引言

记录学习官网的例程中的一些重要语句，遇到的问题等，内容分散，建议顺序查看。
主要是调用Caffe的Python接口
源文件就在{caffe_root}/examples中(目录下面的中文标题也附有链接)，安装sudo pip install jupyter打开即可运行，初学者最好是放在它指定的目录，如，否则要改很多路径。
注：eaxmples是用jupyter notebook写的，部分Cell中出现了一些特殊的用法：
1. 感叹号‘！’：用于执行系统命令，如 !pwd
2. 百分号‘%’：用法太多，如 %matplotlib inline 显示绘图窗口 详见Jupyter Notebook Viewer

目录

构建逻辑回归

虽然Caffe是为深度网络而生但是也可以构建浅层模型，像用于分类的逻辑回归。

对合成数据进行逻辑回归，保存为HDF5作为Caffe的输入向量

• 合成一个数据集，其中含有10000个4维向量（两个信息特征，两个噪声特征）用于二分类

  X, y = sklearn.datasets.make_classification(n_samples=10000, n_features=4, n_redundant=0, n_informative=2, n_clusters_per_class=2, hypercube=False, random_state=0
  )# Split into train and testX, Xt, y, yt = sklearn.model_selection.train_test_split(X, y)# Visualize sample of the dataind = np.random.permutation(X.shape[0])[:1000]
  df = pd.DataFrame(X[ind])
  _ = pd.plotting.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])

• 用通过随机梯度下降（SGD）训练学习和评估sklearn的逻辑回归并检查分类器的准确性。

  
  # Train and test the scikit-learn SGD logistic regression.clf = sklearn.linear_model.SGDClassifier(loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='balanced')clf.fit(X, y)
  yt_pred = clf.predict(Xt)
  print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))

• 保存数据集为HDF5文件， 用于Caffe输入。

  
  # Write out the data to HDF5 files in a temp directory.# This file is assumed to be caffe_root/examples/hdf5_classification.ipynbdirname = os.path.abspath('./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')# HDF5DataLayer source should be a file containing a list of HDF5 filenames.# To show this off, we'll list the same data file twice.with h5py.File(train_filename, 'w') as f:f['data'] = Xf['label'] = y.astype(np.float32)
  with open(os.path.join(dirname, 'train.txt'), 'w') as f:f.write(train_filename + '\n')f.write(train_filename + '\n')# HDF5 is pretty efficient, but can be further compressed.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')

通过Python接口在Caffe中定义逻辑回归模型

• 定义网络模型

  from caffe import layers as L
  from caffe import params as Pdef logreg(hdf5, batch_size):# logistic regression: data, matrix multiplication, and 2-class softmax lossn = 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 = 'examples/hdf5_classification/logreg_auto_train.prototxt'
  with open(train_net_path, 'w') as f:f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'
  with open(test_net_path, 'w') as f:f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))

• 定义网络的求解器

  from caffe.proto import caffe_pb2def solver(train_net_path, test_net_path):s = caffe_pb2.SolverParameter()# Specify locations of the train and test networks.s.train_net = train_net_paths.test_net.append(test_net_path)s.test_interval = 1000  # Test after every 1000 training iterations.s.test_iter.append(250) # Test 250 "batches" each time we test.s.max_iter = 10000      # # of times to update the net (training iterations)# Set the initial learning rate for stochastic gradient descent (SGD).s.base_lr = 0.01        # Set `lr_policy` to define how the learning rate changes during training.# Here, we 'step' the learning rate by multiplying it by a factor `gamma`# every `stepsize` iterations.s.lr_policy = 'step's.gamma = 0.1s.stepsize = 5000# Set other optimization parameters. Setting a non-zero `momentum` takes a# weighted average of the current gradient and previous gradients to make# learning more stable. L2 weight decay regularizes learning, to help prevent# the model from overfitting.s.momentum = 0.9s.weight_decay = 5e-4# Display the current training loss and accuracy every 1000 iterations.s.display = 1000# Snapshots are files used to store networks we've trained. Here, we'll# snapshot every 10K iterations -- just once at the end of training.# For larger networks that take longer to train, you may want to set# snapshot < max_iter to save the network and training state to disk during# optimization, preventing disaster in case of machine crashes, etc.s.snapshot = 10000s.snapshot_prefix = 'examples/hdf5_classification/data/train'# We'll train on the CPU for fair benchmarking against scikit-learn.# Changing to GPU should result in much faster training!s.solver_mode = caffe_pb2.SolverParameter.CPUreturn ssolver_path = 'examples/hdf5_classification/logreg_solver.prototxt'
  with open(solver_path, 'w') as f:f.write(str(solver(train_net_path, test_net_path)))

用Python接口学习和评估Caffe构建的逻辑回归模型

• 对上面定义的逻辑回归模型进行评估

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

• 也可以用命令行接口：!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt

改进逻辑回归模型

• 上面这个模型是一个简单的逻辑回归，可以通过在输入、输出两层之间添加一个非线性层改善性能。

  from caffe import layers as L
  from caffe import params as Pdef nonlinear_net(hdf5, batch_size):# one small nonlinearity, one leap for model kindn = caffe.NetSpec()n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)# define a hidden layer of dimension 40n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))# transform the output through the ReLU (rectified linear) non-linearityn.relu1 = L.ReLU(n.ip1, in_place=True)# score the (now non-linear) featuresn.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))# same accuracy and loss as beforen.accuracy = L.Accuracy(n.ip2, n.label)n.loss = L.SoftmaxWithLoss(n.ip2, n.label)return n.to_proto()train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'
  with open(train_net_path, 'w') as f:f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'
  with open(test_net_path, 'w') as f:f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'
  with open(solver_path, 'w') as f:f.write(str(solver(train_net_path, test_net_path)))

• 对改进的模型进行学习和评估，最终精度有提升。

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

• 同样也可以用命令行：!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt

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