From 2871f3b080c27886c887ae18cff13de0a7c9e811 Mon Sep 17 00:00:00 2001 From: Ethan Date: Wed, 8 Nov 2017 11:37:49 +0800 Subject: [PATCH 01/17] add all drafts. --- ...53\345\222\214\345\256\232\344\275\215.md" | 35 +++++ ...71\350\261\241\346\243\200\346\265\213.md" | 56 ++++++++ ...01\347\247\273\345\255\246\344\271\240.md" | 1 + .../11-Fast\345\222\214Faster.md" | 1 + .../11-R-CNN.md" | 1 + .../11-YOYO.md" | 1 + .../12-LSTM.md" | 1 + .../12-RNN.md" | 1 + ...17\345\212\233\346\250\241\345\236\213.md" | 1 + ...\346\213\276\351\201\227\357\274\232BN.md" | 1 + .../20-\345\257\271\346\212\227.md" | 1 + .../21-\345\274\272\345\214\226.md" | 1 + ...46\213\276\351\201\227\357\274\232Adam.md" | 1 + ...213\276\351\201\227\357\274\232Dropout.md" | 1 + ...15\345\220\221\344\274\240\346\222\255.md" | 0 .../8-GPU.md" | 1 + .../8-\347\273\217\345\205\270CNN.md" | 127 ++++++++++++++++++ ...NN\345\217\257\350\247\206\345\214\226.md" | 1 + 18 files changed, 232 insertions(+) create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\350\277\201\347\247\273\345\255\246\344\271\240.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-LSTM.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-RNN.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/20-\345\257\271\346\212\227.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/21-\345\274\272\345\214\226.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Adam.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Dropout.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/7-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232CNN\347\232\204\345\217\215\345\220\221\344\274\240\346\222\255.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-GPU.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-CNN\345\217\257\350\247\206\345\214\226.md" diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" new file mode 100644 index 0000000..8756201 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" @@ -0,0 +1,35 @@ +# 识别和定位 + +## 适用场景 + +- 一个类别,一个目标; +- 一个类别,多个固定目标; + +## 基本思想 + +识别+用回归做定位: + +- 一个类别,一个目标位置:分类+定位; +- 一个类别,一个目标的多个部分(数量固定)位置:分类+定位; + - DeepPose:Toshev and Szegedy, “DeepPose: Human Pose Estimation via Deep Neural Networks”, CVPR 2014; + +## 步骤 + +1. 先训练CNN分类器,或者下载Alex、VGG或者GoogleNet; +2. 在全连接层之前加入一个定位回归全连接块; +3. 用自己的数据集,使用L2距离作为损失度量训练这个定位回归块; +4. 进行测试,会产生两组结果,一组分类结果,一组回归定位结果; + +## 扩展:sliding window + +Overfeat:Sermanet et al, “Integrated Recognition, Localization and Detection using Convolutional Networks”, ICLR 2014; + +### 特点 + +- 利用分类+定位,应用到大分辨率图片,可以进行目标检测; +- 用卷积层替换全连接层,计算更加高效; +- 合并所有尺度上的分类得分和位置回归,形成最终预测; + +### 缺点 + +每个滑窗都要进行识别+回归定位,计算量非常大。 \ No newline at end of file diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" new file mode 100644 index 0000000..d1d6f1b --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -0,0 +1,56 @@ +# 对象检测 + +## 场景 + +- 多个类别(不固定) +- 多个目标位置(不固定); + +## 方案1:Detection as Classification + +把回归当成分类,扫描框即定位,需要遍历探测许多位置、许多尺度,最终进行合并形成定位。 + +### 发展 + +- 2005年,HOG,方向梯度直方图,用快速的线性分类器进行多位置、多尺度检测,应用NMS,Dalal and Triggs,“Histograms of Oriented Gradients for Human Detection”, CVPR 2005 Slide credit: Ross Girshick; +- 2010年,DPM,Deformable Parts Model; + +## 方案2:Region Proposals + +不全部遍历,只查看一些有可能的区域,通过图像像素级别的相似特征,不进行分类识别,只输出大量的目标框。 + +### 方法 + +- Selective Search,从像素出发,合并相似region,Convert regions to boxes,并在不同尺度上进行操作,可产生大量的候选区域,Uijlings et al, “Selective Search for Object Recognition”, IJCV 2013; +- EdgeBoxes,另一种优秀方法,Hosang et al, “What makes for effective detection proposals?”, PAMI 2015; + +### 特点: + +- 速度快,每张图片3秒~10秒; + +## 方案3:R-CNN + +R-CNN,Regions with CNN features,将候选区域搜索与CNN结合。 + +2014年,R-CNN,先由a Proposal method产生2K个RoI(Regions of Interest),然后进行SVM分类+回归定位操作。 + +Girschick et al, “Rich feature hierarchies for accurate object detection and semantic segmentation”, CVPR 2014。 + +### 训练步骤 + +1. 训练CNN分类模型,或者用训练好的AlexNet; +2. 替换CNN的全连接层,改为输出21个目标类别,用基于检测集产生的正、负片进行分类训练; +3. 遍历检测集每张图片的每个候选区域,用第2部训练好的CNN进行特征提取,然后保存到disk; +4. 将特征用于分别训练BBox和SVM; + +### 基于检测集Pascal + + + +### 缺点: + +- 训练过程复杂,点对点目标训练; +- 训练慢,占用大量磁盘空间; +- 检测慢,基于VGG16每张图片需要47秒; + +## 方案4:Fast R-CNN + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\350\277\201\347\247\273\345\255\246\344\271\240.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\350\277\201\347\247\273\345\255\246\344\271\240.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\350\277\201\347\247\273\345\255\246\344\271\240.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-LSTM.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-LSTM.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-LSTM.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-RNN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-RNN.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-RNN.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/20-\345\257\271\346\212\227.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/20-\345\257\271\346\212\227.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/20-\345\257\271\346\212\227.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/21-\345\274\272\345\214\226.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/21-\345\274\272\345\214\226.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/21-\345\274\272\345\214\226.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Adam.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Adam.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Adam.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Dropout.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Dropout.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/6-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232Dropout.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/7-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232CNN\347\232\204\345\217\215\345\220\221\344\274\240\346\222\255.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/7-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232CNN\347\232\204\345\217\215\345\220\221\344\274\240\346\222\255.md" new file mode 100644 index 0000000..e69de29 diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-GPU.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-GPU.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-GPU.md" @@ -0,0 +1 @@ + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" new file mode 100644 index 0000000..bc52558 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" @@ -0,0 +1,127 @@ +# 经典CNN + +## Alex(2012) + +### 基本信息 + +- 错误率:16.4%; +- 6000万个参数; +- 5个卷积层,3个全连接层; + +### 架构 + +- CONV1 +- MAX POOL1 +- NORM1 +- CONV2 +- MAX POOL2 +- NORM2 +- CONV3 +- CONV4 +- CONV5 +- MAXPOOL3 +- FC6 +- FC7 +- FC8 + +### 技术特点 + +- 首次使用ReLU; +- 使用Norm层(现在不常见了); +- 大量数据扩展; +- 使用SGD Momentum 0.9; +- Dropout 0.5; +- 7 CNN ensemble; +- L2正则; + +### 超参数 + +- batch size 128; +- learning rate 1e-2,手动减小当精度趋于平缓时; + +## ZFNet(2013) + +### 基本信息 + +- 错误率:11.7% +- 8层; + +### 技术特点 + +- 基于AlexNet; +- CONV1:从(11x11,stride 4)变为(7x7 stride 2) +- CONV3,4,5:由384,384,256变为512,1024,512 + +## VGG(2014) + +### 基本信息 + +- 错误率:7.3% +- 19层; + + +### 架构 + +- conv3-64 +- conv3-64 +- maxpool +- conv3-128 +- conv3-128 +- maxpool +- conv3-256 +- conv3-256 +- conv3-256 +- conv3-256 +- maxpool +- conv3-512 +- conv3-512 +- conv3-512 +- conv3-512 +- maxpool +- conv3-512 +- conv3-512 +- conv3-512 +- conv3-512 +- maxpool +- FC-4096 +- FC-4096 +- FC-1000 +- softmax + +### 技术特点 + +- 3x3 CONV stride 1,pad 1; +- 2x2 MAX POOL stride 2; + +### 启示 + +- LRN层作用不大; +- 越深的网络效果越好; +- 1x1的卷积也是很有效的,但是没有3x3的卷积好,大一些的卷积核可以学习更大的空间特征; +- 3个3x3的卷积层串联的效果相当于1个7x7的卷积层,前者只有后者55%的参数量,更多的非线性变换; + +## GoogleNet(2014) + +### 基本信息 + +- 错误率:6.70% +- 500百万参数; +- 比AlexNet速度快2倍; +- 22层; + +### 技术特点 + +- Inception module; +- 完全移除了全连接层; + +## ResNet(2015) + +### 基本信息 + +- 2~3周,8 GPU机器来进行训练; +- 运行速度比VGG要快; + +### 启示 + +- plain net +- residual diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-CNN\345\217\257\350\247\206\345\214\226.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-CNN\345\217\257\350\247\206\345\214\226.md" new file mode 100644 index 0000000..a5bc320 --- /dev/null +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-CNN\345\217\257\350\247\206\345\214\226.md" @@ -0,0 +1 @@ +https://www.youtube.com/watch?v=AgkfIQ4IGaM&t=22s 可视化 From 7375d9dd52b81da5f2fcf3cc0353c4e44657b4e9 Mon Sep 17 00:00:00 2001 From: Ethan Date: Wed, 8 Nov 2017 11:51:34 +0800 Subject: [PATCH 02/17] update 2 --- .../10-\345\257\271\350\261\241\346\243\200\346\265\213.md" | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" index d1d6f1b..11425ff 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -44,7 +44,10 @@ Girschick et al, “Rich feature hierarchies for accurate object detection and s ### 基于检测集Pascal - +1. +2. +3. +4. ### 缺点: From 9092ae1889d195ae48eb239067957b281b489f0d Mon Sep 17 00:00:00 2001 From: Ethan Date: Wed, 8 Nov 2017 17:40:13 +0800 Subject: [PATCH 03/17] update --- ...57\271\350\261\241\346\243\200\346\265\213.md" | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" index 11425ff..5c37fd1 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -38,16 +38,19 @@ Girschick et al, “Rich feature hierarchies for accurate object detection and s ### 训练步骤 1. 训练CNN分类模型,或者用训练好的AlexNet; -2. 替换CNN的全连接层,改为输出21个目标类别,用基于检测集产生的正、负片进行分类训练; +2. 替换CNN的最后一层全连接层,改为输出21个目标类别(20类对象+1类背景),用基于检测集产生的正、负片进行分类训练; 3. 遍历检测集每张图片的每个候选区域,用第2部训练好的CNN进行特征提取,然后保存到disk; 4. 将特征用于分别训练BBox和SVM; -### 基于检测集Pascal +### 基于检测集Pascal VOC的训练 -1. -2. -3. -4. +1. 检测集上的图片中可能有多个对象,以及各自的BBox; +2. selective search算法产生的框是训练之外的独立算法产生的,不能进行优化调整; +3. Fine-Tune CNN时,目标是训练21类的分类器; +4. Fine-Tune CNN时,是以selective search产生的region作为训练集的,这些region一开始并没有label,于是用IoU>0.5为正样本,否则为负样本(背景)的方式,通过对比Pascal VOC原始图片标注,获得了label,这样一方面可以训练出背景类,一方面相当于进行了数据集增强; +5. 对所有图片的所有Region,用CNN提取fc7的4096维特征,用于训练SVM; +6. 在SVM时,会对每个4096维特征,重新打label,负样本label依然是通过IoU的方式,IoU<0.3,但是正样本就是直接用原始BBox(比之前严格了),SVM训练数据量不用那么大; +7. 为每个类都训练一个二分类的SVM; ### 缺点: From e3b49c80e98fef3c05376fb04a658e14ea738986 Mon Sep 17 00:00:00 2001 From: Ethan Date: Wed, 8 Nov 2017 19:13:52 +0800 Subject: [PATCH 04/17] update --- ...-\345\257\271\350\261\241\346\243\200\346\265\213.md" | 9 +++++++++ .../11-Fast\345\222\214Faster.md" | 1 - .../TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" | 1 - .../TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" | 1 - 4 files changed, 9 insertions(+), 3 deletions(-) delete mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" delete mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" delete mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" index 5c37fd1..edd2cf3 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -58,5 +58,14 @@ Girschick et al, “Rich feature hierarchies for accurate object detection and s - 训练慢,占用大量磁盘空间; - 检测慢,基于VGG16每张图片需要47秒; +### 问题: + +- 如何精修边框? +- BBox的精度如何测量? + ## 方案4:Fast R-CNN +## 方案5:Faster R- CNN + +## 方案6:YOLO + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" deleted file mode 100644 index 8b13789..0000000 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-Fast\345\222\214Faster.md" +++ /dev/null @@ -1 +0,0 @@ - diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" deleted file mode 100644 index 8b13789..0000000 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-R-CNN.md" +++ /dev/null @@ -1 +0,0 @@ - diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" deleted file mode 100644 index 8b13789..0000000 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-YOYO.md" +++ /dev/null @@ -1 +0,0 @@ - From 3119f0653e13028a7789d0c5e1d3715c0215887a Mon Sep 17 00:00:00 2001 From: Ethan Date: Wed, 8 Nov 2017 19:20:02 +0800 Subject: [PATCH 05/17] update --- .../10-CNN\345\217\257\350\247\206\345\214\226.md" | 0 ...0\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" | 0 .../12-\345\257\271\350\261\241\346\243\200\346\265\213.md" | 0 .../TensorFlow\344\273\2161\345\210\2602/13-RNN.md" | 0 .../TensorFlow\344\273\2161\345\210\2602/14-LSTM.md" | 0 ...6\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" | 0 .../TensorFlow\344\273\2161\345\210\2602/8-GPU.md" | 1 - .../TensorFlow\344\273\2161\345\210\2602/9-GPU.md" | 0 8 files changed, 1 deletion(-) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-CNN\345\217\257\350\247\206\345\214\226.md" => 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"TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-GPU.md" From 45f9942a6b1390892ed0e49170caacfb520241a4 Mon Sep 17 00:00:00 2001 From: Ethan Date: Thu, 9 Nov 2017 15:12:50 +0800 Subject: [PATCH 06/17] update --- ...53\345\222\214\345\256\232\344\275\215.md" | 33 ++++++++++++++++--- ...71\350\261\241\346\243\200\346\265\213.md" | 20 +++++++++-- .../8-\347\273\217\345\205\270CNN.md" | 4 +-- 3 files changed, 48 insertions(+), 9 deletions(-) diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" index 8756201..b36399e 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" @@ -11,25 +11,48 @@ - 一个类别,一个目标位置:分类+定位; - 一个类别,一个目标的多个部分(数量固定)位置:分类+定位; - - DeepPose:Toshev and Szegedy, “DeepPose: Human Pose Estimation via Deep Neural Networks”, CVPR 2014; -## 步骤 +DeepPose:Toshev and Szegedy(Google), “DeepPose: Human Pose Estimation via Deep Neural Networks”, CVPR 2014; + +### 步骤 1. 先训练CNN分类器,或者下载Alex、VGG或者GoogleNet; 2. 在全连接层之前加入一个定位回归全连接块; 3. 用自己的数据集,使用L2距离作为损失度量训练这个定位回归块; 4. 进行测试,会产生两组结果,一组分类结果,一组回归定位结果; +### 回归的两种方式 + +- 不定类回归,class-agnostic regression,无论分类网络如何输出,不定类回归只输出一个框的4个参数即可; +- 特定类回归,class-specific regression,每个类别输出一个框的4个参数; + ## 扩展:sliding window Overfeat:Sermanet et al, “Integrated Recognition, Localization and Detection using Convolutional Networks”, ICLR 2014; ### 特点 -- 利用分类+定位,应用到大分辨率图片,可以进行目标检测; -- 用卷积层替换全连接层,计算更加高效; +- 利用分类+定位,通过滑窗,应用到大分辨率图片; +- 每个滑窗依然是做分类+定位操作; +- 每个滑窗处理的BBox,不一定都是在滑窗内部; +- 滑窗遍历一张图片后(多个尺度),聚合所有的滑窗结果,来确定目标的种类和位置; - 合并所有尺度上的分类得分和位置回归,形成最终预测; +- 用卷积层替换全连接层,计算更加高效; +- 可以进行目标检测; + +### 卷积层替换全连接层 + +- 与输入尺寸相同的卷积核,可以输出1x1xC的一维特征向量; +- 1x1的卷积核,输出原有的尺寸,这个过程中,进行了跨通道的信息整合,并增加了非线性转换,同时还可以调整输出Channel的数量(用于降维或者升维); ### 缺点 -每个滑窗都要进行识别+回归定位,计算量非常大。 \ No newline at end of file +每个滑窗都要进行识别+回归定位,计算量非常大。 + +### 问题 + +- 如何聚合每个滑窗的结果? + +## 总结 + +如果任务中的目标具有固定数量,使用定位框架会让问题变得简单。 \ No newline at end of file diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\345\257\271\350\261\241\346\243\200\346\265\213.md" index edd2cf3..2547fbe 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -60,12 +60,28 @@ Girschick et al, “Rich feature hierarchies for accurate object detection and s ### 问题: -- 如何精修边框? -- BBox的精度如何测量? +1. **如何精修边框**? +2. BBox的精度如何测量? +3. SS算法细节? +4. **TensorFlow如何做fine-tune**? ## 方案4:Fast R-CNN +### 问题: + +1. **联合loss实现?反向传播如何做**? +2. + ## 方案5:Faster R- CNN +### RPN,Region Proposal Network + +### 问题: + +1. **RPN如何产生regions**? + ## 方案6:YOLO + + +## 方案7:SSD diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" index bc52558..8803c04 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" @@ -105,14 +105,14 @@ ### 基本信息 - 错误率:6.70% -- 500百万参数; +- 500百万参数(参数量只有AlexNet 6000万的1/12); - 比AlexNet速度快2倍; - 22层; ### 技术特点 - Inception module; -- 完全移除了全连接层; +- 完全移除了全连接层,用全局平均池化层来取代,全连接层占据了VGG和AlexNet 90%的参数量; ## ResNet(2015) From 365b30a86c1f6593ad8f5cf93acab2235ef7fc45 Mon Sep 17 00:00:00 2001 From: Ethan Date: Thu, 9 Nov 2017 16:15:16 +0800 Subject: [PATCH 07/17] rename --- .../10-\347\273\217\345\205\270CNN.md" | 6 ++++++ ...40\347\216\221\346\213\276\351\201\227\357\274\232BN.md" | 0 .../TensorFlow\344\273\2161\345\210\2602/12-GPU.md" | 0 ...\206\345\210\253\345\222\214\345\256\232\344\275\215.md" | 0 .../14-\345\257\271\350\261\241\346\243\200\346\265\213.md" | 0 .../15-\345\233\276\345\203\217\345\210\206\345\211\262.md" | 0 ...\250\346\204\217\345\212\233\346\250\241\345\236\213.md" | 0 .../TensorFlow\344\273\2161\345\210\2602/17-RNN.md" | 0 .../TensorFlow\344\273\2161\345\210\2602/18-LSTM.md" | 0 ...7\216\221\346\213\276\351\201\227\357\274\232Dropout.md" | 0 ...\204\345\217\215\345\220\221\344\274\240\346\222\255.md" | 0 .../9-CNN\345\217\257\350\247\206\345\214\226.md" | 0 12 files changed, 6 insertions(+) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-\347\273\217\345\205\270CNN.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\347\273\217\345\205\270CNN.md" (84%) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-RNN.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" (100%) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/12-GPU.md" (100%) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" (100%) rename 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"b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-\347\273\217\345\205\270CNN.md" @@ -114,6 +114,12 @@ - Inception module; - 完全移除了全连接层,用全局平均池化层来取代,全连接层占据了VGG和AlexNet 90%的参数量; +### Inception Family + +- Inception V2,提出了BN,可以去除Dropout、LRN,传统的DNN在每一层的输入的分布都在变化,导致训练变得困难,我们只能使用一个很小的学习率解决这个问题。 +- Inception V3,非对称的卷积结构拆分,优化了Inception Module的结构; +- Inception V4,结合了微软的ResNet; + ## ResNet(2015) ### 基本信息 diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-RNN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-RNN.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/11-\347\217\240\347\216\221\346\213\276\351\201\227\357\274\232BN.md" diff --git 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"a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-CNN\347\232\204\345\217\215\345\220\221\344\274\240\346\222\255.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/8-CNN\347\232\204\345\217\215\345\220\221\344\274\240\346\222\255.md" new file mode 100644 index 0000000..e69de29 diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-CNN\345\217\257\350\247\206\345\214\226.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-CNN\345\217\257\350\247\206\345\214\226.md" similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/10-CNN\345\217\257\350\247\206\345\214\226.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/9-CNN\345\217\257\350\247\206\345\214\226.md" From 7636c018da3d3a36a144ea590d1a5f18ff8b4a2f Mon Sep 17 00:00:00 2001 From: Ethan Date: Thu, 9 Nov 2017 19:37:03 +0800 Subject: [PATCH 08/17] update --- .../14-\345\257\271\350\261\241\346\243\200\346\265\213.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" index 2547fbe..768a6aa 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -82,6 +82,10 @@ Girschick et al, “Rich feature hierarchies for accurate object detection and s ## 方案6:YOLO +### 问题: +1. 使用Pascal VOC如何训练? ## 方案7:SSD + +## 方案8:YOLO 9000 \ No newline at end of file From f5d801da6dd7fd5791dfa214ac35d983fe9e6866 Mon Sep 17 00:00:00 2001 From: Ethan Date: Fri, 10 Nov 2017 19:50:39 +0800 Subject: [PATCH 09/17] update --- ...71\350\261\241\346\243\200\346\265\213.md" | 38 ++++++++++++++++--- ...76\345\203\217\345\210\206\345\211\262.md" | 4 ++ ...56\346\240\207\350\267\237\350\270\252.md" | 0 .../18-RNN.md" | 0 .../19-LSTM.md" | 0 5 files changed, 37 insertions(+), 5 deletions(-) create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-RNN.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/18-RNN.md" (100%) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/18-LSTM.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/19-LSTM.md" (100%) diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" index 768a6aa..e918d26 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -67,25 +67,53 @@ Girschick et al, “Rich feature hierarchies for accurate object detection and s ## 方案4:Fast R-CNN +Girschick, “Fast R-CNN”, ICCV 2015。 + +### 技术特点 + +- ROI Pooling; +- 同时训练基于CNN的分类器和BBox回归; + ### 问题: 1. **联合loss实现?反向传播如何做**? -2. -## 方案5:Faster R- CNN +## 方案5:Faster R-CNN + +在2015年中期,一个微软研究员的团队 Shaoqing Ren,Kaiming He,Ross Girshick 和 Jian Sun,找到了一个方法来解决这个瓶颈问题,他们将这个方法命名为 Faster R-CNN。 + +Ren et al, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, NIPS 2015。 ### RPN,Region Proposal Network +- 基于FCN,Fully Conv Network; + ### 问题: 1. **RPN如何产生regions**? ## 方案6:YOLO -### 问题: +特点: -1. 使用Pascal VOC如何训练? +结构: + +- 基础CNN:GoogleNet(Inception Module有所简化); +- 有全连接层; ## 方案7:SSD -## 方案8:YOLO 9000 \ No newline at end of file +特点: + +- 具有同YOLO将detection转化为regression的思路; +- 具有同Faster RCNN中anchor box类似的prior box; +- 加入特征金字塔(Pyramidal Feature Hierarchy); + +结构: + +- VGG16; +- FCN; + +## 方案8:YOLO 9000 + +CVPR 2017 YOLO 9000。 \ No newline at end of file diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" index e69de29..be88fc4 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" @@ -0,0 +1,4 @@ +# 图像分割 + +## Mask R-CNN + diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" new file mode 100644 index 0000000..e69de29 diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-RNN.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/18-RNN.md" similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-RNN.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/18-RNN.md" diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/18-LSTM.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/19-LSTM.md" similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/18-LSTM.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/19-LSTM.md" From 28d9b7bc57fa20546f356677d344cbdcf7d25c6e Mon Sep 17 00:00:00 2001 From: Ethan Date: Tue, 14 Nov 2017 18:30:10 +0800 Subject: [PATCH 10/17] update. --- ...71\350\261\241\346\243\200\346\265\213.md" | 23 +++++++++++++++--- .../img/FastRCNN.jpg" | Bin 0 -> 43695 bytes .../img/FasterRCNN.jpg" | Bin 0 -> 54632 bytes .../img/RCNN.jpg" | Bin 0 -> 62886 bytes .../img/SSD.jpg" | Bin 0 -> 37194 bytes 5 files changed, 19 insertions(+), 4 deletions(-) create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/FastRCNN.jpg" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/FasterRCNN.jpg" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/RCNN.jpg" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/SSD.jpg" diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" index e918d26..ce23cad 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -60,7 +60,7 @@ Girschick et al, “Rich feature hierarchies for accurate object detection and s ### 问题: -1. **如何精修边框**? +1. **如何精修边框**? 2. BBox的精度如何测量? 3. SS算法细节? 4. **TensorFlow如何做fine-tune**? @@ -74,6 +74,11 @@ Girschick, “Fast R-CNN”, ICCV 2015。 - ROI Pooling; - 同时训练基于CNN的分类器和BBox回归; +### ROI Pooling + +1. ROI Pooling层的输入是两个来源,卷积层的feature map和ss产生的框; +2. 对于不同大小的框的feature maps,通过ROI Pooling,对每个框进行m x n划分,然后进行max pooling,最后输出大小相同的m x n的feature maps; + ### 问题: 1. **联合loss实现?反向传播如何做**? @@ -88,9 +93,12 @@ Ren et al, “Faster R-CNN: Towards Real-Time Object Detection with Region Propo - 基于FCN,Fully Conv Network; -### 问题: +### RPN算法 -1. **RPN如何产生regions**? +1. 生成锚框(anchor box); +2. 通过回归来进行修正; +3. 通过独立的softmax分类来决定是否是有效目标(不会分更具体的类别); +4. 输出有效的目标框; ## 方案6:YOLO @@ -101,7 +109,9 @@ Ren et al, “Faster R-CNN: Towards Real-Time Object Detection with Region Propo - 基础CNN:GoogleNet(Inception Module有所简化); - 有全连接层; -## 方案7:SSD +## 方案7:SSD(Single Shot) + +单发多框检测器; 特点: @@ -114,6 +124,11 @@ Ren et al, “Faster R-CNN: Towards Real-Time Object Detection with Region Propo - VGG16; - FCN; +### 如何生成anchor box + +- 可以以每个像素为中心都生成; +- 分类器有N+1(背景)类; + ## 方案8:YOLO 9000 CVPR 2017 YOLO 9000。 \ No newline at end of file diff --git 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update --- TF1_3/tf_2-6_checkpoint.ipynb | 248 ++++++++++++++++++ TF1_3/tf_2-6_checkpoint.py | 156 +++++++++++ .../tf_2-6_finetune.py | 0 .../img/cifar10.jpg" | Bin 0 -> 384222 bytes .../img/opencv.jpg" | Bin 0 -> 192755 bytes ...53\345\222\214\345\256\232\344\275\215.md" | 0 ...71\350\261\241\346\243\200\346\265\213.md" | 2 +- ...76\345\203\217\345\210\206\345\211\262.md" | 0 ...17\345\212\233\346\250\241\345\236\213.md" | 0 ...56\346\240\207\350\267\237\350\270\252.md" | 0 10 files changed, 405 insertions(+), 1 deletion(-) create mode 100644 TF1_3/tf_2-6_checkpoint.ipynb create mode 100644 TF1_3/tf_2-6_checkpoint.py rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" => TF1_3/tf_2-6_finetune.py (100%) create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/cifar10.jpg" create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/opencv.jpg" rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/13-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" (100%) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" (99%) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" (100%) rename "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/16-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" => "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/16-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" (100%) create mode 100644 "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" diff --git a/TF1_3/tf_2-6_checkpoint.ipynb b/TF1_3/tf_2-6_checkpoint.ipynb new file mode 100644 index 0000000..e5be7d9 --- /dev/null +++ b/TF1_3/tf_2-6_checkpoint.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import argparse\n", + "import sys\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "\n", + "FLAGS = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def weight_variable(shape):\n", + " initial = tf.truncated_normal(shape, stddev=0.1)\n", + " return tf.Variable(initial)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def bias_variable(shape):\n", + " initial = tf.constant(0.1, shape=shape)\n", + " return tf.Variable(initial)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def conv2d(x, W):\n", + " retufn tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def max_pool_2x2(x):\n", + " return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[1 1]\n", + " [2 2]\n", + " [3 3]]\n", + "\n", + " [[1 1]\n", + " [2 2]\n", + " [3 3]]]\n", + "(2, 3, 2)\n", + "(2,)\n", + "[[[2 3]\n", + " [3 4]\n", + " [4 5]]\n", + "\n", + " [[2 3]\n", + " [3 4]\n", + " [4 5]]]\n" + ] + } + ], + "source": [ + "a = np.array([[[1,1],[2,2],[3,3]],[[1,1],[2,2],[3,3]]])\n", + "print(a)\n", + "b = np.array([1,2])\n", + "print(np.shape(a))\n", + "print(np.shape(b))\n", + "c = a + b\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def deepnn(x):\n", + " with tf.name_scope('reshape'):\n", + " x_image = tf.reshape(x, [-1, 28, 28, 1])\n", + " \n", + " with tf.name_scope('conv1'):\n", + " W_conv1 = weight_variable([5, 5, 1, 32])\n", + " b_conv1 = bias_variable([32])\n", + " h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n", + " \n", + " with tf.name_scope('pool1'):\n", + " h_pool1 = max_pool_2x2(h_conv1)\n", + " \n", + " with tf.name_scope('conv2'):\n", + " W_conv2 = weight_variable([5, 5, 32, 64])\n", + " b_conv2 = bias_variable([64])\n", + " h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+ b_conv2)\n", + " \n", + " with tf.name_scope('pool2'):\n", + " h_pool2 = max_pool_2x2(h_conv2)\n", + " \n", + " with tf.name_scope('fc1'):\n", + " h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 *64])\n", + " W_fc1 = weight_variable([7 * 7 * 64, 1024])\n", + " b_fc1 = bias_variable([1024])\n", + " h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n", + " \n", + " with tf.name_scope('dropout'):\n", + " keep_prob = tf.placeholder(tf.float32)\n", + " h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n", + " \n", + " with tf.name_scope('fc2')\n", + " W_fc2 = weight_variable([1024, 10])\n", + " b_fc2 = bias_variable([10])\n", + " \n", + " y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n", + " return y_conv, keep_prob\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def main(_):\n", + " mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True,\n", + " validation_size=10000)\n", + " deep()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n" + ] + }, + { + "ename": "SystemExit", + "evalue": "", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[1;31mSystemExit\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Ethan\\Anaconda3\\envs\\tensorflow13\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2870: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", + " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" + ] + } + ], + "source": [ + "if __name__ == '__main__':\n", + " parser = argparse.ArgumentParser()\n", + " parser.add_argument('--data_dir', type=str, default='../MNIST/',\n", + " help='Directory for storing input data')\n", + " FLAGS, unparsed = parser.parse_known_args()\n", + " tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/TF1_3/tf_2-6_checkpoint.py b/TF1_3/tf_2-6_checkpoint.py new file mode 100644 index 0000000..2593c4a --- /dev/null +++ b/TF1_3/tf_2-6_checkpoint.py @@ -0,0 +1,156 @@ +import argparse +import sys +from tensorflow.examples.tutorials.mnist import input_data +import tensorflow as tf + +FLAGS = None + + +def deepnn(x): + """deepnn builds the graph for a deep net for classifying digits. + Args: + x: an input tensor with the dimensions (N_examples, 784), where 784 is + the number of pixels in a standard MNIST image. + Returns: + A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with + values equal to the logits of classifying the digit into one of 10 + classes (the digits 0-9). keep_prob is a scalar placeholder for the + probability of dropout. + """ + # Reshape to use within a convolutional neural net. + # Last dimension is for "features" - there is only one here, since images + # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. + with tf.name_scope('reshape'): + x_image = tf.reshape(x, [-1, 28, 28, 1]) + + # First convolutional layer - maps one grayscale image to 32 feature maps. + with tf.name_scope('conv1'): + W_conv1 = weight_variable([5, 5, 1, 32]) + b_conv1 = bias_variable([32]) + h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) + + # Pooling layer - downsamples by 2X. + with tf.name_scope('pool1'): + h_pool1 = max_pool_2x2(h_conv1) + + # Second convolutional layer -- maps 32 feature maps to 64. + with tf.name_scope('conv2'): + W_conv2 = weight_variable([5, 5, 32, 64]) + b_conv2 = bias_variable([64]) + h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) + + # Second pooling layer. + with tf.name_scope('pool2'): + h_pool2 = max_pool_2x2(h_conv2) + + # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image + # is down to 7x7x64 feature maps -- maps this to 1024 features. + with tf.name_scope('fc1'): + W_fc1 = weight_variable([7 * 7 * 64, 1024]) + b_fc1 = bias_variable([1024]) + + h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) + + # Dropout - controls the complexity of the model, prevents co-adaptation of + # features. + with tf.name_scope('dropout'): + keep_prob = tf.placeholder(tf.float32) + h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) + + # Map the 1024 features to 10 classes, one for each digit + with tf.name_scope('fc2'): + W_fc2 = weight_variable([1024, 10]) + b_fc2 = bias_variable([10]) + + y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 + return y_conv, keep_prob + + +def conv2d(x, W): + """conv2d returns a 2d convolution layer with full stride.""" + return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') + + +def max_pool_2x2(x): + """max_pool_2x2 downsamples a feature map by 2X.""" + return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], padding='SAME') + + +def weight_variable(shape): + """weight_variable generates a weight variable of a given shape.""" + initial = tf.truncated_normal(shape, stddev=0.1) + return tf.Variable(initial) + + +def bias_variable(shape): + """bias_variable generates a bias variable of a given shape.""" + initial = tf.constant(0.1, shape=shape) + return tf.Variable(initial) + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, + validation_size=10000) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + y_conv, keep_prob = deepnn(x) + + with tf.name_scope('loss'): + cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, + logits=y_conv) + cross_entropy = tf.reduce_mean(cross_entropy) + + with tf.name_scope('adam_optimizer'): + train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) + + with tf.name_scope('accuracy'): + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + correct_prediction = tf.cast(correct_prediction, tf.float32) + accuracy = tf.reduce_mean(correct_prediction) + + graph_location = 'MNIST/logs/tf2-6/train' + print('Saving graph to: %s' % graph_location) + train_writer = tf.summary.FileWriter(graph_location) + train_writer.add_graph(tf.get_default_graph()) + + saver = tf.train.Saver() + + best = 0 + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for epoch in range(60): + for _ in range(1000): + batch = mnist.train.next_batch(50) + train_step.run( + feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + accuracy_validation = accuracy.eval( + feed_dict={ + x: mnist.validation.images, + y_: mnist.validation.labels, + keep_prob: 1.0}) + print('epoch %d, validation accuracy %s' % ( + epoch, accuracy_validation)) + best = (best, accuracy_validation)[ + best <= accuracy_validation] + + saver.save(sess, graph_location + '/model.ckpt', epoch * _) + + # Test trained model + print("best: %s" % best) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default='../MNIST/', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) \ No newline at end of file diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" b/TF1_3/tf_2-6_finetune.py similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" rename to TF1_3/tf_2-6_finetune.py diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/cifar10.jpg" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/img/cifar10.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..1f03d1a7a5b90382ad3fed4a2c62e5220be1ce30 GIT binary patch literal 384222 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"a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/13-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/13-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/13-\345\233\276\345\203\217\350\257\206\345\210\253\345\222\214\345\256\232\344\275\215.md" diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" similarity index 99% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" index ce23cad..d97b6ef 100644 --- "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" +++ "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/14-\345\257\271\350\261\241\346\243\200\346\265\213.md" @@ -129,6 +129,6 @@ Ren et al, “Faster R-CNN: Towards Real-Time Object Detection with Region Propo - 可以以每个像素为中心都生成; - 分类器有N+1(背景)类; -## 方案8:YOLO 9000 +## 方案8:YOLO 9000 & YOLO v2 CVPR 2017 YOLO 9000。 \ No newline at end of file diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/15-\345\233\276\345\203\217\345\210\206\345\211\262.md" diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/16-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/16-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" similarity index 100% rename from "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2161\345\210\2602/16-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" rename to "TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/16-\346\263\250\346\204\217\345\212\233\346\250\241\345\236\213.md" diff --git "a/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" "b/TensorFlow\344\273\2160\345\210\260N/TensorFlow\344\273\2162\345\210\2603/17-\347\233\256\346\240\207\350\267\237\350\270\252.md" new file mode 100644 index 0000000..e69de29 From 5352cd12f9e250bf0093c4b9ddd0fb911fb36517 Mon Sep 17 00:00:00 2001 From: Ethan Date: Sat, 18 Nov 2017 19:41:14 +0800 Subject: [PATCH 12/17] update --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 6c4029f..a0434db 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ TF1_3/tf_2-3_tf_name_scope.py TensorFlow从0到N/ML* TensorFlow从0到N/手稿/* *.pptx +TF1_3/.ipynb_checkpoints/* From 19d6d3e581ac285d660f7ea182c81c6268d3133a Mon Sep 17 00:00:00 2001 From: Ethan Date: Mon, 20 Nov 2017 00:06:59 +0800 Subject: [PATCH 13/17] add restore example. --- TF1_3/tf_2-6_checkpoint.ipynb | 148 ++++++++++++++++++++++++---------- TF1_3/tf_2-6_checkpoint.py | 4 +- TF1_3/tf_2-6_restore.py | 48 +++++++++++ 3 files changed, 155 insertions(+), 45 deletions(-) create mode 100644 TF1_3/tf_2-6_restore.py diff --git a/TF1_3/tf_2-6_checkpoint.ipynb b/TF1_3/tf_2-6_checkpoint.ipynb index e5be7d9..2ee4b18 100644 --- a/TF1_3/tf_2-6_checkpoint.ipynb +++ b/TF1_3/tf_2-6_checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 40, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -45,19 +45,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv2d(x, W):\n", - " retufn tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')" + " return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -91,7 +91,17 @@ "\n", " [[2 3]\n", " [3 4]\n", - " [4 5]]]\n" + " [4 5]]]\n", + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" ] } ], @@ -102,12 +112,15 @@ "print(np.shape(a))\n", "print(np.shape(b))\n", "c = a + b\n", - "print(c)" + "print(c)\n", + "\n", + "for epoch in range(10):\n", + " print(epoch)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -143,7 +156,7 @@ " keep_prob = tf.placeholder(tf.float32)\n", " h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n", " \n", - " with tf.name_scope('fc2')\n", + " with tf.name_scope('fc2'):\n", " W_fc2 = weight_variable([1024, 10])\n", " b_fc2 = bias_variable([10])\n", " \n", @@ -154,45 +167,103 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 0]\n", + "[2 1]\n", + "[ True False]\n", + "[ 1. 0.]\n", + "0.5\n" + ] + } + ], + "source": [ + "y1 = np.array([[1, 2, 3],[6, 5, 4]])\n", + "y2 = np.array([[0.3, 0.4, 0.9],[0.5, 0.6, 0.6]])\n", + "p = tf.equal(tf.argmax(y1, 1),tf.argmax(y2, 1))\n", + "p2 = tf.cast(p, tf.float32)\n", + "p3 = tf.reduce_mean(p2)\n", + "\n", + "with tf.Session() as sess:\n", + " print(sess.run(tf.argmax(y1, 1)))\n", + " print(sess.run(tf.argmax(y2, 1)))\n", + " print(sess.run(p))\n", + " print(sess.run(p2))\n", + " print(sess.run(p3))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def main(_):\n", - " mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True,\n", - " validation_size=10000)\n", - " deep()" + " mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, validation_size=10000)\n", + " \n", + " x = tf.placeholder(tf.float32, [None, 784])\n", + " \n", + " y_ = tf.placeholder(tf.float32, [None, 10])\n", + " \n", + " y_conv, keep_prob = deepnn(x)\n", + " \n", + " with tf.name_scope('loss'):\n", + " cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)\n", + " cross_entropy = tf.reduce_mean(cross_entropy)\n", + " \n", + " with tf.name_scope('adam_optimizer'):\n", + " train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n", + " \n", + " with tf.name_scope('accuracy'):\n", + " correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n", + " correct_prediction = tf.cast(correct_prediction, tf.float32)\n", + " accuracy = tf.reduce_mean(correct_prediction)\n", + " \n", + " graph_location = 'MNIST/logs/tf2-6/train'\n", + " print('Saving graph to: %s' % graph_location)\n", + " train_writer = tf.summary.FileWriter(graph_location)\n", + " train_writer.add_graph(tf.get_default_graph())\n", + " \n", + " saver = tf.train.Saver()\n", + " \n", + " best = 0\n", + " with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " for epoch in range(60):\n", + " for _ in range(1000):\n", + " batch = mnist.train.next_batch(50)\n", + " train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n", + " accuracy_validation = accuracy.eval(feed_dict={x: mnist.validation.images, y_: mnist.validation.labels, keep_prob: 1.0})\n", + " print('epcho %d, validation accuracy %s' % (epoch+1, accuracy_validation))\n", + " best = (best, accuracy_validation)[best <= accuracy_validation]\n", + " \n", + " saver.save(sess, 'MNIST/logs/tf2-6/checkpoint/model.ckpt', epoch + 1)\n", + " \n", + " print(\"best: %s\" % best)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hello!\n" - ] - }, - { - "ename": "SystemExit", - "evalue": "", - "output_type": "error", - "traceback": [ - "An exception has occurred, use %tb to see the full traceback.\n", - "\u001b[1;31mSystemExit\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Ethan\\Anaconda3\\envs\\tensorflow13\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2870: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", - " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" + "Extracting ../MNIST/train-images-idx3-ubyte.gz\n", + "Extracting ../MNIST/train-labels-idx1-ubyte.gz\n", + "Extracting ../MNIST/t10k-images-idx3-ubyte.gz\n", + "Extracting ../MNIST/t10k-labels-idx1-ubyte.gz\n", + "Saving graph to: MNIST/logs/tf2-6/train\n", + "epcho 1, validation accuracy 0.9616\n" ] } ], @@ -205,15 +276,6 @@ " tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, diff --git a/TF1_3/tf_2-6_checkpoint.py b/TF1_3/tf_2-6_checkpoint.py index 2593c4a..e1a3be1 100644 --- a/TF1_3/tf_2-6_checkpoint.py +++ b/TF1_3/tf_2-6_checkpoint.py @@ -138,11 +138,11 @@ def main(_): y_: mnist.validation.labels, keep_prob: 1.0}) print('epoch %d, validation accuracy %s' % ( - epoch, accuracy_validation)) + epoch + 1, accuracy_validation)) best = (best, accuracy_validation)[ best <= accuracy_validation] - saver.save(sess, graph_location + '/model.ckpt', epoch * _) + saver.save(sess, 'MNIST/logs/tf2-6/checkpoint/model.ckpt', epoch + 1) # Test trained model print("best: %s" % best) diff --git a/TF1_3/tf_2-6_restore.py b/TF1_3/tf_2-6_restore.py new file mode 100644 index 0000000..770edae --- /dev/null +++ b/TF1_3/tf_2-6_restore.py @@ -0,0 +1,48 @@ +import argparse +import sys +from tensorflow.examples.tutorials.mnist import input_data +import tensorflow as tf +from importlib import import_module +import_module('tf_2-6_checkpoint') + +FLAGS = None + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, + validation_size=10000) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + cnn = sys.modules['tf_2-6_checkpoint'] + y_conv, keep_prob = cnn.deepnn(x) + + with tf.name_scope('accuracy'): + correct_prediction = tf.equal( + tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + correct_prediction = tf.cast(correct_prediction, tf.float32) + accuracy = tf.reduce_mean(correct_prediction) + + with tf.Session() as sess: + ckpt = tf.train.get_checkpoint_state('MNIST/logs/tf2-6/checkpoint') + tf.train.Saver().restore(sess, ckpt.model_checkpoint_path) + accuracy_test = accuracy.eval( + feed_dict={ + x: mnist.test.images, + y_: mnist.test.labels, + keep_prob: 1.0}) + print('test accuracy %s' % accuracy_test) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default='../MNIST/', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) From 1e1d7260dcbd233ec4e751dd39936a1c771143d5 Mon Sep 17 00:00:00 2001 From: Ethan Date: Mon, 20 Nov 2017 14:32:10 +0800 Subject: [PATCH 14/17] add fine tune example. --- .gitignore | 1 + TF1_3/tf_2-6_finetune.ipynb | 188 ++++++++++++++++++++++++++++++++++++ TF1_3/tf_2-6_finetune.py | 79 +++++++++++++++ TF1_3/tf_2-6_restore.py | 1 + 4 files changed, 269 insertions(+) create mode 100644 TF1_3/tf_2-6_finetune.ipynb diff --git a/.gitignore b/.gitignore index a0434db..9b4dd10 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,4 @@ TensorFlow从0到N/ML* TensorFlow从0到N/手稿/* *.pptx TF1_3/.ipynb_checkpoints/* +TF1_3/__pycache__/* \ No newline at end of file diff --git a/TF1_3/tf_2-6_finetune.ipynb b/TF1_3/tf_2-6_finetune.ipynb new file mode 100644 index 0000000..f6af753 --- /dev/null +++ b/TF1_3/tf_2-6_finetune.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import argparse\n", + "import sys\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "import tensorflow as tf\n", + "from importlib import import_module\n", + "import_module('tf_2-6_checkpoint')\n", + "\n", + "FLAGS = None\n", + "\n", + "\n", + "def cnn():\n", + " # Create the model\n", + " x = tf.placeholder(tf.float32, [None, 784])\n", + "\n", + " # Define loss and optimizer\n", + " y_ = tf.placeholder(tf.float32, [None, 10])\n", + " \n", + " # Build the graph for the deep net\n", + " cnn = sys.modules['tf_2-6_checkpoint']\n", + " y_conv, keep_prob = cnn.deepnn(x)\n", + " return y_conv, keep_prob, x, y_ " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_conv, keep_prob, x, y_ = cnn()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "[, ]\n" + ] + } + ], + "source": [ + "for var in tf.trainable_variables():\n", + " print(var)\n", + " \n", + "fine_tune_var_list = [i for i in tf.trainable_variables() if 'fc2' in i.name]\n", + "print(fine_tune_var_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def main(_):\n", + " # Import data\n", + " mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True,\n", + " validation_size=10000)\n", + " \n", + " with tf.name_scope('loss'):\n", + " cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)\n", + " cross_entropy = tf.reduce_mean(cross_entropy)\n", + " \n", + " with tf.name_scope('adam_optimizer'):\n", + " gradients = tf.gradients(cross_entropy, fine_tune_var_list)\n", + " gradients = list(zip(gradients, fine_tune_var_list))\n", + " optimizer = tf.train.AdamOptimizer(1e-4)\n", + " train_step = optimizer.apply_gradients(grads_and_vars=gradients)\n", + " \n", + " with tf.name_scope('accuracy'):\n", + " correct_prediction = tf.equal(\n", + " tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n", + " correct_prediction = tf.cast(correct_prediction, tf.float32)\n", + " accuracy = tf.reduce_mean(correct_prediction)\n", + "\n", + " best = 0\n", + " with tf.Session() as sess:\n", + " ckpt = tf.train.get_checkpoint_state('MNIST/logs/tf2-6/checkpoint')\n", + " tf.train.Saver().restore(sess, ckpt.model_checkpoint_path)\n", + " for epoch in range(20):\n", + " for _ in range(1000):\n", + " batch = mnist.train.next_batch(50)\n", + " train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n", + " accuracy_validation = accuracy.eval(\n", + " feed_dict={x: mnist.validation.images,\n", + " y_: mnist.validation.labels,\n", + " keep_prob: 1.0})\n", + " print('epoch %d, validation accuracy %s' % (epoch + 1, accuracy_validation))\n", + " best = (best, accuracy_validation)[best <= accuracy_validation]\n", + "\n", + " # Test trained model\n", + " print(\"best: %s\" % best)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ../MNIST/train-images-idx3-ubyte.gz\n", + "Extracting ../MNIST/train-labels-idx1-ubyte.gz\n", + "Extracting ../MNIST/t10k-images-idx3-ubyte.gz\n", + "Extracting ../MNIST/t10k-labels-idx1-ubyte.gz\n", + "INFO:tensorflow:Restoring parameters from MNIST/logs/tf2-6/checkpoint\\model.ckpt-60\n" + ] + } + ], + "source": [ + "if __name__ == '__main__':\n", + " parser = argparse.ArgumentParser()\n", + " parser.add_argument('--data_dir', type=str, default='../MNIST/',\n", + " help='Directory for storing input data')\n", + " FLAGS, unparsed = parser.parse_known_args()\n", + " tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/TF1_3/tf_2-6_finetune.py b/TF1_3/tf_2-6_finetune.py index e69de29..344b392 100644 --- a/TF1_3/tf_2-6_finetune.py +++ b/TF1_3/tf_2-6_finetune.py @@ -0,0 +1,79 @@ +import argparse +import sys +from tensorflow.examples.tutorials.mnist import input_data +import tensorflow as tf +from importlib import import_module +import_module('tf_2-6_checkpoint') + +FLAGS = None + + +def cnn(): + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + cnn = sys.modules['tf_2-6_checkpoint'] + y_conv, keep_prob = cnn.deepnn(x) + + return y_conv, keep_prob, x, y_ + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, + validation_size=10000) + + y_conv, keep_prob, x, y_ = cnn() + + fine_tune_var_list = [ + i for i in tf.trainable_variables() if 'fc2' in i.name] + + with tf.name_scope('loss'): + cross_entropy = tf.nn.softmax_cross_entropy_with_logits( + labels=y_, logits=y_conv) + cross_entropy = tf.reduce_mean(cross_entropy) + + with tf.name_scope('adam_optimizer'): + gradients = tf.gradients(cross_entropy, fine_tune_var_list) + gradients = list(zip(gradients, fine_tune_var_list)) + optimizer = tf.train.AdamOptimizer(1e-4) + train_step = optimizer.apply_gradients(grads_and_vars=gradients) + + with tf.name_scope('accuracy'): + correct_prediction = tf.equal( + tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + correct_prediction = tf.cast(correct_prediction, tf.float32) + accuracy = tf.reduce_mean(correct_prediction) + + best = 0 + with tf.Session() as sess: + ckpt = tf.train.get_checkpoint_state('MNIST/logs/tf2-6/checkpoint') + tf.train.Saver().restore(sess, ckpt.model_checkpoint_path) + + for epoch in range(20): + for _ in range(1000): + batch = mnist.train.next_batch(50) + train_step.run( + feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + accuracy_validation = accuracy.eval( + feed_dict={x: mnist.validation.images, + y_: mnist.validation.labels, + keep_prob: 1.0}) + print('epoch %d, validation accuracy %s' % + (epoch + 1, accuracy_validation)) + best = (best, accuracy_validation)[best <= accuracy_validation] + + # Test trained model + print("best: %s" % best) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default='../MNIST/', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/TF1_3/tf_2-6_restore.py b/TF1_3/tf_2-6_restore.py index 770edae..9e5bed4 100644 --- a/TF1_3/tf_2-6_restore.py +++ b/TF1_3/tf_2-6_restore.py @@ -32,6 +32,7 @@ def main(_): with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state('MNIST/logs/tf2-6/checkpoint') tf.train.Saver().restore(sess, ckpt.model_checkpoint_path) + print(ckpt) accuracy_test = accuracy.eval( feed_dict={ x: mnist.test.images, From 2dbb13c8bb32364606e5d7efad944226913e724c Mon Sep 17 00:00:00 2001 From: Ethan Date: Mon, 20 Nov 2017 15:39:45 +0800 Subject: [PATCH 15/17] remove global_step para in save op. --- TF1_3/tf_2-6_checkpoint.ipynb | 2 +- TF1_3/tf_2-6_checkpoint.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/TF1_3/tf_2-6_checkpoint.ipynb b/TF1_3/tf_2-6_checkpoint.ipynb index 2ee4b18..605454e 100644 --- a/TF1_3/tf_2-6_checkpoint.ipynb +++ b/TF1_3/tf_2-6_checkpoint.ipynb @@ -244,7 +244,7 @@ " print('epcho %d, validation accuracy %s' % (epoch+1, accuracy_validation))\n", " best = (best, accuracy_validation)[best <= accuracy_validation]\n", " \n", - " saver.save(sess, 'MNIST/logs/tf2-6/checkpoint/model.ckpt', epoch + 1)\n", + " saver.save(sess, 'MNIST/logs/tf2-6/checkpoint/model.ckpt')\n", " \n", " print(\"best: %s\" % best)" ] diff --git a/TF1_3/tf_2-6_checkpoint.py b/TF1_3/tf_2-6_checkpoint.py index e1a3be1..fab2193 100644 --- a/TF1_3/tf_2-6_checkpoint.py +++ b/TF1_3/tf_2-6_checkpoint.py @@ -142,7 +142,7 @@ def main(_): best = (best, accuracy_validation)[ best <= accuracy_validation] - saver.save(sess, 'MNIST/logs/tf2-6/checkpoint/model.ckpt', epoch + 1) + saver.save(sess, 'MNIST/logs/tf2-6/checkpoint/model.ckpt') # Test trained model print("best: %s" % best) @@ -153,4 +153,4 @@ def main(_): parser.add_argument('--data_dir', type=str, default='../MNIST/', help='Directory for storing input data') FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) \ No newline at end of file + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) From 410255efb8db8a238c0ff6e8d4e9789445efa706 Mon Sep 17 00:00:00 2001 From: Ethan Date: Mon, 20 Nov 2017 19:50:48 +0800 Subject: [PATCH 16/17] update restore example --- TF1_3/tf_2-6_checkpoint_2.py | 95 +++++++++++++++++++ ...tore.py => tf_2-6_restore_prediction_1.py} | 0 TF1_3/tf_2-6_restore_prediction_2.py | 36 +++++++ 3 files changed, 131 insertions(+) create mode 100644 TF1_3/tf_2-6_checkpoint_2.py rename TF1_3/{tf_2-6_restore.py => tf_2-6_restore_prediction_1.py} (100%) create mode 100644 TF1_3/tf_2-6_restore_prediction_2.py diff --git a/TF1_3/tf_2-6_checkpoint_2.py b/TF1_3/tf_2-6_checkpoint_2.py new file mode 100644 index 0000000..f64f187 --- /dev/null +++ b/TF1_3/tf_2-6_checkpoint_2.py @@ -0,0 +1,95 @@ +import argparse +import sys +from tensorflow.examples.tutorials.mnist import input_data +import tensorflow as tf + +FLAGS = None + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, + validation_size=10000) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784], name='x') + + with tf.name_scope('fc1'): + W_2 = tf.Variable(tf.random_normal([784, 100]) / tf.sqrt(784.0 / 2)) + b_2 = tf.Variable(tf.random_normal([100])) + z_2 = tf.matmul(x, W_2) + b_2 + a_2 = tf.nn.relu(z_2) + + with tf.name_scope('fc2'): + W_3 = tf.Variable(tf.random_normal([100, 100]) / tf.sqrt(100.0 / 2)) + b_3 = tf.Variable(tf.random_normal([100])) + z_3 = tf.matmul(a_2, W_3) + b_3 + a_3 = tf.nn.relu(z_3) + + with tf.name_scope('fc3'): + W_4 = tf.Variable(tf.random_normal([100, 10]) / tf.sqrt(100.0)) + b_4 = tf.Variable(tf.random_normal([10])) + z_4 = tf.matmul(a_3, W_4) + b_4 + a_4 = tf.sigmoid(z_4) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10], name='y_') + + tf.add_to_collection(tf.GraphKeys.WEIGHTS, W_2) + tf.add_to_collection(tf.GraphKeys.WEIGHTS, W_3) + tf.add_to_collection(tf.GraphKeys.WEIGHTS, W_4) + regularizer = tf.contrib.layers.l2_regularizer(scale=5.0 / 50000) + reg_term = tf.contrib.layers.apply_regularization(regularizer) + + with tf.name_scope('loss'): + loss = (tf.reduce_mean( + tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=z_4)) + + reg_term) + + with tf.name_scope('optimizer'): + train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss) + + sess = tf.InteractiveSession() + tf.global_variables_initializer().run() + + with tf.name_scope('accuracy'): + correct_prediction = tf.equal(tf.argmax(a_4, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean( + tf.cast(correct_prediction, tf.float32), name='accuracy') + + saver = tf.train.Saver() + + # Train + best = 0 + for epoch in range(30): + for _ in range(5000): + batch_xs, batch_ys = mnist.train.next_batch(10) + sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) + # Test trained model + accuracy_currut_train = sess.run( + accuracy, + feed_dict={x: mnist.train.images, + y_: mnist.train.labels}) + + accuracy_currut_validation = sess.run( + accuracy, + feed_dict={x: mnist.validation.images, + y_: mnist.validation.labels}) + + print("Epoch %s: train: %s validation: %s" + % (epoch, accuracy_currut_train, accuracy_currut_validation)) + best = (best, accuracy_currut_validation)[ + best <= accuracy_currut_validation] + + saver.save(sess, 'MNIST/logs/tf2-6/checkpoint_2/model.ckpt', epoch+1) + + # Test trained model + print("best: %s" % best) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default='../MNIST/', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/TF1_3/tf_2-6_restore.py b/TF1_3/tf_2-6_restore_prediction_1.py similarity index 100% rename from TF1_3/tf_2-6_restore.py rename to TF1_3/tf_2-6_restore_prediction_1.py diff --git a/TF1_3/tf_2-6_restore_prediction_2.py b/TF1_3/tf_2-6_restore_prediction_2.py new file mode 100644 index 0000000..74a9488 --- /dev/null +++ b/TF1_3/tf_2-6_restore_prediction_2.py @@ -0,0 +1,36 @@ +import argparse +import sys +from tensorflow.examples.tutorials.mnist import input_data +import tensorflow as tf +from importlib import import_module +import_module('tf_2-6_checkpoint') + +FLAGS = None + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, + validation_size=10000) + + with tf.Session() as sess: + saver = tf.train.import_meta_graph( + 'MNIST/logs/tf2-6/checkpoint_2/model.ckpt-10.meta') + saver.restore(sess, tf.train.latest_checkpoint( + 'MNIST/logs/tf2-6/checkpoint_2')) + + graph = tf.get_default_graph() + x = graph.get_tensor_by_name("x:0") + y_ = graph.get_tensor_by_name("y_:0") + print(x) + print(sess.run('accuracy/accuracy:0', feed_dict={ + x: mnist.test.images, + y_: mnist.test.labels})) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default='../MNIST/', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) From 41056437650c769df973cd5e0066fed3413af760 Mon Sep 17 00:00:00 2001 From: Ethan Date: Tue, 5 Dec 2017 09:53:29 +0800 Subject: [PATCH 17/17] update --- TF1_3/tf_2-6_checkpoint_2.py | 2 +- TF1_3/tf_2-6_restore_prediction_2.py | 16 ++++++++++++---- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/TF1_3/tf_2-6_checkpoint_2.py b/TF1_3/tf_2-6_checkpoint_2.py index f64f187..daefea9 100644 --- a/TF1_3/tf_2-6_checkpoint_2.py +++ b/TF1_3/tf_2-6_checkpoint_2.py @@ -24,7 +24,7 @@ def main(_): W_3 = tf.Variable(tf.random_normal([100, 100]) / tf.sqrt(100.0 / 2)) b_3 = tf.Variable(tf.random_normal([100])) z_3 = tf.matmul(a_2, W_3) + b_3 - a_3 = tf.nn.relu(z_3) + a_3 = tf.nn.relu(z_3, name='a_3') with tf.name_scope('fc3'): W_4 = tf.Variable(tf.random_normal([100, 10]) / tf.sqrt(100.0)) diff --git a/TF1_3/tf_2-6_restore_prediction_2.py b/TF1_3/tf_2-6_restore_prediction_2.py index 74a9488..846f0eb 100644 --- a/TF1_3/tf_2-6_restore_prediction_2.py +++ b/TF1_3/tf_2-6_restore_prediction_2.py @@ -15,19 +15,27 @@ def main(_): with tf.Session() as sess: saver = tf.train.import_meta_graph( - 'MNIST/logs/tf2-6/checkpoint_2/model.ckpt-10.meta') + 'MNIST/logs/tf2-6/checkpoint_2/model.ckpt-30.meta') saver.restore(sess, tf.train.latest_checkpoint( 'MNIST/logs/tf2-6/checkpoint_2')) graph = tf.get_default_graph() + a_3 = graph.get_tensor_by_name("fc2/a_3:0") + + with tf.name_scope('fc4_2'): + W_4 = tf.Variable(tf.random_normal([100, 10]) / tf.sqrt(100.0)) + b_4 = tf.Variable(tf.random_normal([10])) + z_4 = tf.matmul(a_3, W_4) + b_4 + a_4 = tf.sigmoid(z_4) + init_new_vars_op = tf.initialize_variables([W_4, b_4]) + sess.run(init_new_vars_op) + x = graph.get_tensor_by_name("x:0") y_ = graph.get_tensor_by_name("y_:0") - print(x) - print(sess.run('accuracy/accuracy:0', feed_dict={ + print(sess.run(a_4, feed_dict={ x: mnist.test.images, y_: mnist.test.labels})) - if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='../MNIST/',