http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat

#coding=utf-8

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 = 1

    for d in shape[1:]:

        dim *= d

    x = 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)

        break

    return 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_image

    for 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] = current

    assert len(net) == len(layers)

    return net, mean_pixel, layers

if __name__ == '__main__':

    imagenet_path = '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('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=>224 224

            image_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 model

            nets, mean_pixel, layers = net(data, image_preTensor)

            preds = nets['softmax']

            predsSortIndex = np.argsort(-preds[0].eval())

            print('\n#####%s#######' % imgPath)

            for i in range(3):   ##输出前3种分类

                nIndex = predsSortIndex

                classificationName = 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()))

        sess.close()

#####images/airplay.jpg#######

1.ClassificationName=n04228054 ski

  Problity=0.177715

2.ClassificationName=n04286575 spotlight, spot

  Problity=0.108483

3.ClassificationName=n04127249 safety pin

  Problity=0.026277

#####images/bird.jpg#######

1.ClassificationName=n01608432 kite

  Problity=0.096818

2.ClassificationName=n01833805 hummingbird

  Problity=0.072687

3.ClassificationName=n02231487 walking stick, walkingstick, stick insect

  Problity=0.069186

#####images/cat1.jpg#######

1.ClassificationName=n02123045 tabby, tabby cat

  Problity=0.232015

2.ClassificationName=n02123159 tiger cat

  Problity=0.094694

3.ClassificationName=n02124075 Egyptian cat

  Problity=0.030673

#####images/cat2.jpg#######

1.ClassificationName=n02123045 tabby, tabby cat

  Problity=0.333797

2.ClassificationName=n02123159 tiger cat

  Problity=0.164726

3.ClassificationName=n02124075 Egyptian cat

  Problity=0.057272

#####images/cat3.jpg#######

1.ClassificationName=n03887697 paper towel

  Problity=0.086723

2.ClassificationName=n02111889 Samoyed, Samoyede

  Problity=0.055845

3.ClassificationName=n03131574 crib, cot

  Problity=0.052640

#####images/dog1.jpg#######

1.ClassificationName=n02096585 Boston bull, Boston terrier

  Problity=0.429622

2.ClassificationName=n02108089 boxer

  Problity=0.199422

3.ClassificationName=n02093256 Staffordshire bullterrier, Staffordshire bull terrier

  Problity=0.093615

#####images/dog2.jpg#######

1.ClassificationName=n02085936 Maltese dog, Maltese terrier, Maltese

  Problity=0.172208

2.ClassificationName=n03445777 golf ball

  Problity=0.139949

3.ClassificationName=n02259212 leafhopper

  Problity=0.118109

#####images/lena.jpg#######

1.ClassificationName=n02869837 bonnet, poke bonnet

  Problity=0.130357

2.ClassificationName=n04356056 sunglasses, dark glasses, shades

  Problity=0.066170

3.ClassificationName=n04355933 sunglass

  Problity=0.043199

#####images/sky.jpg#######

1.ClassificationName=n03733281 maze, labyrinth

  Problity=0.711163

2.ClassificationName=n03065424 coil, spiral, volute, whorl, helix

  Problity=0.181123

3.ClassificationName=n04259630 sombrero

  Problity=0.010005