基于caffe特征可视化　以及　用训练好的模型进行分类

环境：caffe, ipython notebook

1. 配置好caffe 的python环境，确保　import caffe 命令成功

2. 在notebook编写程序(或参考参考官网imagenet的实现过程: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb)：
import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

caffe_root = '/home/wanghl/CAFFE/'# this file is expected to be in {caffe_root}/examples

import sys

sys.path.insert(0, caffe_root + 'python')

import caffe

# Set the right path to your model definition file, pretrained model weights,

#设置好模型路径,和想分类的图像

# and the image you would like to classify.

MODEL_FILE = caffe_root+'examples/imagenet/imagenet_deploy.prototxt'

PRETRAINED = caffe_root+'examples/imagenet/caffe_reference_imagenet_model'

caffe.set_mode_cpu()

net = caffe.Classifier(MODEL_FILE, PRETRAINED, mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1), channel_swap=(2,1,0), raw_scale=255,image_dims=(256, 256))

imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'

labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')

IMAGE_FILE = caffe_root+'examples/images/cat.jpg'

input_image = caffe.io.load_image(IMAGE_FILE)

#plt.imshow(input_image)

#plt.show()

#分类预测，预测完就可以对每一层特征进行可视化

prediction = net.predict([input_image])

#输出前５个概率最高的类别

print 'predicted class:',prediction[0].argmax()

top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]

print labels[top_k]

[(k, v.data.shape) for k, v in net.blobs.items()]

[(k, v[0].data.shape) for k, v in net.params.items()]

#定义显示函数

#def showimage(input_image):

# if input_image.ndim == 3:

# m = input_image[:, :, ::-1]

# plt.imshow(input_image)

#定义函数vis_square

def vis_square(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))

# 对图像使用滤波器

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)

#conv1层特征显示

conv1 = net.blobs['conv1']

conv1_data = conv1.data[0,:]

vis_square(conv1_data,padval=1)

plt.show()

#显示第85个特征

res = conv1_data[84:85,:]

vis_square(res,padval=1)

plt.show()

# index four is the center crop

# 输出输入的图像

image = net.blobs['data'].data[4].copy()

image -= image.min()

image /= image.max()

plt.imshow(image.transpose(1, 2, 0))

# the parameters are a list of [weights, biases]

filters = net.params['conv1'][0].data

vis_square(filters.transpose(0, 2, 3, 1))

feat = net.blobs['conv1'].data[4, :96]

vis_square(feat, padval=1)

#全连接层每一层的类别直方图

feat = net.blobs['fc6'].data[4]

plt.subplot(2, 1, 1)

plt.plot(feat.flat)

plt.subplot(2, 1, 2)

_ = plt.hist(feat.flat[feat.flat > 0], bins=100)

feat = net.blobs['fc7'].data[4]

plt.subplot(2, 1, 1)

plt.plot(feat.flat)

plt.subplot(2, 1, 2)

_ = plt.hist(feat.flat[feat.flat > 0], bins=100)

feat = net.blobs['prob'].data[4]

plt.subplot(2, 1, 1)

plt.plot(feat.flat)