Caffe学习系列:模型各层数据和参数可视化

先用caffe对cifar10进行训练,将训练的结果模型进行保存,得到一个caffemodel,然后从测试图片中选出一张进行测试,并进行可视化。

In [1]:
#加载必要的库
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import sys,os,caffe
In [2]:
#设置当前目录,判断模型是否训练好
caffe_root = '/home/bnu/caffe/' 
sys.path.insert(0, caffe_root + 'python')
os.chdir(caffe_root)
if not os.path.isfile(caffe_root + 'examples/cifar10/cifar10_quick_iter_4000.caffemodel'):
    print("caffemodel is not exist...")
In [3]:
#利用提前训练好的模型,设置测试网络
caffe.set_mode_gpu()
net = caffe.Net(caffe_root + 'examples/cifar10/cifar10_quick.prototxt',
                caffe_root + 'examples/cifar10/cifar10_quick_iter_4000.caffemodel',
                caffe.TEST)
In [4]:
net.blobs['data'].data.shape
Out[4]:
(1, 3, 32, 32)
In [5]:
#加载测试图片,并显示
im = caffe.io.load_image('examples/images/32.jpg')
print im.shape
plt.imshow(im)
plt.axis('off')
 
(32, 32, 3)
Out[5]:
(-0.5, 31.5, 31.5, -0.5)
 
In [6]:
# 编写一个函数,将二进制的均值转换为python的均值
def convert_mean(binMean,npyMean):
    blob = caffe.proto.caffe_pb2.BlobProto()
    bin_mean = open(binMean, 'rb' ).read()
    blob.ParseFromString(bin_mean)
    arr = np.array( caffe.io.blobproto_to_array(blob) )
    npy_mean = arr[0]
    np.save(npyMean, npy_mean )
binMean=caffe_root+'examples/cifar10/mean.binaryproto'
npyMean=caffe_root+'examples/cifar10/mean.npy'
convert_mean(binMean,npyMean)
In [7]:
#将图片载入blob中,并减去均值
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_mean('data', np.load(npyMean).mean(1).mean(1)) # 减去均值
transformer.set_raw_scale('data', 255)  
transformer.set_channel_swap('data', (2,1,0))
net.blobs['data'].data[...] = transformer.preprocess('data',im)
inputData=net.blobs['data'].data
In [8]:
#显示减去均值前后的数据
plt.figure()
plt.subplot(1,2,1),plt.title("origin")
plt.imshow(im)
plt.axis('off')
plt.subplot(1,2,2),plt.title("subtract mean")
plt.imshow(transformer.deprocess('data', inputData[0]))
plt.axis('off')
Out[8]:
(-0.5, 31.5, 31.5, -0.5)
 
In [9]:
#运行测试模型,并显示各层数据信息
net.forward()
[(k, v.data.shape) for k, v in net.blobs.items()]
Out[9]:
[('data', (1, 3, 32, 32)),
 ('conv1', (1, 32, 32, 32)),
 ('pool1', (1, 32, 16, 16)),
 ('conv2', (1, 32, 16, 16)),
 ('pool2', (1, 32, 8, 8)),
 ('conv3', (1, 64, 8, 8)),
 ('pool3', (1, 64, 4, 4)),
 ('ip1', (1, 64)),
 ('ip2', (1, 10)),
 ('prob', (1, 10))]
In [10]:
#显示各层的参数信息
[(k, v[0].data.shape) for k, v in net.params.items()]
Out[10]:
[('conv1', (32, 3, 5, 5)),
 ('conv2', (32, 32, 5, 5)),
 ('conv3', (64, 32, 5, 5)),
 ('ip1', (64, 1024)),
 ('ip2', (10, 64))]
In [11]:
# 编写一个函数,用于显示各层数据
def show_data(data, padsize=1, padval=0):
    data -= data.min()
    data /= data.max()
    
    # force the number of filters to be square
    n = int(np.ceil(np.sqrt(data.shape[0])))
    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)
    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))
    
    # tile the filters into an image
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
    plt.figure()
    plt.imshow(data,cmap='gray')
    plt.axis('off')
plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
In [12]:
#显示第一个卷积层的输出数据和权值(filter)
show_data(net.blobs['conv1'].data[0])
print net.blobs['conv1'].data.shape
show_data(net.params['conv1'][0].data.reshape(32*3,5,5))
print net.params['conv1'][0].data.shape
 
(1, 32, 32, 32)
(32, 3, 5, 5)
 
 
In [13]:
#显示第一次pooling后的输出数据
show_data(net.blobs['pool1'].data[0])
net.blobs['pool1'].data.shape
Out[13]:
(1, 32, 16, 16)
 
In [14]:
#显示第二次卷积后的输出数据以及相应的权值(filter)
show_data(net.blobs['conv2'].data[0],padval=0.5)
print net.blobs['conv2'].data.shape
show_data(net.params['conv2'][0].data.reshape(32**2,5,5))
print net.params['conv2'][0].data.shape
 
(1, 32, 16, 16)
(32, 32, 5, 5)
 
 
In [15]:
#显示第三次卷积后的输出数据以及相应的权值(filter),取前1024个进行显示
show_data(net.blobs['conv3'].data[0],padval=0.5)
print net.blobs['conv3'].data.shape
show_data(net.params['conv3'][0].data.reshape(64*32,5,5)[:1024])
print net.params['conv3'][0].data.shape
 
(1, 64, 8, 8)
(64, 32, 5, 5)
 
 
In [16]:
#显示第三次池化后的输出数据
show_data(net.blobs['pool3'].data[0],padval=0.2)
print net.blobs['pool3'].data.shape
 
(1, 64, 4, 4)
 
In [17]:
# 最后一层输入属于某个类的概率
feat = net.blobs['prob'].data[0]
print feat
plt.plot(feat.flat)
 
[  5.21440245e-03   1.58397834e-05   3.71246301e-02   2.28459597e-01
   1.08315737e-03   7.17785358e-01   1.91939052e-03   7.67927198e-03
   6.13298907e-04   1.05107691e-04]
Out[17]:
[]
 
 

从输入的结果和图示来看,最大的概率是7.17785358e-01,属于第5类(标号从0开始)。与cifar10中的10种类型名称进行对比:

airplane、automobile、bird、cat、deer、dog、frog、horse、ship、truck

根据测试结果,判断为dog。 测试无误!

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