library(tensorflow) #加载TensorFlow包

library(magrittr) #数据处理包，可以使用管道函数

slim= tf$contrib$slim #slim是一个使构建，训练，评估神经网络变得简单的库。

# slim提供了很多计算机视觉方面的著名模型（VGG, AlexNet等），我们不仅可以直接使用，甚至能以各种方式进行扩展。

tf$reset_default_graph() #在每次运行中清除当前图形，以避免变量重复#Session会话 张量的具体值和操作,会话关闭时，张量的任何具体值都会丢失

images = tf$placeholder(tf$float32, shape(NULL, NULL, NULL, 3))# 创建占位符

imgs_scaled = tf$image$resize_images(images, shape(224,224)) #设置图片大小# slim$conv2d自带卷积功能+激励函数 

fc8 = slim$conv2d(imgs_scaled, 64, shape(3,3), scope='vgg_16/conv1/conv1_1') %>% 
  slim$conv2d(64, shape(3,3), scope='vgg_16/conv1/conv1_2')  %>%
  slim$max_pool2d( shape(2, 2), scope='vgg_16/pool1')  %>% #池化操作

  slim$conv2d(128, shape(3,3), scope='vgg_16/conv2/conv2_1')  %>%
  slim$conv2d(128, shape(3,3), scope='vgg_16/conv2/conv2_2')  %>%
  slim$max_pool2d( shape(2, 2), scope='vgg_16/pool2')  %>%

  slim$conv2d(256, shape(3,3), scope='vgg_16/conv3/conv3_1')  %>%
  slim$conv2d(256, shape(3,3), scope='vgg_16/conv3/conv3_2')  %>%
  slim$conv2d(256, shape(3,3), scope='vgg_16/conv3/conv3_3')  %>%
  slim$max_pool2d(shape(2, 2), scope='vgg_16/pool3')  %>%

  slim$conv2d(512, shape(3,3), scope='vgg_16/conv4/conv4_1')  %>%
  slim$conv2d(512, shape(3,3), scope='vgg_16/conv4/conv4_2')  %>%
  slim$conv2d(512, shape(3,3), scope='vgg_16/conv4/conv4_3')  %>%
  slim$max_pool2d(shape(2, 2), scope='vgg_16/pool4')  %>%

  slim$conv2d(512, shape(3,3), scope='vgg_16/conv5/conv5_1')  %>%
  slim$conv2d(512, shape(3,3), scope='vgg_16/conv5/conv5_2')  %>%
  slim$conv2d(512, shape(3,3), scope='vgg_16/conv5/conv5_3')  %>%
  slim$max_pool2d(shape(2, 2), scope='vgg_16/pool5')  %>%

  slim$conv2d(4096, shape(7, 7), padding='VALID', scope='vgg_16/fc6')  %>%
  slim$conv2d(4096, shape(1, 1), scope='vgg_16/fc7') %>% 

  # Setting the activation_fn=NULL does not work, so we get a ReLU
  slim$conv2d(1000, shape(1, 1), scope='vgg_16/fc8')  %>%
  tf$squeeze(shape(1, 2), name='vgg_16/fc8/squeezed')

tf$summary$FileWriter('/tmp/dumm/vgg16', tf$get_default_graph())$close() #保存在/tmp/dumm/vgg16目录下

restorer = tf$train$Saver() #创建一个Saver 来管理模型中的所有变量。

sess = tf$Session()
restorer$restore(sess, 'vgg_16.ckpt') #复原模型

library(jpeg)
img1<-readJPEG('caomei.jpg') #img1的值在0-1之间。

d=dim(img1)  #获取img1的维度
imgs =array(255*img1,dim = c(1,d[1],d[2],d[3]))
#因为数值需要在0到225之间，所以需要乘225.形成一个四维数组#我们可以通过与存储在数组imgs中的图像的张量来对图像做预测

fc8_vals=sess$run(fc8,dict(images=imgs))#将fc8的张量存储在fc8_vals中。
fc8_vals[1:5]

probs=exp(fc8_vals)/sum(exp(fc8_vals))

#按概率从高排序，并取前五个。
idx=sort.int(fc8_vals,index.return = TRUE,decreasing = TRUE)$ix[1:5]

#读取图像分类文件
library(readr)
names = read_delim("imagenet_classes.txt", "\t", escape_double = FALSE, trim_ws = TRUE,col_names = FALSE)
library(grid) #图片处理的一个包，主要控制输出图形的大的外观和一些细节东西的排列

g = rasterGrob(img1, interpolate=TRUE)  #图形进行栅格化

text = ""for (id in idx) {
  text = paste0(text, names[id,][[1]], " ", round(probs[id],5), "\n") 
}

#annotate 添加文本注释
#annotation_custom 可以添加各种图形元素到ggplot图中

library(ggplot2)
ggplot(data.frame(d=1:3)) + annotation_custom(g) + 
  annotate('text',x=0.05,y=0.05,label=text, size=7, hjust = 0, vjust=0, color='blue') + xlim(0,1) + ylim(0,1)