Caffe学习系列(16):caffemodel可视化
通过前面的学习,我们已经能够正常训练各种数据了。设置好solver.prototxt后,我们可以把训练好的模型保存起来,如lenet_iter_10000.caffemodel。 训练多少次就自动保存一下,这个是通过snapshot进行设置的,保存文件的路径及文件名前缀是由snapshot_prefix来设定的。这个文件里面存放的就是各层的参数,即net.params,里面没有数据(net.blobs)。顺带还生成了一个相应的solverstate文件,这个和caffemodel差不多,但它多了一些数据,如模型名称、当前迭代次数等。两者的功能不一样,训练完后保存起来的caffemodel,是在测试阶段用来分类的,而solverstate是用来恢复训练的,防止意外终止而保存的快照(有点像断点续传的感觉)。
既然我们知道了caffemodel里面保存的就是模型各层的参数,因此我们可以把这些参数提取出来,进行可视化,看一看究竟长什么样。
我们先训练cifar10数据(mnist也可以),迭代10000次,然后将训练好的 model保存起来,名称为my_iter_10000.caffemodel,然后使用jupyter notebook 来进行可视化。
在此顺便问一下各位大牛:如何在cnblogs中,发表jupyter notebook生成的文章?
import numpy as np
import matplotlib.pyplot as plt
import os,sys,caffe
%matplotlib inline
caffe_root='/home/lee/caffe/'
os.chdir(caffe_root)
sys.path.insert(0,caffe_root+'python')
plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
net = caffe.Net(caffe_root + 'examples/cifar10/cifar10_full.prototxt',
caffe_root + 'examples/cifar10/my_iter_10000.caffemodel',
caffe.TEST)
[(k, v[0].data.shape) for k, v in net.params.items()]
[('conv1', (32, 3, 5, 5)),
('conv2', (32, 32, 5, 5)),
('conv3', (64, 32, 5, 5)),
('ip1', (10, 1024))]
#编写一个函数,用于显示各层的参数
def show_feature(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.imshow(data)
plt.axis('off')
# 第一个卷积层,参数规模为(32,3,5,5),即32个5*5的3通道filter
weight = net.params["conv1"][0].data
print weight.shape
show_feature(weight.transpose(0, 2, 3, 1))
(32, 3, 5, 5)
参数有两种类型:权值参数和偏置项。分别用params["conv1"][0] 和params["conv1"][1] 表示 。
我们只显示权值参数,因此用params["conv1"][0]
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# 第二个卷积层的权值参数,共有32*32个filter,每个filter大小为5*5
weight = net.params["conv2"][0].data
print weight.shape
show_feature(weight.reshape(32**2, 5, 5))
(32, 32, 5, 5)
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# 第三个卷积层的权值,共有64*32个filter,每个filter大小为5*5,取其前1024个进行可视化
weight = net.params["conv3"][0].data print weight.shape show_feature(weight.reshape(64*32, 5, 5)[:1024])
(64, 32, 5, 5)
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