本文是深度学习实战系列文章，主要是利用官网VGG 19层网络训练得到模型产生的weight和bias数值，对输入的任意一张图像进行前向训练，从而得到特征图。

一. 代码

以下是对应代码：

# coding: utf-8
 
import scipy.io
import numpy as np 
import os 
import scipy.misc 
import matplotlib.pyplot as plt 
import tensorflow as tf
 
 
 
def _conv_layer(input, weights, bias):
    conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),
            padding='SAME')
    return tf.nn.bias_add(conv, bias)
def _pool_layer(input):
    return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
            padding='SAME')
def preprocess(image, mean_pixel):
    return image - mean_pixel
def unprocess(image, mean_pixel):
    return image + mean_pixel
def imread(path):
    return scipy.misc.imread(path).astype(np.float)
def imsave(path, img):
    img = np.clip(img, 0, 255).astype(np.uint8)
    scipy.misc.imsave(path, img)
print ("Functions for VGG ready")
 
 
 
def net(data_path, 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'
    )
    data = scipy.io.loadmat(data_path)
    mean = data['normalization'][0][0][0]
    mean_pixel = np.mean(mean, axis=(0, 1))
    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]
            # matconvnet: weights are [width, height, in_channels, out_channels]
            # tensorflow: weights are [height, width, in_channels, out_channels]
            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)
        net[name] = current
    assert len(net) == len(layers)
    return net, mean_pixel, layers
print ("Network for VGG ready")
 
 
 
cwd  = os.getcwd()
VGG_PATH = cwd + "/data/imagenet-vgg-verydeep-19.mat"
IMG_PATH = cwd + "/data/cat.jpg"
input_image = imread(IMG_PATH)
shape = (1,input_image.shape[0],input_image.shape[1],input_image.shape[2]) 
with tf.Session() as sess:
    image = tf.placeholder('float', shape=shape)
    nets, mean_pixel, all_layers = net(VGG_PATH, image)
    input_image_pre = np.array([preprocess(input_image, mean_pixel)])
    layers = all_layers # For all layers 
    # layers = ('relu2_1', 'relu3_1', 'relu4_1')
    for i, layer in enumerate(layers):
        print ("[%d/%d] %s" % (i+1, len(layers), layer))
        features = nets[layer].eval(feed_dict={image: input_image_pre})
        
        print (" Type of 'features' is ", type(features))
        print (" Shape of 'features' is %s" % (features.shape,))
        # Plot response 
        if 1:
            plt.figure(i+1, figsize=(10, 5))
            plt.matshow(features[0, :, :, 0], cmap=plt.cm.gray, fignum=i+1)
            plt.title("" + layer)
            plt.colorbar()
            plt.show()

二. VGG19网络结构

其中VGG19的网络结构图如下，方便理解net中的各个层在做什么：

在.mat文件中的layers结构中能找到对应各个层的数据信息(如下图)，第一个结构即对应第一个conv1-1,里面包含核大小为3X3的权重数据（共计3X63个），同理，第3个对应conv1-2(尺寸为3X3，共64X64个)……

每个层和对应weight的维度信息如下：

 0 is conv1_1 (3, 3, 3, 64)

        1 is relu activation function 

        2 is conv1_2 (3, 3, 64, 64)
        3 is relu    
        4 is maxpool
        5 is conv2_1 (3, 3, 64, 128)
        6 is relu
        7 is conv2_2 (3, 3, 128, 128)
        8 is relu
        9 is maxpool
        10 is conv3_1 (3, 3, 128, 256)
        11 is relu
        12 is conv3_2 (3, 3, 256, 256)
        13 is relu
        14 is conv3_3 (3, 3, 256, 256)
        15 is relu
        16 is conv3_4 (3, 3, 256, 256)
        17 is relu
        18 is maxpool
        19 is conv4_1 (3, 3, 256, 512)
        20 is relu
        21 is conv4_2 (3, 3, 512, 512)
        22 is relu
        23 is conv4_3 (3, 3, 512, 512)
        24 is relu
        25 is conv4_4 (3, 3, 512, 512)
        26 is relu
        27 is maxpool
        28 is conv5_1 (3, 3, 512, 512)
        29 is relu
        30 is conv5_2 (3, 3, 512, 512)
        31 is relu
        32 is conv5_3 (3, 3, 512, 512)
        33 is relu
        34 is conv5_4 (3, 3, 512, 512)
        35 is relu
        36 is maxpool
        37 is fullyconnected (7, 7, 512, 4096)
        38 is relu
        39 is fullyconnected (1, 1, 4096, 4096)
        40 is relu
        41 is fullyconnected (1, 1, 4096, 1000)

        42 is softmax
三. 代码解析

VGG_PATH = cwd + "/data/imagenet-vgg-verydeep-19.mat"
IMG_PATH = cwd + "/data/cat.jpg"

其中的VGG_PATH路径下保存的是原来VGG19网络模型训练过程得到的各个层weight和bias的值，即这个mat文件，具体这个mat下面的数据结构如何可用matlab打开看看。

imagenet-vgg-verydeep-19.mat

其中IMG_PATH这个路径是存放你要测试用的图片，整个代码的作用是利用之前训练的模型数据对输入图像做特征图的显示；

这个 cwd 指令就是获取得到当前代码文件所在的路径；

下图是我输入的图像：

输出结果（取其中前两个结果）

[1/36] conv1_1
 Type of 'features' is  <class 'numpy.ndarray'>
 Shape of 'features' is (1, 1440, 2560, 64)

[2/36] relu1_1
 Type of 'features' is  <class 'numpy.ndarray'>
 Shape of 'features' is (1, 1440, 2560, 64)