Merge branch 'master' into rename_QTZ_linear_mid_tread_half

blue-oil · Mar 11, 2020 · 7867a33 · 7867a33
2 parents c051937 + a0078e1
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diff --git a/blueoil/configs/core/classification/darknet_cifar10.py b/blueoil/configs/core/classification/darknet_cifar10.py
@@ -69,9 +69,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // 200
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.001, 0.0001, 0.00001],

diff --git a/blueoil/configs/core/classification/darknet_quantize_cifar10.py b/blueoil/configs/core/classification/darknet_quantize_cifar10.py
@@ -72,9 +72,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // 200
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.001, 0.0001, 0.00001],

diff --git a/blueoil/configs/core/classification/darknet_quantize_ilsvrc_2012.py b/blueoil/configs/core/classification/darknet_quantize_ilsvrc_2012.py
@@ -76,9 +76,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.polynomial_decay
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.polynomial_decay
 # TODO(wakiska): It is same as original yolov2 paper (batch size = 128).
 NETWORK.LEARNING_RATE_KWARGS = {"learning_rate": 1e-1, "decay_steps": 1600000, "power": 4.0, "end_learning_rate": 0.0}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE

diff --git a/blueoil/configs/core/classification/lm_resnet_quantize_cifar10.py b/blueoil/configs/core/classification/lm_resnet_quantize_cifar10.py
@@ -76,9 +76,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.1, 0.01, 0.001, 0.0001],
     "boundaries": [40000, 60000, 80000],

diff --git a/blueoil/configs/core/classification/lmnet_cifar10.py b/blueoil/configs/core/classification/lmnet_cifar10.py
@@ -69,9 +69,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // BATCH_SIZE
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.1, 0.01, 0.001, 0.0001],

diff --git a/blueoil/configs/core/classification/lmnet_cifar100.py b/blueoil/configs/core/classification/lmnet_cifar100.py
@@ -69,9 +69,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // 200
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.001, 0.0001, 0.00001],

diff --git a/blueoil/configs/core/classification/lmnet_openimagesv4.py b/blueoil/configs/core/classification/lmnet_openimagesv4.py
@@ -68,9 +68,9 @@
 # IS_DEBUG = True
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // 200
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.001, 0.0001, 0.00001],

diff --git a/blueoil/configs/core/classification/lmnet_quantize_cifar10.py b/blueoil/configs/core/classification/lmnet_quantize_cifar10.py
@@ -73,9 +73,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // BATCH_SIZE
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.1, 0.01, 0.001, 0.0001],

diff --git a/blueoil/configs/core/classification/lmnet_quantize_cifar100.py b/blueoil/configs/core/classification/lmnet_quantize_cifar100.py
@@ -72,9 +72,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // 200
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.001, 0.0001, 0.00001],

diff --git a/blueoil/configs/core/classification/lmnet_v1_cifar10.py b/blueoil/configs/core/classification/lmnet_v1_cifar10.py
@@ -68,9 +68,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // BATCH_SIZE
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.001, 0.0001, 0.00001],

diff --git a/blueoil/configs/core/classification/lmnet_v1_quantize_cifar10.py b/blueoil/configs/core/classification/lmnet_v1_quantize_cifar10.py
@@ -72,9 +72,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // BATCH_SIZE
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.001, 0.0001, 0.00001],

diff --git a/blueoil/configs/core/classification/lmnet_v1_quantize_cifar10_tune.py b/blueoil/configs/core/classification/lmnet_v1_quantize_cifar10_tune.py
@@ -86,7 +86,7 @@
     'optimizer_class': hp.choice(
         'optimizer_class', [
             {
-                'optimizer': tf.train.MomentumOptimizer,
+                'optimizer': tf.compat.v1.train.MomentumOptimizer,
                 'momentum': 0.9,
             },
         ]
@@ -95,7 +95,7 @@
     'learning_rate_func': hp.choice(
         'learning_rate_func', [
             {
-                'scheduler': tf.train.piecewise_constant,
+                'scheduler': tf.compat.v1.train.piecewise_constant,
                 'scheduler_factor': hp.uniform('scheduler_factor', 0.05, 0.5),
                 'scheduler_steps': [25000, 50000, 75000],
             },

diff --git a/blueoil/configs/core/classification/mobilenet_v2_cifar10.py b/blueoil/configs/core/classification/mobilenet_v2_cifar10.py
@@ -69,9 +69,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 step_per_epoch = 50000 // BATCH_SIZE
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.01, 0.1, 0.01, 0.001, 0.0001],

diff --git a/blueoil/configs/core/classification/resnet_cifar10.py b/blueoil/configs/core/classification/resnet_cifar10.py
@@ -70,9 +70,9 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [0.1, 0.01, 0.001, 0.0001],
     "boundaries": [40000, 60000, 80000],

diff --git a/blueoil/configs/core/keypoint_detection/lm_single_pose_v1_quantize_mscoco.py b/blueoil/configs/core/keypoint_detection/lm_single_pose_v1_quantize_mscoco.py
@@ -85,9 +85,9 @@
 step_per_epoch = 149813 // BATCH_SIZE
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 NETWORK.LEARNING_RATE_KWARGS = {
         "values": [1e-4, 1e-3, 1e-4, 1e-5],
         "boundaries": [5000, step_per_epoch * 5, step_per_epoch * 10],

diff --git a/blueoil/configs/core/object_detection/lm_fyolo_bdd100k.py b/blueoil/configs/core/object_detection/lm_fyolo_bdd100k.py
@@ -80,9 +80,9 @@
 ])
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 # In the origianl yolov2 Paper, with a starting learning rate of 10−3, dividing it by 10 at 60 and 90 epochs.
 # Train data num per epoch is 16551
 step_per_epoch = 16551 // BATCH_SIZE

diff --git a/blueoil/configs/core/object_detection/lm_fyolo_quantize_pascalvoc_2007_2012.py b/blueoil/configs/core/object_detection/lm_fyolo_quantize_pascalvoc_2007_2012.py
@@ -99,9 +99,9 @@
 ])
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 # In the origianl yolov2 Paper, with a starting learning rate of 10−3, dividing it by 10 at 60 and 90 epochs.
 # Train data num per epoch is 16551
 step_per_epoch = 16551 // BATCH_SIZE

diff --git a/blueoil/configs/core/object_detection/yolo_v2_quantize_pascalvoc_2007_2012.py b/blueoil/configs/core/object_detection/yolo_v2_quantize_pascalvoc_2007_2012.py
@@ -187,9 +187,9 @@
 ])
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 # In the yolov2 paper, with a starting learning rate of 10−3, dividing it by 10 at 60 and 90 epochs.
 # Train data num per epoch is 16551
 # In first 5000 steps, use small learning rate for warmup.

diff --git a/blueoil/configs/core/segmentation/lm_bisenet_quantize_camvid.py b/blueoil/configs/core/segmentation/lm_bisenet_quantize_camvid.py
@@ -80,7 +80,7 @@
 
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/core/segmentation/lm_segnet_v0_quantize_camvid.py b/blueoil/configs/core/segmentation/lm_segnet_v0_quantize_camvid.py
@@ -68,7 +68,7 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/core/segmentation/lm_segnet_v1_quantize_camvid.py b/blueoil/configs/core/segmentation/lm_segnet_v1_quantize_camvid.py
@@ -72,7 +72,7 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/core/segmentation/segnet_camvid.py b/blueoil/configs/core/segmentation/segnet_camvid.py
@@ -66,7 +66,7 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/core/segmentation/segnet_quantize_bdd100k.py b/blueoil/configs/core/segmentation/segnet_quantize_bdd100k.py
@@ -66,7 +66,7 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/core/segmentation/segnet_quantize_camvid.py b/blueoil/configs/core/segmentation/segnet_quantize_camvid.py
@@ -70,7 +70,7 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/core/segmentation/segnet_quantize_camvid_tune.py b/blueoil/configs/core/segmentation/segnet_quantize_camvid_tune.py
@@ -85,15 +85,15 @@
     'optimizer_class': hp.choice(
         'optimizer_class', [
             {
-                'optimizer': tf.train.AdamOptimizer,
+                'optimizer': tf.compat.v1.train.AdamOptimizer,
             },
         ]
     ),
     'learning_rate': hp.uniform('learning_rate', 0, 0.01),
     'learning_rate_func': hp.choice(
         'learning_rate_func', [
             {
-                'scheduler': tf.train.piecewise_constant,
+                'scheduler': tf.compat.v1.train.piecewise_constant,
                 'scheduler_factor': 1.0,
                 'scheduler_steps': [25000, 50000, 75000],
             },

diff --git a/blueoil/configs/core/segmentation/segnet_quantize_cityscapes.py b/blueoil/configs/core/segmentation/segnet_quantize_cityscapes.py
@@ -72,7 +72,7 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/example/classification.py b/blueoil/configs/example/classification.py
@@ -80,7 +80,7 @@
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE

diff --git a/blueoil/configs/example/object_detection.py b/blueoil/configs/example/object_detection.py
@@ -88,9 +88,9 @@
 ])
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
 NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
-NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
 _epoch_steps = 16551 // BATCH_SIZE
 NETWORK.LEARNING_RATE_KWARGS = {
     "values": [1e-6, 1e-4, 1e-5, 1e-6, 1e-7],

diff --git a/docker-compose.yml b/docker-compose.yml
@@ -23,4 +23,3 @@ services:
       - CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-0}
       - DATA_DIR=/home/blueoil/dataset
       - OUTPUT_DIR=/home/blueoil/saved
-      - PYTHONPATH=/home/blueoil:/home/blueoil/lmnet:/home/blueoil/dlk/python/dlk
diff --git a/docs/tutorial/image_cls.md b/docs/tutorial/image_cls.md
@@ -51,11 +51,12 @@ The CIFAR-10 dataset consists of 60,000 32x32 color images split into 10 classe
 Generate your model configuration file interactively by running the `blueoil init` command.
 
     $ docker run --rm -it \
+        -v $(pwd)/cifar:/home/blueoil/cifar \
         -v $(pwd)/config:/home/blueoil/config \
         blueoil_$(id -un):{TAG} \
-        blueoil init -o config/my_config.yml
+        blueoil init -o config/cifar10_test.py
 
-The `{TAG}` value must be set to a value like `v0.15.0-15-gf493ec9` that can be obtained with the `docker images` command.
+The `{TAG}` value must be set to a value like `v0.20.0-11-gf1e07c8` that can be obtained with the `docker images` command.
 This value depends on your environment.
 
 Below is an example configuration.
@@ -67,18 +68,18 @@ Below is an example configuration.
   choose network:  LmnetV1Quantize
   choose dataset format:  Caltech101
   training dataset path:  /home/blueoil/cifar/train/
-  set validataion dataset? (if answer no, the dataset will be separated for training and validation by 9:1 ratio.)  yes
+  set validation dataset? (if answer no, the dataset will be separated for training and validation by 9:1 ratio.):  yes
   validataion dataset path:  /home/blueoil/cifar/test/
   batch size (integer):  64
   image size (integer x integer):  32x32
   how many epochs do you run training (integer):  100
   select optimizer:  Momentum
   initial learning rate:  0.001
   choose learning rate schedule ({epochs} is the number of training epochs you entered before):  '3-step-decay' -> learning rate decrease by 1/10 on {epochs}/3 and {epochs}*2/3 and {epochs}-1
-  enable data augmentation: Yes
+  enable data augmentation?  (Y/n):  Yes
   Please choose augmentors:  done (5 selections)
 -> select Brightness, Color, FlipLeftRight, Hue, SSDRandomCrop
-  apply quantization at the first layer?  no 
+  apply quantization at the first layer? (Y/n):  no 
 ```
 
 - Model name: (Any)
@@ -98,7 +99,7 @@ Below is an example configuration.
 - Augmentors: (Random)
 - Quantization on the first layer: No
 
-If configuration finishes, the configuration file is generated in the `my_config.yml` under config directory.
+If configuration finishes, the configuration file is generated in the `cifar10_test.py` under config directory.
 
 ## Train a neural network
 
@@ -110,7 +111,7 @@ Train your model by running `blueoil train` with model configuration.
         -v $(pwd)/config:/home/blueoil/config \
         -v $(pwd)/saved:/home/blueoil/saved \
         blueoil_$(id -un):{TAG} \
-        blueoil train -c config/my_config.yml
+        blueoil train -c config/cifar10_test.py
 
 Just like init, set the value of `{TAG}` to the value obtained by `docker images`.
 Change the value of `CUDA_VISIBLE_DEVICES` according to your environment.