conv_filter

import numpy as np
import matplotlib.pyplot as plt
import os,sys,caffe
caffe_root = '~/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 (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，规模为(32,3,5,5)
weight = net.params["conv1"][0].data
print weight.shape
show_feature(weight.transpose(0, 2, 3, 1))