ceil(niter / test_interval)))

# the main solver loop
for it in range(niter):
    solver.step(1)  # SGD by Caffe

    # store the train loss
    train_loss[it] = solver.net.blobs['loss'].data
    solver.test_nets[0].forward(start='conv1')

    if it % test_interval == 0:
        acc=solver.test_nets[0].blobs['accuracy'].data
        print 'Iteration', it, 'testing...','accuracy:',acc
        test_acc[it // test_interval] = acc

Iteration 0 testing… accuracy: 0.10000000149
Iteration 200 testing… accuracy: 0.419999986887
Iteration 400 testing… accuracy: 0.479999989271
Iteration 600 testing… accuracy: 0.540000021458
Iteration 800 testing… accuracy: 0.620000004768
Iteration 1000 testing… accuracy: 0.629999995232
Iteration 1200 testing… accuracy: 0.649999976158
Iteration 1400 testing… accuracy: 0.660000026226
Iteration 1600 testing… accuracy: 0.660000026226
Iteration 1800 testing… accuracy: 0.670000016689
Iteration 2000 testing… accuracy: 0.709999978542
Iteration 2200 testing… accuracy: 0.699999988079
Iteration 2400 testing… accuracy: 0.75
Iteration 2600 testing… accuracy: 0.740000009537
Iteration 2800 testing… accuracy: 0.769999980927
Iteration 3000 testing… accuracy: 0.75
Iteration 3200 testing… accuracy: 0.699999988079
Iteration 3400 testing… accuracy: 0.740000009537
Iteration 3600 testing… accuracy: 0.72000002861
Iteration 3800 testing… accuracy: 0.769999980927
CPU times: user 41.7 s, sys: 54.2 s, total: 1min 35s
Wall time: 1min 18s
绘制train过程中的loss曲线，和测试过程中的accuracy曲线

print test_acc
_, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(np.arange(niter), train_loss)
ax2.plot(test_interval * np.arange(len(test_acc)), test_acc, 'r')
ax1.set_xlabel('iteration')
ax1.set_ylabel('train loss')
ax2.set_ylabel('test accuracy')

[ 0.1 0.41999999 0.47999999 0.54000002 0.62 0.63
0.64999998 0.66000003 0.66000003 0.67000002 0.70999998 0.69999999
0.75 0.74000001 0.76999998 0.75 0.69999999 0.74000001
0.72000003 0.76999998]