#加載必要的庫
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import sys,os,caffe
#設置當前目錄
caffe_root = '/home/bnu/caffe/' 
sys.path.insert(0, caffe_root + 'python')
os.chdir(caffe_root)

 　　　　　　　　設置求解器，和c++/caffe一樣，需要一個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')

 　　　　　　如果不需要繪制曲線，只需要訓練出一個caffemodel, 直接調用solver.solve()就可以了。如果要繪制曲線，就需要把迭代過程中的值

保存下來，因此不能直接調用solver.solve(), 需要迭代。在迭代過程中，每迭代200次測試一次

%%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

 　　　　　　繪制train過程中的loss曲線，和測試過程中的accuracy曲線。

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]

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