Caffe 实例笔记 3 Brewing Logistic Regression then Going Deeper

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In this example, we will
1. use caffe as a generic SGD optimizer to train logistic regression ,
2. and then we will add layers to improve accuracy.

1 classify with sklearn

shutil - Utility functions for copying and archiving files and directory trees.

Note the following function when generate a binary classification:

sklearn.datasets.make_classification(...)sklearn.cross_validation.train_test_split(X,y)ind=np.random.permutation(X.shape[0])[:1000]df=pd.DataFrame(X[ind])_=pd.scatter_matrix(df,figsize=(9,9),diagonal='kde',marker='o',s=40,alpha=0.4,c=y[ind])#alpha=transparencyclf = sklearn.linear_model.SGDClassifier(...)clf.fit(X, y)yt_pred = clf.predict(Xt)print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))

we can get the result like this:
accuracy:0.772
accuracy:0.772
accuracy:0.772
accuracy:0.772
1 loops, best of 3: 443 ms per loop

2 use caffe

2.1 save dataset to HDF5

?

dirname=os.path.abspath('/home/beatree/caffe-rc3/examples/hdf5_classification/data')if not os.path.exists(dirname):    os.makedirs(dirname)train_filename=os.path.join(dirname,'train.h5')test_filename=os.path.join(dirname,'test.h5')with h5py.File(train_filename,'w')as f:    f['data']=X    f['label']=y.astype(np.float32)with open(os.path.join(dirname,'train.txt'),'w') as f:    f.write(train_filename+'\n')#why list the same data file twice???    f.write(train_filename+'\n')comp_kwargs={'compression':'gzip','compression_opts':1}with h5py.File(test_filename,'w')as f:     f.create_dataset('data',data=Xt,**comp_kwargs)    f.create_dataset('label',data=yt.astype(np.float32),**comp_kwargs)with open (os.path.join(dirname,'test.txt'),'w')as f:    f.write(test_filename+'\n')

2.2 prottobuf model(shallow)

from caffe import layers as Lfrom caffe import params as Pdef logreg(hdf5,batch_size):    n=caffe.NetSpec()    n.data,n.label=L.HDF5Data(batch_size=batch_size,source=hdf5,ntop=2)    n.ip1=L.InnerProduct(n.data,num_output=2,weight_filler=dict(type='xavier'))    n.accuracy=L.Accuracy(n.ip1,n.label)    n.loss=L.SoftmaxWithLoss(n.ip1,n.label)    return n.to_proto()train_net_path='/home/beatree/caffe-rc3/examples/hdf5_classification/logreg_auto_train.prototxt'test_net_path='/home/beatree/caffe-rc3/examples/hdf5_classification/logreg_auto_test.prototxt'with open(train_net_path,'w')as f:    f.write(str(logreg('/home/beatree/caffe-rc3/examples/hdf5_classification/data/train.txt',10)))with open(test_net_path,'w')as f:    f.write(str(logreg('/home/beatree/caffe-rc3/examples/hdf5_classification/data/test.txt',10)))

solver.prototxt

from caffe.proto import caffe_pb2def solver(train_net_path,test_net_path):    s= caffe_pb2.SolverParameter()    s.train_net= train_net_path    s.test_net.append(test_net_path)    s.test_interval=1000    s.test_iter.append(250)    s.max_iter=10000    s.base_lr=0.01    s.lr_policy='step'    s.gamma=0.1    s.stepsize=5000    s.momentum=0.9    s.weight_decay=5e-4    s.display=1000    s.snapshot=10000    s.snapshot_prefix='/home/beatree/caffe-rc3/examples/hdf5_classification/data/train'    s.solver_mode=caffe_pb2.SolverParameter.CPU    return ssolver_path='/home/beatree/caffe-rc3/examples/hdf5_classification/logreg_solver.protxt'with open (solver_path,'w') as f:    f.write(str(solver(train_net_path,test_net_path)))

%%timeitcaffe.set_mode_cpu()solver=caffe.get_solver(solver_path)solver.solve()accuracy=0batch_size=solver.test_nets[0].blobs['data'].numtest_iters=int(len(Xt)/batch_size)for i in range (test_iters):    solver.test_nets[0].forward()    accuracy+=solver.test_nets[0].blobs['accuracy'].dataaccuracy/=test_itersprint ('accuracy:{:.3}'.format(accuracy))

accuracy:0.771
accuracy:0.771
10 loops, best of 3: 156 ms per loop

2.3 deeper model

from caffe import layers as Lfrom caffe import params as Pdef nonlinear_net(hdf5,batch_size):    n=caffe.NetSpec()    n.data,n.label=L.HDF5Data(batch_size=batch_size,source=hdf5,ntop=2)    n.ip1=L.InnerProduct(n.data,num_output=40,weight_filler=dict(type='xavier'))    n.relu1=L.ReLU(n.ip1,in_place=True)    n.ip2=L.InnerProduct(n.relu1,num_output=2,weight_filler=dict(type='xavier'))    n.accuracy=L.Accuracy(n.ip2,n.label)    n.loss=L.SoftmaxWithLoss(n.ip2,n.label)    return n.to_proto()train_net_path='/home/beatree/caffe-rc3/examples/hdf5_classification/nonlinear_auto_train.prototxt'test_net_path='/home/beatree/caffe-rc3/examples/hdf5_classification/nonlinear_auto_test.prototxt'with open(train_net_path,'w')as f:    f.write(str(nonlinear_net('/home/beatree/caffe-rc3/examples/hdf5_classification/data/train.txt',10)))with open(test_net_path,'w')as f:    f.write(str(nonlinear_net('/home/beatree/caffe-rc3/examples/hdf5_classification/data/test.txt',10)))solver_path='/home/beatree/caffe-rc3/examples/hdf5_classification/nonliear_logreg_solver.prototxt'with open (solver_path,'w') as f:    f.write(str(solver(train_net_path,test_net_path)))

%%timeitcaffe.set_mode_cpu()solver=caffe.get_solver(solver_path)solver.solve()accuracy=0batch_size=solver.test_nets[0].blobs['data'].numtest_iters=int(len(Xt)/batch_size)for i in range (test_iters):    solver.test_nets[0].forward()    accuracy+=solver.test_nets[0].blobs['accuracy'].dataaccuracy/=test_itersprint ('acc:{:.3}'.format(accuracy))

acc:0.827
acc:0.826
acc:0.828
acc:0.829
1 loops, best of 3: 254 ms per loop

we get a higher accuracy!
finally,if you want to clean up the dir

shutil.remtree(dirname)