106062525 高穎

load_datas.py

@@ -0,0 +1,95 @@
+#############################################################################################################
+# load features and labels                                                                                  #
+# feature dimension : (N,d), (i.e., N*4096), label dimension : (N,)                                         #
+# type of all the output will be "list"                                                                     #
+#############################################################################################################
+# There are two different setting of training and testing :                                                 #
+# The default setting (setting-0) is splitting the dataset into two parts, half for training, half for      #
+# testing. Training index: lab-1234, office-123, house-123. Testing index: lab-5678, office-456, house-456  #
+#############################################################################################################
+
+import sys, os
+import numpy as np
+import pdb
+import warnings
+import re
+
+def load_one_label_seq(path):
+    npy = np.load(path)
+    return npy
+
+
+def load_label_seqs(path, mode, index):
+    labels=[]
+    for i in range(len(index)):
+        loc = index[i][0]
+        idx = index[i][1]
+        labelnpy = os.path.join(path,loc,mode+"_left"+str(idx)+'.npy')
+        labels.append(load_one_label_seq(labelnpy))
+        labelnpy = os.path.join(path,loc,mode+"_right"+str(idx)+'.npy')
+        labels.append(load_one_label_seq(labelnpy))
+
+    return labels
+
+def gen_index(setting_index):
+    train_index=[]
+    test_index =[]
+    if setting_index == 0:   
+        for i in range(1,9):
+            if i <= 4:
+                train_index.append(('lab',i))
+            else:
+                test_index.append(('lab',i))
+        for i in range(1,7):
+            if i <= 3:
+                train_index.append(('office',i))
+            else:
+                test_index.append(('office',i))
+        for i in range(1,7):
+            if i <= 3:
+                train_index.append(('house',i))
+            else:
+                test_index.append(('house',i))
+    else:
+        raise ValueError ('error setting index')
+
+    return train_index, test_index
+
+
+
+def gen_index_process(index=None, setting_index=None):
+    if index == None:
+        if setting_index==None:
+            raise ValueError('Setting index can not be none')
+        else:
+            train_index, test_index = gen_index(setting_index)
+    return train_index, test_index
+
+
+def load_train_labels(path, mode, index=None, setting_index=None):
+    if index == None:
+        index,_ = gen_index_process(index,setting_index)
+    else:
+        if setting_index != None:
+            warnings.warn('setting_index has no effect when given particular index')
+    labels = load_label_seqs(path, mode, index)
+    return labels
+
+def load_test_labels(path, mode, index=None, setting_index=None):
+    if index == None:
+        _,index = gen_index_process(index,setting_index)
+    else:
+        if setting_index != None:
+            warnings.warn('setting_index has no effect when given particular index')
+    labels = load_label_seqs(path, mode, index)
+    return labels
+
+
+def load_all_labels(path, mode, setting_index):
+    train_index, test_index = (setting_index)
+    train_labels = load_train_labels(path, mode,train_index)
+    test_labels = load_train_labels(path, mode,test_index)
+    return train_labels, test_labels
+
+
+

results/index.md

@@ -1,25 +1,26 @@
-# Your Name <span style="color:red">(id)</span>
+# 高穎  106062525 <span style="color:red">(id)</span>
 
 #Project 5: Deep Classification
 
 ## Overview
 The project is related to 
-> quote
+ >tensorflow
+ >vgg16
 
+>model和權重如下
+https://drive.google.com/drive/folders/0B4i3Xp6AJX7YSExOMEMxYTBxWnM?usp=sharing
 
 ## Implementation
-1. One
-	* item
-	* item
-2. Two
+1. tensorflow_finetune
+	用ImageNet pretrain好的vgg16參數,前七層不改,最後一層fc重新train.每train一個epoch就把train好的權重記下來
+2. load_datas
+	load training和test的label
+3.tensorflow_test
+	用記下來的權重加上test的檔案計算準確度,因為時間不夠所以沒有test完全部資料夾,只test了lab資料夾 準確度0.41
 
-```
-Code highlights
-```
 
 ## Installation
-* Other required packages.
-* How to compile from source?
+* 用anaconda建一個有tensorflow的虛擬環境,利用spyder編譯
 
 ### Results