update

Author: andytu28

.gitignore

@@ -0,0 +1,4 @@
+*.pyc
+data/cifar10
+init_weights/vgg_cnn_f_rmlast.h5
+experiments

README.md

@@ -8,17 +8,29 @@ Learning-based hash has been widely used for large-scale similarity retrieval du
 
 
 ## Prerequisites 
-* python3.5
+* python2.7
 * keras2.3.0
 * tensorflow1.13.1
+* scikit-learn0.20.4
 
 Use the following command to install all requirements: 
 ```bash
 $ pip install -r requirements.txt
 ```
 
+If you want to evaluate the image retrieval performance, you need matlab as well. 
+
+
 ## Usage
 
+### Preparing data
+
+### Training 
+
+### Testing 
+
+### Evaluation (matlab required)
+
 
 ## Citation 
 Please cite following paper if these codes help your research:

init_weights/.place_holder

@@ -1,2 +1,3 @@
 tensorflow-gpu==1.13.1
 keras==2.3.0
+scikit-learn==0.20.4

requirements.txt

@@ -1,46 +1,46 @@
 [16bits.model]
 k = 1.0
-save_period = 50
-max_epochs = 250
+save_period = 100
+max_epochs = 200
 patience = 50
 
 [16bits.optim]
 optimizer = Adam
 lr = 0.00001
-decay = 1e-6
+decay = 1e-4
 
 
 [24bits.model]
 k = 1.0
-save_period = 50
-max_epochs = 250
+save_period = 100
+max_epochs = 200
 patience = 50
 
 [24bits.optim]
 optimizer = Adam
 lr = 0.00001
-decay = 1e-6
+decay = 1e-4
 
 
 [32bits.model]
 k = 1.0
-save_period = 50
-max_epochs = 250
+save_period = 100
+max_epochs = 200
 patience = 50
 
 [32bits.optim]
 optimizer = Adam
 lr = 0.00001
-decay = 1e-6
+decay = 1e-4
 
 
 [48bits.model]
 k = 1.0
-save_period = 50
-max_epochs = 250
+save_period = 100
+max_epochs = 200
 patience = 50
 
 [48bits.optim]
 optimizer = Adam
 lr = 0.00001
-decay = 1e-6
+decay = 1e-4

settings.cfg

@@ -5,6 +5,7 @@
 import importlib
 import tensorflow as tf
 import keras.backend as K
+from pathlib2 import Path
 from keras import optimizers
 from keras.models import load_model
 from keras.engine import Model
@@ -46,7 +47,8 @@ def run_train(args, optim_args):
 
     ## Initial preparation
     model_dir = os.path.join(args.exp_dir, 'models', args.config_opt)
-    os.makedirs(model_dir, exist_ok=True)
+    # os.makedirs(model_dir, exist_ok=True)
+    Path(model_dir).mkdir(parents=True, exist_ok=True)
     model_check_point = ModelCheckpoint(os.path.join(model_dir, '{epoch:03d}.h5'),
                                         period=args.save_period)
     csv_logger        = CSVLogger(os.path.join(model_dir, 'log.csv'))