change examples

examples/evaluate.py

@@ -1,14 +1,10 @@
-from model import BostonHousingModel
 from model_zoo.evaluater import BaseEvaluater
 import tensorflow as tf
 
 tf.flags.DEFINE_string('checkpoint_name', 'model.ckpt', help='Model name')
 
 
 class Evaluater(BaseEvaluater):
-    def __init__(self):
-        BaseEvaluater.__init__(self)
-        self.model_class = BostonHousingModel
 
     def prepare_data(self):
         from tensorflow.python.keras.datasets import boston_housing

examples/infer.py

@@ -1,21 +1,16 @@
-from model import BostonHousingModel
 from model_zoo.inferer import BaseInferer
+from model_zoo.preprocess import standardize
 import tensorflow as tf
-from tensorflow.python.keras.datasets import boston_housing
-from sklearn.preprocessing import StandardScaler
 
-tf.flags.DEFINE_string('checkpoint_name', 'model.ckpt-38', help='Model name')
+tf.flags.DEFINE_string('checkpoint_name', 'model.ckpt-20', help='Model name')
+
 
 class Inferer(BaseInferer):
-    def __init__(self):
-        BaseInferer.__init__(self)
-        self.model_class = BostonHousingModel
 
     def prepare_data(self):
+        from tensorflow.python.keras.datasets import boston_housing
         (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
-        ss = StandardScaler()
-        ss.fit(x_train)
-        x_test = ss.transform(x_test)
+        _, x_test = standardize(x_train, x_test)
         return x_test
 
 

examples/model.py

@@ -1,6 +1,7 @@
 from model_zoo.model import BaseModel
 import tensorflow as tf
 
+
 class BostonHousingModel(BaseModel):
     def __init__(self, config):
         super(BostonHousingModel, self).__init__(config)

examples/train.py

@@ -1,23 +1,19 @@
-from model import BostonHousingModel
+import tensorflow as tf
 from model_zoo.trainer import BaseTrainer
-from tensorflow.python.keras.datasets import boston_housing
-from sklearn.preprocessing import StandardScaler
+from model_zoo.preprocess import standardize
+
+tf.flags.DEFINE_integer('epochs', 20, 'Max epochs')
+tf.flags.DEFINE_string('model_class', 'BostonHousingModel', 'Model class name')
 
 
 class Trainer(BaseTrainer):
 
-    def __init__(self):
-        BaseTrainer.__init__(self)
-        self.model_class = BostonHousingModel
-
     def prepare_data(self):
+        from tensorflow.python.keras.datasets import boston_housing
         (x_train, y_train), (x_eval, y_eval) = boston_housing.load_data()
-        ss = StandardScaler()
-        ss.fit(x_train)
-        x_train, x_eval = ss.transform(x_train), ss.transform(x_eval)
+        x_train, x_eval = standardize(x_train, x_eval)
         train_data, eval_data = (x_train, y_train), (x_eval, y_eval)
         return train_data, eval_data
 
-
 if __name__ == '__main__':
     Trainer().run()