基于tensorflow + Vgg16进行图像分类识别

1. VGG-16介绍

vgg是在Very Deep Convolutional Networks for Large-Scale Image Recognition期刊上提出的。模型可以达到92.7%的测试准确度,在ImageNet的前5位。它的数据集包括1400万张图像，1000个类别。
vgg-16是一种深度卷积神经网络模型，16表示其深度，在图像分类等任务中取得了不错的效果。
vgg16 的宏观结构图如下。代码定义在tensorflow的vgg16.py文件 。注意，包括一个预处理层，使用RGB图像在0-255范围内的像素值减去平均值（在整个ImageNet图像训练集计算）。

2. 文件组成

模型权重 - vgg16_weights.npz
TensorFlow模型- vgg16.py
类名（输出模型到类名的映射） - imagenet_classes.py
示例图片输入 - laska.png
我们使用特定的工具转换了原作者在GitHub profile上公开可用的Caffe权重，并做了一些后续处理，以确保模型符合TensorFlow标准。最终实现可用的权重文件vgg16_weights.npz
下载所有的文件到同一文件夹下，然后运行 python vgg16.py

- vgg16.py文件代码：

import tensorflow as tf
import numpy as np
from scipy.misc import imread, imresize
from imagenet_classes import class_names


class vgg16:
    def __init__(self, imgs, weights=None, sess=None):
        self.imgs = imgs
        self.convlayers()
        self.fc_layers()
        self.probs = tf.nn.softmax(self.fc3l)
        if weights is not None and sess is not None:
            self.load_weights(weights, sess)


    def convlayers(self):
        self.parameters = []

        # zero-mean input
        with tf.name_scope('preprocess') as scope:
            mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
            images = self.imgs-mean

        # conv1_1
        with tf.name_scope('conv1_1') as scope:
            kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32,
                                                     stddev=1e-1), name='weights')
            conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
            biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
                                 trainable=True, name='biases')
            out = tf.nn.bias_add(conv, biases)
            self.conv1_1 = tf.nn.relu(out, name=scope)
            self.parameters += [kernel, biases]

        # conv1_2
        with tf.name_scope('conv1_2') as scope:
            kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,
                                                     stddev=1e-1), name='weights')
            conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME')
            biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
                                 trainable=True, name='biases')
            out = tf.nn.bias_add(conv, biases)
            self.conv1_2 = tf.nn.relu(out, name=scope)
            self.parameters += [kernel, biases]

        # pool1
        self.pool1 = tf.nn.max_pool(self.conv1_2,
                               ksize=[1, 2, 2, 1],
                               strides=[1, 2, 2, 1],
                               padding='SAME',
                               name='pool1')

        # conv2_1
        with tf.name_scope('conv2_1') as scope:
            kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
                                                     stddev=1e-1), name='weights')
            conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')
            biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
                                 trainable=True, name='biases')
            out = tf.nn.bias_add(conv, biases)
            self.conv2_1 = tf.nn.relu(out, name=scope)
            self.parameters += [kernel, biases]

        # conv2_2
        with tf.name_scope('conv2_2') as scope:
            kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32,
                                                     stddev=1e-1), name='weights')
            conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME')
            biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
                                 trainable=True, name='biases')
            out = tf.nn.bias_add(conv, biases)
            self.conv2_2 = tf.nn.relu(out, name=scope)
            self.parameters += [kernel, biases]

        # pool2
        self.pool2 = tf.nn.max_pool(self.conv2_2,
                               ksize=[1, 2, 2, 1],
                               strides=[1, 2, 2, 1],
                               padding='SAME',
                               name='pool2')

        # conv3_1
        with tf.name_scope('conv3_1') as scope:
            kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,
                                                     stddev=1e-1), name='weights')
            conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')
            biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                                 trainable=True, name='biases')
            out = tf.nn.bias_add(conv, biases)
            self.conv3_1 = tf.nn.relu(out, name=scope)
            self.parameters += [kernel, biases]

        # conv3_2
        with tf.name_scope('conv3_2') as scope:
            kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
                                                     stddev=1e-1), name='weights')
            conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME')
            biases = tf.Variable(tf.constant(