105061634 hw1

results/checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "model_ges.ckpt"
+all_model_checkpoint_paths: "model_ges.ckpt"

results/data_resize.py

@@ -0,0 +1,199 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Oct  3 15:32:52 2017
+
+@author: nvlab
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Sep 30 12:13:40 2017
+
+@author: nvlab
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Sep 23 15:58:21 2017
+
+@author: nvlab
+"""
+
+import numpy as np
+from PIL import Image
+import scipy.misc
+
+global num_house1_L,num_house2_L,num_house3_L,num_lab1_L,num_lab2_L,num_lab3_L,num_lab4_L
+global num_house1_R,num_house2_R,num_house3_R,num_lab1_R,num_lab2_R,num_lab3_R,num_lab4_R
+global num_off1_R, num_off2_R, num_off3_R, num_off1_L, num_off2_L, num_off3_L
+global img_path
+num_house1_L = 1
+num_off1_L = 1
+num_off1_R = 1
+num_house2_L = 1
+num_off2_L = 1
+num_off2_R = 1
+num_house3_L = 1
+num_off3_L = 1
+num_off3_R = 1
+num_lab1_L = 1
+num_lab2_L = 1
+num_lab3_L = 1
+num_lab4_L = 1
+num_house1_R = 1
+num_house2_R = 1
+num_house3_R = 1
+num_lab1_R = 1
+num_lab2_R = 1
+num_lab3_R = 1
+num_lab4_R = 1
+def read_data(batch):
+
+    global num_house1_L,num_house2_L,num_house3_L,num_lab1_L,num_lab2_L,num_lab3_L,num_lab4_L
+    global num_house1_R,num_house2_R,num_house3_R,num_lab1_R,num_lab2_R,num_lab3_R,num_lab4_R
+    global num_off1_R, num_off2_R, num_off3_R, num_off1_L, num_off2_L, num_off3_L
+    global img_path
+    for i in range(batch):
+        if num_house1_L<=831:        
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/house/1/Lhand/Image%s.png"%(num_house1_L)
+            num_house1_L+=1
+        elif num_house1_R<=831:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/house/1/Rhand/Image%s.png"%(num_house1_R)
+            num_house1_R+=1
+        elif num_house2_L<=988:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/house/2/Lhand/Image%s.png"%(num_house2_L)
+            num_house2_L+=1
+        elif num_house2_R<=988:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/house/2/Rhand/Image%s.png"%(num_house2_R)
+            num_house2_R+=1
+        elif num_house3_L<=1229:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/house/3/Lhand/Image%s.png"%(num_house3_L)
+            num_house3_L+=1
+        elif num_house3_R<=1229:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/house/3/Rhand/Image%s.png"%(num_house3_R)
+            num_house3_R+=1
+        elif num_lab1_L<=501:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/1/Lhand/Image%s.png"%(num_lab1_L)
+            num_lab1_L+=1
+        elif num_lab1_R<=501:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/1/Rhand/Image%s.png"%(num_lab1_R)
+            num_lab1_R+=1
+        elif num_lab2_L<=589:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/2/Lhand/Image%s.png"%(num_lab2_L)
+            num_lab2_L+=1
+        elif num_lab2_R<=589:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/2/Rhand/Image%s.png"%(num_lab2_R)
+            num_lab2_R+=1
+        elif num_lab3_L<=730:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/3/Lhand/Image%s.png"%(num_lab3_L)
+            num_lab3_L+=1
+        elif num_lab3_R<=730:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/3/Rhand/Image%s.png"%(num_lab3_R)
+            num_lab3_R+=1
+        elif num_lab4_L<=660:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/4/Lhand/Image%s.png"%(num_lab4_L)
+            num_lab4_L+=1
+        elif num_lab4_R<=660:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/lab/4/Rhand/Image%s.png"%(num_lab4_R)
+            num_lab4_R+=1
+        elif num_off1_L<=745:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/office/1/Lhand/Image%s.png"%(num_off1_L)
+            num_off1_L+=1
+        elif num_off1_R<=745:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/office/1/Rhand/Image%s.png"%(num_off1_R)
+            num_off1_R+=1
+        elif num_off2_L<=572:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/office/2/Lhand/Image%s.png"%(num_off2_L)
+            num_off2_L+=1
+        elif num_off2_R<=572:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/office/2/Rhand/Image%s.png"%(num_off2_R)
+            num_off2_R+=1
+        elif num_off3_L<=651:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/office/3/Lhand/Image%s.png"%(num_off3_L)
+            num_off3_L+=1
+        elif num_off3_R<=651:
+            img_path = "../../../../Disk2/cedl/handcam/frames/train/office/3/Rhand/Image%s.png"%(num_off3_R)
+            num_off3_R+=1
+        img = Image.open(img_path)
+        img = np.array(img)
+        x_img = np.resize(img, [128,248,3])
+        x_img = x_img/255.0
+
+        scipy.misc.imsave('frames_resize/Image%s.png'%i, x_img)
+
+def read_data_test(batch):
+
+    global num_house1_L,num_house2_L,num_house3_L,num_lab1_L,num_lab2_L,num_lab3_L,num_lab4_L
+    global num_house1_R,num_house2_R,num_house3_R,num_lab1_R,num_lab2_R,num_lab3_R,num_lab4_R
+    global num_off1_R, num_off2_R, num_off3_R, num_off1_L, num_off2_L, num_off3_L
+    global img_path
+    for i in range(batch):
+        if num_house1_L<=830:        
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/house/1/Lhand/Image%s.png"%(num_house1_L)
+            num_house1_L+=1
+        elif num_house1_R<=830:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/house/1/Rhand/Image%s.png"%(num_house1_R)
+            num_house1_R+=1
+        elif num_house2_L<=887:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/house/2/Lhand/Image%s.png"%(num_house2_L)
+            num_house2_L+=1
+        elif num_house2_R<=887:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/house/2/Rhand/Image%s.png"%(num_house2_R)
+            num_house2_R+=1
+        elif num_house3_L<=929:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/house/3/Lhand/Image%s.png"%(num_house3_L)
+            num_house3_L+=1
+        elif num_house3_R<=929:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/house/3/Rhand/Image%s.png"%(num_house3_R)
+            num_house3_R+=1
+        elif num_lab1_L<=539:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/1/Lhand/Image%s.png"%(num_lab1_L)
+            num_lab1_L+=1
+        elif num_lab1_R<=539:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/1/Rhand/Image%s.png"%(num_lab1_R)
+            num_lab1_R+=1
+        elif num_lab2_L<=658:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/2/Lhand/Image%s.png"%(num_lab2_L)
+            num_lab2_L+=1
+        elif num_lab2_R<=658:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/2/Rhand/Image%s.png"%(num_lab2_R)
+            num_lab2_R+=1
+        elif num_lab3_L<=467:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/3/Lhand/Image%s.png"%(num_lab3_L)
+            num_lab3_L+=1
+        elif num_lab3_R<=467:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/3/Rhand/Image%s.png"%(num_lab3_R)
+            num_lab3_R+=1
+        elif num_lab4_L<=503:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/4/Lhand/Image%s.png"%(num_lab4_L)
+            num_lab4_L+=1
+        elif num_lab4_R<=503:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/lab/4/Rhand/Image%s.png"%(num_lab4_R)
+            num_lab4_R+=1
+        elif num_off1_L<=590:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/office/1/Lhand/Image%s.png"%(num_off1_L)
+            num_off1_L+=1
+        elif num_off1_R<=590:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/office/1/Rhand/Image%s.png"%(num_off1_R)
+            num_off1_R+=1
+        elif num_off2_L<=419:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/office/2/Lhand/Image%s.png"%(num_off2_L)
+            num_off2_L+=1
+        elif num_off2_R<=419:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/office/2/Rhand/Image%s.png"%(num_off2_R)
+            num_off2_R+=1
+        elif num_off3_L<=566:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/office/3/Lhand/Image%s.png"%(num_off3_L)
+            num_off3_L+=1
+        elif num_off3_R<=566:
+            img_path = "../../../../Disk2/cedl/handcam/frames/test/office/3/Rhand/Image%s.png"%(num_off3_R)
+            num_off3_R+=1
+        img = Image.open(img_path)
+        img = np.array(img)
+        x_img = np.resize(img, [128,248,3])
+        x_img = x_img/255.0
+
+        scipy.misc.imsave('test_resize/Image%s.png'%i, x_img)
+
+read_data_test(12776)
+read_data(14992)

results/index.md

@@ -1,44 +1,124 @@
-# Your Name <span style="color:red">(id)</span>
+# 邱煜淵 <span style="color:red">(105061634)</span>
 
 #Project 5: Deep Classification
 
 ## Overview
-The project is related to 
-> quote
+The project is related to object classification. There are two different case. One is every task has itself model. The other is just one model handle multiple task (Obj, Ged and FA). Some experiments photos are not already finish so I will make up its as soon.
+> quote https://github.com/CEDL2017/homework1
 
 
 ## Implementation
-1. One
-	* item
-	* item
-2. Two
+### Result
+<p>Average FA loss : 0.743
+<p>Average FA accuracy : 0.509 (use shallower network, train 50 epoch)
+<p>Average Ges loss : 1.7635
+<p>Average Ges accuracy : 0.9253 (use deeper network, train 60 epoch)
+<p>Average Obj loss : 2.268
+<p>Average Obj accuracy : 0.538
+
+### Spotlight code
 
 ```
-Code highlights
+with tf.name_scope('C1_Conv'):
+    W1 = weight([3,3,3,36])
+    b1 = bias([36])
+    Conv1 = conv2d(x_image, W1)+b1
+    Conv1_BN = BN(Conv1, 36)
+    C1_Conv = tf.nn.relu(Conv1_BN)  
+with tf.name_scope('C2_Conv'):
+    W2 = weight([3,3,36,36])
+    b2 = bias([36])
+    Conv2 = conv2d(C1_Conv, W2)+b2
+    Conv2_BN = BN(Conv2, 36)
+    C2_Conv = tf.nn.relu(Conv2_BN) 
+with tf.name_scope('C1_Pool'):
+    C1_Pool = max_pooling(C2_Conv)
+
+with tf.name_scope('C3_Conv'):
+    W3 = weight([3,3,36,64])
+    b3 = bias([64])
+    Conv3 = conv2d(C1_Pool, W3)+b3
+    Conv3_BN = BN(Conv3, 64)
+    C3_Conv = tf.nn.relu(Conv3_BN)
+C3_concate = tf.concat([C1_Pool, C3_Conv],3)
+with tf.name_scope('C4_Conv'):
+    W4 = weight([3,3,100,128])
+    b4 = bias([128])
+    Conv4 = conv2d(C3_concate, W4)+b4
+    Conv4_BN = BN(Conv4, 128)
+    C4_Conv = tf.nn.relu(Conv4_BN)    
+with tf.name_scope('C2_Pool'):
+    C2_Pool = max_pooling(C4_Conv)
+
+with tf.name_scope('C5_Conv'):
+    W8 = weight([3,3,128,128])
+    b8 = bias([128])
+    Conv5 = conv2d(C2_Pool, W8)+b8
+    Conv5_BN = BN(Conv5, 128)
+    C5_Conv = tf.nn.relu(Conv5_BN)
+C5_concate = tf.concat([C2_Pool, C5_Conv],3)
+with tf.name_scope('C6_Conv'):
+    W9 = weight([3,3,256,256])
+    b9 = bias([256])
+    Conv6 = conv2d(C5_concate, W9)+b9
+    Conv6_BN = BN(Conv6, 256)
+    C6_Conv = tf.nn.relu(Conv6_BN)    
+with tf.name_scope('C3_Pool'):
+    C3_Pool = max_pooling(C6_Conv)
+
+with tf.name_scope('C7_Conv'):
+    W10 = weight([3,3,256,256])
+    b10 = bias([256])
+    Conv7 = conv2d(C3_Pool, W10)+b10
+    Conv7_BN = BN(Conv7, 256)
+    C7_Conv = tf.nn.relu(Conv7_BN)
+C7_concate = tf.concat([C3_Pool, C7_Conv],3)
+with tf.name_scope('C8_Conv'):
+    W11 = weight([3,3,512,512])
+    b11 = bias([512])
+    Conv8 = conv2d(C7_concate, W11)+b11
+    Conv8_BN = BN(Conv8, 512)
+    C8_Conv = tf.nn.relu(Conv8_BN)    
+with tf.name_scope('C4_Pool'):
+    C4_Pool = max_pooling(C8_Conv)
 ```
 
 ## Installation
 * Other required packages.
+	* python2.7
+	* tensorflow
+	* numpy
+	* PIL
 * How to compile from source?
-
+	<p>First, you must resize the input image.<p>
+
+	>$ python data_resize.py
+
+	Than you can chose which task you want to train.
+
+	>$ python train.py or $ python train_obj.py
+
+	Last, you can test your model.
+
+	>$ python test.py
 ### Results
 
 <table border=1>
 <tr>
 <td>
-<img src="placeholder.jpg" width="24%"/>
-<img src="placeholder.jpg"  width="24%"/>
-<img src="placeholder.jpg" width="24%"/>
+<img src="multiple_loss.jpg" width="24%"/>
+<img src="multiple_acc.jpg"  width="24%"/>
+<img src="obj_precision-recall.png" width="24%"/>
 <img src="placeholder.jpg" width="24%"/>
 </td>
 </tr>
 
 <tr>
 <td>
-<img src="placeholder.jpg" width="24%"/>
-<img src="placeholder.jpg"  width="24%"/>
-<img src="placeholder.jpg" width="24%"/>
-<img src="placeholder.jpg" width="24%"/>
+<img src="ges_precision-recall.png" width="24%"/>
+<img src="ges.jpg"  width="24%"/>
+<img src="FA_precision-recall.png" width="24%"/>
+<img src="FA.png" width="24%"/>
 </td>
 </tr>