使用 Caffe Python 编写 LeNet

前言:

本文翻译自 Solving in Python with LeNet，基于深度学习框架 Caffe 的应用，运行本代码的前提是：

1. 安装了Caffe，windows Caffe安装教程以及添加Python接口请参考Caffe安装 和编译Caffe Python接口

2. 安装 Python 2.7，推荐使用Anaconda安装。安装完以后启动Jupyter Notebook

注：为正确运行程序，本文代码对原文代码有改动。

正文：

1. 开始 （Setup）

导入 pylab，画图使用。

from pylab import *

导入 caffe，添加系统变量。

caffe_root = 'D:/wincaffe/caffe-master/'  # 这是我的caffe 根目录import syssys.path.insert(0, caffe_root + 'python')import caffe

下载数据，下载好的数据请放在 D:\wincaffe\caffe-master\examples\mnist\mnist_data 目录下，我使用的是绝对路径。mnist 二进制文件需要转换为lmdb格式，我将原mnist文件和 lmdb 文件放在百度网盘mnist+lmdb (密码：h7wh)上了，不会转换的可以下载。

2.创建网络（Creating the net）

现在我们创建一个源自1989年的经典卷积网络结构 LeNet 的变体。
我们需要两个额外的文件方便输出：

• net prototxt：定义了指向 train/test 数据的网络结构。
• solver prototxt : 定义了网络学习参数。

from caffe import layers as L, params as Pdef lenet(lmdb, batch_size):    # our version of LeNet: a series of linear and simple nonlinear transformations    n = caffe.NetSpec()    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,                             transform_param=dict(scale=1./255), ntop=2)    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)    n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))    n.relu1 = L.ReLU(n.fc1, in_place=True)    n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))    n.loss =  L.SoftmaxWithLoss(n.score, n.label)    return n.to_proto()with open('D:/wincaffe/caffe-master/examples/mnist/lenet_auto_train.prototxt', 'w') as f:    f.write(str(lenet('D:/wincaffe/caffe-master/examples/mnist/mnist_data/mnist_train_lmdb', 64)))with open('D:/wincaffe/caffe-master/examples/mnist/lenet_auto_test.prototxt', 'w') as f:    f.write(str(lenet('D:/wincaffe/caffe-master/examples/mnist/mnist_data/mnist_test_lmdb', 100)))

让我们看一下 train net 的结构:

layer {  name: "data"  type: "Data"  top: "data"  top: "label"  transform_param {    scale: 0.00392156862745  }  data_param {    source: "D:/wincaffe/caffe-master/examples/mnist/mnist_data/mnist_train_lmdb"    batch_size: 64    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  convolution_param {    num_output: 20    kernel_size: 5    weight_filler {      type: "xavier"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  convolution_param {    num_output: 50    kernel_size: 5    weight_filler {      type: "xavier"    }  }}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "fc1"  type: "InnerProduct"  bottom: "pool2"  top: "fc1"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "fc1"  top: "fc1"}layer {  name: "score"  type: "InnerProduct"  bottom: "fc1"  top: "score"  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "score"  bottom: "label"  top: "loss"}

还有 test net 的结构:

layer {  name: "data"  type: "Data"  top: "data"  top: "label"  transform_param {    scale: 0.00392156862745  }  data_param {    source: "D:/wincaffe/caffe-master/examples/mnist/mnist_data/mnist_test_lmdb"    batch_size: 100    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  convolution_param {    num_output: 20    kernel_size: 5    weight_filler {      type: "xavier"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  convolution_param {    num_output: 50    kernel_size: 5    weight_filler {      type: "xavier"    }  }}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "fc1"  type: "InnerProduct"  bottom: "pool2"  top: "fc1"  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "fc1"  top: "fc1"}layer {  name: "score"  type: "InnerProduct"  bottom: "fc1"  top: "score"  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "score"  bottom: "label"  top: "loss"}

嗯，再给你看看solver的结构。这个文件在 D:\wincaffe\caffe-master\examples\mnist 中本来就存在。不过你需要根据路径自己修改一下代码中 train_net，test_net的路径。

# The train/test net protocol buffer definitiontrain_net: "D:/wincaffe/caffe-master/examples/mnist/lenet_auto_train.prototxt"test_net: "D:/wincaffe/caffe-master/examples/mnist/lenet_auto_test.prototxt"# test_iter specifies how many forward passes the test should carry out.# In the case of MNIST, we have test batch size 100 and 100 test iterations,# covering the full 10,000 testing images.test_iter: 100# Carry out testing every 500 training iterations.test_interval: 500# The base learning rate, momentum and the weight decay of the network.base_lr: 0.01momentum: 0.9weight_decay: 0.0005# The learning rate policylr_policy: "inv"gamma: 0.0001power: 0.75# Display every 100 iterationsdisplay: 100# The maximum number of iterationsmax_iter: 10000# snapshot intermediate resultssnapshot: 5000snapshot_prefix: "mnist/lenet"

3.加载和验证solver（Loading and checking the solver）

译者注：选择设备和使用GPU。（没有安装CUDA+cuDnn的可以使用caffe.set_mode_cpu()，亲测，速度慢成屎。）

caffe.set_device(0) #选择默认gpucaffe.set_mode_gpu()    #使用gpu### load the solver and create train and test netssolver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)solver = caffe.SGDSolver('C:/Users/Admin512/Desktop/MyStudy/caffe_python/LeNet/mnist/lenet_auto_solver.prototxt')

看一下中间变量的维度：

代码1：

# each output is (batch size, feature dim, spatial dim)[(k, v.data.shape) for k, v in solver.net.blobs.items()]

输出1：

[('data', (64L, 1L, 28L, 28L)), ('label', (64L,)), ('conv1', (64L, 20L, 24L, 24L)), ('pool1', (64L, 20L, 12L, 12L)), ('conv2', (64L, 50L, 8L, 8L)), ('pool2', (64L, 50L, 4L, 4L)), ('fc1', (64L, 500L)), ('score', (64L, 10L)), ('loss', ())]

---

代码2：

# just print the weight sizes (we'll omit the biases)[(k, v[0].data.shape) for k, v in solver.net.params.items()]

输出2：

[('conv1', (20, 1, 5, 5)), ('conv2', (50, 20, 5, 5)), ('fc1', (500, 800)), ('score', (10, 500))]

---

训练前，让我们检查一下是否一切就绪：

代码1：

solver.net.forward()  # train netsolver.test_nets[0].forward()  # test net (there can be more than one)

输出1：

{'loss': array(2.365971088409424, dtype=float32)}

---

代码2：

# we use a little trick to tile the first eight imagesimshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')print 'train labels:', solver.net.blobs['label'].data[:8]

输出2：

train labels: [ 5.  0.  4.  1.  9.  2.  1.  3.]

---

代码3：

imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]

输出3：

test labels: [ 7.  2.  1.  0.  4.  1.  4.  9.]

4.做一步solver（Stepping the solver）

训练网络和测试网看起来都在加载数据，并拥有正确的标签。

• 让我们做一步 minibatch-SGD ，看一下发生了什么：

solver.step(1)

---

我们通过我们的滤波器（filters）进行了梯度传播吗？让我们看一下第一层的更新，这里展示了5×5的滤波器组成的4×5的网格。

代码4：

imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')

输出4：

(-0.5, 24.5, 19.5, -0.5)

5.编写一个自定义的训练循环（Writing a custom training loop）

Something is happening. 我们让网络运行一段时间，运行期间我们保持跟踪一些事情。注意，这个过程和通过caffe二进制训练一样。特别是：

• 日志记录将继续正常进行。
• 在solver prototxt 指定的间隔获取快照（在这里，间隔取5000次迭代）
• 在指定的间隔进行测试（在这里，间隔取500次迭代）

因为我们可以使用 Python 控制循环，我们可以自由的计算额外的东西，就像下面展示的一样。我们也可以做一些别的事情，比如：

• 写一个自定义的停止标准
• 通过更新循环中的网络来改变求解过程

代码5：

%%timeniter = 200test_interval = 25# losses will also be stored in the logtrain_loss = zeros(niter)test_acc = zeros(int(np.ceil(niter / test_interval)))output = zeros((niter, 8, 10))# the main solver loopfor it in range(niter):    solver.step(1)  # SGD by Caffe    # store the train loss    train_loss[it] = solver.net.blobs['loss'].data    # store the output on the first test batch    # (start the forward pass at conv1 to avoid loading new data)    solver.test_nets[0].forward(start='conv1')    output[it] = solver.test_nets[0].blobs['score'].data[:8]    # run a full test every so often    # (Caffe can also do this for us and write to a log, but we show here    #  how to do it directly in Python, where more complicated things are easier.)    if it % test_interval == 0:        print 'Iteration', it, 'testing...'        correct = 0        for test_it in range(100):            solver.test_nets[0].forward()            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)                           == solver.test_nets[0].blobs['label'].data)        test_acc[it // test_interval] = correct / 1e4

输出5：
（貌似我的GPU比他们的好不少！）

Iteration 0 testing...Iteration 25 testing...Iteration 50 testing...Iteration 75 testing...Iteration 100 testing...Iteration 125 testing...Iteration 150 testing...Iteration 175 testing...Wall time: 2.3 s

---

我们看一下训练损失函数和测试正确率

代码6：

_, ax1 = subplots()ax2 = ax1.twinx()ax1.plot(arange(niter), train_loss)ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')ax1.set_xlabel('iteration')ax1.set_ylabel('train loss')ax2.set_ylabel('test accuracy')ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))

输出6：

<matplotlib.text.Text at 0x32b3fb38>

损失函数下降的很快，而且收敛（except for stochasticity，我觉得这句话翻译成除了局部随机性），相应的，正确率在上升。Hooray!

---

因为我们在第一次测试batch中保存了结果，所以我们可以观察我们的预测分数是如何演变的。x轴是迭代次数，y轴是标签。

代码7：

for i in range(8):    figure(figsize=(2, 2))    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')    figure(figsize=(10, 2))    imshow(output[:50, i].T, interpolation='nearest', cmap='gray')    xlabel('iteration')    ylabel('label')

输出7：

我们刚开始对这些数字（digit）一点都不了解，但是最后我们对每个数字都有正确的分类。随着分类的进行，你会发现识别最后的数字是非常困难的，倾斜的“9”很容易和“4”混淆。

---

注意：以上都是原始的输出而不是 softmax 计算的概率向量。如下所示，后者很容易表示我们的网络的 confidence （但是很难看到难以识别数字的分数）。

代码8：

for i in range(8):    figure(figsize=(2, 2))    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')    figure(figsize=(10, 2))    imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')    xlabel('iteration')    ylabel('label')####

输出8：

---

6.网络结构和优化的实验（Experiment with architecture and optimization）

现在，我们已经定义，训练和测试了LeNet，下一步要做什么有很多可能的方向：

• 定义新的网络结构进行比较
• 通过设置 base_lr 和简单的训练更长的时间进行调优。
• 将 SGD 改成有适应能力的方法如 AdaDelta 或者 Adam

随意地通过编辑以下的多功能示例来探索这些方向。寻找“ EDIT HERE ”的注释所建议的修改要点。
默认情况下，它定义了一个简单的线性分类器作为基准。
In case your coffee hasn’t kicked in and you’d like inspiration, try out！！！

1. 将 ReLU 转换为非线性的 ELU 或 Sigmoid 。
2. 添加更多的连接和非线性层
3. 以10×的倍数改变学习率
4. 将solver type改为 Adam 。
5. 将 niter 设置的更大让训练变得更长，让训练结果更好 。

代码9：

train_net_path = 'mnist/custom_auto_train.prototxt'test_net_path = 'mnist/custom_auto_test.prototxt'solver_config_path = 'mnist/custom_auto_solver.prototxt'### define netdef custom_net(lmdb, batch_size):    # define your own net!    n = caffe.NetSpec()    # keep this data layer for all networks    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,                             transform_param=dict(scale=1./255), ntop=2)    # EDIT HERE to try different networks    # this single layer defines a simple linear classifier    # (in particular this defines a multiway logistic regression)    n.score =   L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))    # EDIT HERE this is the LeNet variant we have already tried    # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))    # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)    # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))    # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)    # n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))    # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity    # n.relu1 = L.ReLU(n.fc1, in_place=True)    # n.score =   L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))    # keep this loss layer for all networks    n.loss =  L.SoftmaxWithLoss(n.score, n.label)    return n.to_proto()with open(train_net_path, 'w') as f:    f.write(str(custom_net('mnist/mnist_train_lmdb', 64)))    with open(test_net_path, 'w') as f:    f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))### define solverfrom caffe.proto import caffe_pb2s = caffe_pb2.SolverParameter()# Set a seed for reproducible experiments:# this controls for randomization in training.s.random_seed = 0xCAFFE# Specify locations of the train and (maybe) test networks.s.train_net = train_net_paths.test_net.append(test_net_path)s.test_interval = 500  # Test after every 500 training iterations.s.test_iter.append(100) # Test on 100 batches each time we test.s.max_iter = 10000     # no. of times to update the net (training iterations)# EDIT HERE to try different solvers# solver types include "SGD", "Adam", and "Nesterov" among others.s.type = "SGD"# Set the initial learning rate for SGD.s.base_lr = 0.01  # EDIT HERE to try different learning rates# Set momentum to accelerate learning by# taking weighted average of current and previous updates.s.momentum = 0.9# Set weight decay to regularize and prevent overfittings.weight_decay = 5e-4# Set `lr_policy` to define how the learning rate changes during training.# This is the same policy as our default LeNet.s.lr_policy = 'inv's.gamma = 0.0001s.power = 0.75# EDIT HERE to try the fixed rate (and compare with adaptive solvers)# `fixed` is the simplest policy that keeps the learning rate constant.# s.lr_policy = 'fixed'# Display the current training loss and accuracy every 1000 iterations.s.display = 1000# Snapshots are files used to store networks we've trained.# We'll snapshot every 5K iterations -- twice during training.s.snapshot = 5000s.snapshot_prefix = 'mnist/custom_net'# Train on the GPUs.solver_mode = caffe_pb2.SolverParameter.GPU# Write the solver to a temporary file and return its filename.with open(solver_config_path, 'w') as f:    f.write(str(s))### load the solver and create train and test netssolver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)solver = caffe.get_solver(solver_config_path)### solveniter = 250  # EDIT HERE increase to train for longertest_interval = niter / 10# losses will also be stored in the logtrain_loss = zeros(niter)test_acc = zeros(int(np.ceil(niter / test_interval)))# the main solver loopfor it in range(niter):    solver.step(1)  # SGD by Caffe    # store the train loss    train_loss[it] = solver.net.blobs['loss'].data    # run a full test every so often    # (Caffe can also do this for us and write to a log, but we show here    #  how to do it directly in Python, where more complicated things are easier.)    if it % test_interval == 0:        print 'Iteration', it, 'testing...'        correct = 0        for test_it in range(100):            solver.test_nets[0].forward()            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)                           == solver.test_nets[0].blobs['label'].data)        test_acc[it // test_interval] = correct / 1e4_, ax1 = subplots()ax2 = ax1.twinx()ax1.plot(arange(niter), train_loss)ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')ax1.set_xlabel('iteration')ax1.set_ylabel('train loss')ax2.set_ylabel('test accuracy')ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))

输出9：

Iteration 0 testing...Iteration 25 testing...Iteration 50 testing...Iteration 75 testing...Iteration 100 testing...Iteration 125 testing...Iteration 150 testing...Iteration 175 testing...Iteration 200 testing...Iteration 225 testing...<matplotlib.text.Text at 0x7f5199af9f50>