资源：

2.ImageNet 1000种分类以及排列

https://github.com/sh1r0/caffe-Android-demo/blob/master/app/src/main/assets/synset_words.txt（如果下载单个txt格式不对的话就整包下载）

 

完整代码如下：

import numpy as np
import scipy.misc
import scipy.io as sio
import tensorflow as  tf
import os##卷积层
def _conv_layer(input, weight, bias):conv = tf.nn.conv2d(input, tf.constant(weight), strides=(1, 1, 1, 1), padding='SAME')return tf.nn.bias_add(conv, bias)##池化层
def _pool_layer(input):return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')##全链接层
def _fc_layer(input, weights, bias):shape = input.get_shape().as_list()dim = 1for d in shape[1:]:dim *= dx = tf.reshape(input, [-1, dim])fc = tf.nn.bias_add(tf.matmul(x, weights), bias)return fc##softmax输出层
def _softmax_preds(input):preds = tf.nn.softmax(input, name='prediction')return preds##图片处里前减去均值
def _preprocess(image, mean_pixel):return image - mean_pixel##加均值  显示图片
def _unprocess(image, mean_pixel):return image + mean_pixel##读取图片 并压缩
def _get_img(src, img_size=False):img = scipy.misc.imread(src, mode='RGB')if not (len(img.shape) == 3 and img.shape[2] == 3):img = np.dstack((img, img, img))if img_size != False:img = scipy.misc.imresize(img, img_size)return img.astype(np.float32)##获取名列表
def list_files(in_path):files = []for (dirpath, dirnames, filenames) in os.walk(in_path):# print("dirpath=%s, dirnames=%s, filenames=%s"%(dirpath, dirnames, filenames))files.extend(filenames)breakreturn files##获取文件路径列表dir+filename
def _get_files(img_dir):files = list_files(img_dir)return [os.path.join(img_dir, x) for x in files]##获得图片lable列表
def _get_allClassificationName(file_path):f = open(file_path, 'r')lines = f.readlines()f.close()return lines##构建cnn前向传播网络
def net(data, input_image):layers = ('conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1','conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2','conv3_1', 'relu3_1', 'conv3_2', 'relu3_2','conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3','conv4_1', 'relu4_1', 'conv4_2', 'relu4_2','conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4','conv5_1', 'relu5_1', 'conv5_2', 'relu5_2','conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5','fc6', 'relu6','fc7', 'relu7','fc8', 'softmax')weights = data['layers'][0]net = {}current = input_imagefor i, name in enumerate(layers):kind = name[:4]if kind == 'conv':kernels, bias = weights[i][0][0][0][0]kernels = np.transpose(kernels, (1, 0, 2, 3))bias = bias.reshape(-1)current = _conv_layer(current, kernels, bias)elif kind == 'relu':current = tf.nn.relu(current)elif kind == 'pool':current = _pool_layer(current)elif kind == 'soft':current = _softmax_preds(current)kind2 = name[:2]if kind2 == 'fc':kernels1, bias1 = weights[i][0][0][0][0]kernels1 = kernels1.reshape(-1, kernels1.shape[-1])bias1 = bias1.reshape(-1)current = _fc_layer(current, kernels1, bias1)net[name] = currentassert len(net) == len(layers)return net, mean_pixel, layersif __name__ == '__main__':imagenet_path = 'data/imagenet-vgg-verydeep-19.mat'image_dir = 'images/'data = sio.loadmat(imagenet_path)  ##加载ImageNet mat模型mean = data['normalization'][0][0][0]mean_pixel = np.mean(mean, axis=(0, 1))  ##获取图片像素均值lines = _get_allClassificationName('data/synset_words.txt')  ##加载ImageNet mat标签images = _get_files(image_dir)  ##获取图片路径列表with tf.Session() as sess:for i, imgPath in enumerate(images):image = _get_img(imgPath, (224, 224, 3))  ##加载图片并压缩到标准格式=>224 224image_pre = _preprocess(image, mean_pixel)# image_pre = image_pre.transpose((2, 0, 1))image_pre = np.expand_dims(image_pre, axis=0)image_preTensor = tf.convert_to_tensor(image_pre)image_preTensor = tf.to_float(image_preTensor)# Test pretrained modelnets, mean_pixel, layers = net(data, image_preTensor)preds = nets['softmax']predsSortIndex = np.argsort(-preds[0].eval())print('#####%s#######' % imgPath)for i in range(3):   ##输出前3种分类nIndex = predsSortIndexclassificationName = lines[nIndex[i]] ##分类名称problity = preds[0][nIndex[i]]   ##某一类型概率print('%d.ClassificationName=%s  Problity=%f' % ((i + 1), classificationName, problity.eval()))

 

#####images/cat1.jpg#######
1.ClassificationName=n02123045 tabby, tabby cat
  Problity=0.219027
2.ClassificationName=n02123159 tiger cat
  Problity=0.091527
3.ClassificationName=n02445715 skunk, polecat, wood pussy
  Problity=0.028864

 

#####images/cat2.jpg#######
1.ClassificationName=n02123045 tabby, tabby cat
  Problity=0.337648
2.ClassificationName=n02123159 tiger cat
  Problity=0.171013
3.ClassificationName=n02124075 Egyptian cat
  Problity=0.059857

 

#####images/cat_two.jpg#######
1.ClassificationName=n03887697 paper towel
  Problity=0.178623
2.ClassificationName=n02111889 Samoyed, Samoyede
  Problity=0.119629
3.ClassificationName=n02098286 West Highland white terrier
  Problity=0.060589

 

#####images/dog1.jpg#######
1.ClassificationName=n02096585 Boston bull, Boston terrier
  Problity=0.403131
2.ClassificationName=n02108089 boxer
  Problity=0.184223
3.ClassificationName=n02093256 Staffordshire bullterrier, Staffordshire bull terrier
  Problity=0.101937