使用python可视化特征层参数和特征图

import numpy as npimport matplotlib.pyplot as pltimport osimport caffeimport sysimport pickleimport cv2caffe_root = 'deeplearning/regressionNet/sln/install/'  deployPrototxt =  'deeplearning/regressionNet/sln/examples/regression_vggNet_deploy.prototxt'modelFile = 'deeplearning/regressionNet/sln/examples/_iter_1000.caffemodel'#meanFile = 'python/caffe/imagenet/ilsvrc_2012_mean.npy'

#网络初始化def initilize():    print 'initilize ... '    sys.path.insert(0, caffe_root + 'python')    caffe.set_mode_gpu()    #caffe.set_device(4)    net = caffe.Net(deployPrototxt, modelFile,caffe.TEST)    return net#取出网络中的params和net.blobs的中的数据def getNetDetails(image, net):    # input preprocessing: 'data' is the name of the input blob == net.inputs[0]    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})    transformer.set_transpose('data', (2,0,1))    #transformer.set_mean('data', np.load(caffe_root + meanFile ).mean(1).mean(1)) # mean pixel    transformer.set_raw_scale('data', 255)      # the reference model operates on images in [0,255] range instead of [0,1]    transformer.set_channel_swap('data', (2,1,0))      # the reference model has channels in BGR order instead of RGB    # set net to batch size of 50    net.blobs['data'].reshape(1,3,288,352)    net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(image))    out = net.forward()        #网络提取conv1的卷积核    #filters = net.params['conv1_1'][0].data    # with open('FirstLayerFilter.pickle','wb') as f:    #  pickle.dump(filters,f)    #vis_square(filters.transpose(0, 2, 3, 1))    #conv1的特征图    feat = net.blobs['pool3'].data[0, :256]    with open('FirstLayerOutput.pickle','wb') as f:       pickle.dump(feat,f)    vis_square(feat,padval=1)# 此处将卷积图和进行显示，def vis_square(data, padsize=1, padval=0 ):    plt.figure()    data -= data.min()    data /= data.max()        #让合成图为方    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))    #合并卷积图到一个图像中        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:])    print data.shape    plt.imshow(data)    plt.show()

net = initilize()testimage = '423.jpg'getNetDetails(testimage, net)prob = net.blobs['fc6'].data[0]print prob