caffe示例实现之10LeNet的python接口
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本例利用python学写caffe模型的prototxt。先载入一些必要的模块,把路径改成自己的:
import osos.chdir('/home/lml/caffe-master/')import syssys.path.insert(0, './python')import caffefrom pylab import %matplotlib inline
接下来运行给出的LeNet例子(确定已经下载了数据,转换好了数据格式):
# Download and prepare data!data/mnist/get_mnist.sh!examples/mnist/create_mnist.sh
Downloading…
–2015-09-21 09:11:03– http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz 正在解析主机
yann.lecun.com (yann.lecun.com)… 128.122.47.89 正在连接 yann.lecun.com
(yann.lecun.com)|128.122.47.89|:80… 已连接。 已发出 HTTP 请求,正在等待回应… 200
OK 长度: 9912422 (9.5M) [application/x-gzip] 正在保存至:
“train-images-idx3-ubyte.gz”
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–2015-09-21 09:11:16– http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz 正在解析主机
yann.lecun.com (yann.lecun.com)… 128.122.47.89 正在连接 yann.lecun.com
(yann.lecun.com)|128.122.47.89|:80… 已连接。 已发出 HTTP 请求,正在等待回应… 200
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–2015-09-21 09:11:18– http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz 正在解析主机
yann.lecun.com (yann.lecun.com)… 128.122.47.89 正在连接 yann.lecun.com
(yann.lecun.com)|128.122.47.89|:80… 已连接。 已发出 HTTP 请求,正在等待回应… 200
OK 长度: 1648877 (1.6M) [application/x-gzip] 正在保存至:
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–2015-09-21 09:11:22– http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz 正在解析主机
yann.lecun.com (yann.lecun.com)… 128.122.47.89 正在连接 yann.lecun.com
(yann.lecun.com)|128.122.47.89|:80… 已连接。 已发出 HTTP 请求,正在等待回应… 200
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“t10k-labels-idx1-ubyte.gz” [4542/4542]) Unzipping… Done. Creating
lmdb… Done.
还需要两个文件:
- net prototxt,定义网络结构,并指定对应的训练/测试数据
- solver prototxt,定义学习的参数
先从网络开始,用python代码以caffe的protobuf模型格式写网络。这个网络读取LMDB格式的预生成数据,也可以用MemoryDataLayer从ndarray中直接读取。
from caffe import layers as Lfrom caffe import 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.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier')) n.relu1 = L.ReLU(n.ip1, in_place=True) n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.ip2, n.label) return n.to_proto()with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f: f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f: f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))
这个网络利用Google的protobuf库写入到磁盘,虽然这样写出来的网络模型有些冗长,但是可读性好,可以直接读、写、修改。看一下这样自动生成的训练网络:
!cat examples/mnist/lenet_auto_train.prototxt
layer { name: "data" type: "Data" top: "data" top: "label" transform_param { scale: 0.00392156862745 } data_param { source: "examples/mnist/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: "ip1" type: "InnerProduct" bottom: "pool2" top: "ip1" inner_product_param { num_output: 500 weight_filler { type: "xavier" } }}layer { name: "relu1" type: "ReLU" bottom: "ip1" top: "ip1"}layer { name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" inner_product_param { num_output: 10 weight_filler { type: "xavier" } }}layer { name: "loss" type: "SoftmaxWithLoss" bottom: "ip2" bottom: "label" top: "loss"}
看看学习参数,同样是prototxt文件,用带动量的SGD(随机梯度下降),权重递减,以及特定的学习率:
!cat examples/mnist/lenet_auto_solver.prototxt
# The train/test net protocol buffer definitiontrain_net: "examples/mnist/lenet_auto_train.prototxt"test_net: "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: "examples/mnist/lenet"
选择GPU,载入solver,这里用的是SGD,Adagrad和Nesterov加速梯度也是可行的:
caffe.set_device(0)caffe.set_mode_gpu()solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')
查看中间特征(blobs)和参数(params)的维数,对网络结构加深理解:
# each output is (batch size, feature dim, spatial dim)[(k, v.data.shape) for k, v in solver.net.blobs.items()]
[(‘data’, (64, 1, 28, 28)),
(‘label’, (64,)),
(‘conv1’, (64, 20, 24, 24)),
(‘pool1’, (64, 20, 12, 12)),
(‘conv2’, (64, 50, 8, 8)),
(‘pool2’, (64, 50, 4, 4)),
(‘ip1’, (64, 500)),
(‘ip2’, (64, 10)),
(‘loss’, ())]
# just print the weight sizes (not biases)[(k, v[0].data.shape) for k, v in solver.net.params.items()]
[(‘conv1’, (20, 1, 5, 5)),
(‘conv2’, (50, 20, 5, 5)),
(‘ip1’, (500, 800)),
(‘ip2’, (10, 500))]
在开始训练前,检查一下所有东西是不是都载入了,接下来在测试集和训练集上执行一个前向的过程,确保网络中包含了数据:
solver.net.forward() # train netsolver.test_nets[0].forward() # test net (there can be more than one)
{‘loss’: array(2.394181489944458, dtype=float32)}
显示训练集的前8个图像和它们的标签来看看:
# we use a little trick to tile the first eight imagesimshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 828), cmap='gray')print solver.net.blobs['label'].data[:8]
[ 5. 0. 4. 1. 9. 2. 1. 3.]
再显示测试集的前8个图像和它们的标签来看看:
imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 828), cmap='gray')print solver.test_nets[0].blobs['label'].data[:8]
[ 7. 2. 1. 0. 4. 1. 4. 9.]
看起来训练集和测试集的数据都已经顺利载入了,并且标签都是正确的。下面执行一个minibatch的SGD,看看会发生什么:
solver.step(1)
我们看一下第一层的滤波器经过传播后的更新情况,20个5×5的滤波器:
imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5).transpose(0, 2, 1, 3).reshape(45, 55), cmap='gray')
让网络运行一会,注意到这个过程和通过caffe的binary训练是一样的。由于可以控制python中的循环,因此可以做一些其他的事情,例如自定义停止的标准,通过循环更新网络来改变求解过程:
%%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['ip2'].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['ip2'].data.argmax(1) == solver.test_nets[0].blobs['label'].data) test_acc[it // test_interval] = correct / 1e4
Iteration 0 testing…
Iteration 25 testing…
Iteration 50 testing…
Iteration 75 testing…
Iteration 100 testing…
Iteration 125 testing…
Iteration 150 testing…
Iteration 175 testing…
CPU times: user 2.29 s, sys: 588 ms, total: 2.88 s
Wall time: 2.05 s
接下来画出训练损失和测试准确率:
_, 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')
损失下降很快,并且会收敛(有些随机性),准确率相应上升。由于保存了第一个测试batch的结果,可以看一下预测得分是怎样变化的,x轴为时间,y轴为每个可能的标签,亮度表示置信度:
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')
发现最后一个数字9最容易出错,会和数字4混淆。注意到这是原始输出得分,而不是softmax计算后的概率,下面的内容可以看到网络的置信度:
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')
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