Refined Tensorflow train methods

platypush/plugins/tensorflow/__init__.py

@@ -634,9 +634,28 @@ class TensorflowPlugin(Plugin):
 
         return ret
 
+    @classmethod
+    def _get_outputs(cls, data: Union[str, np.ndarray, Iterable], model: Model) -> np.ndarray:
+        if isinstance(data, str):
+            if model.output_labels:
+                label_index = model.output_labels.index(data)
+                if label_index >= 0:
+                    return np.array([1 if i == label_index else 0 for i in range(len(model.output_labels))])
+
+            return np.array([data])
+
+        if len(data) > 0 and isinstance(data[0], str):
+            return np.array([cls._get_outputs(item, model) for item in data])
+
+        return data
+
     @classmethod
     def _get_data(cls, data: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]], model: Model) \
             -> Union[np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]]:
+        if isinstance(data, List) or isinstance(data, Tuple):
+            if len(data) and isinstance(data[0], str):
+                return np.array([cls._get_data(item, model) for item in data])
+
         if not isinstance(data, str):
             return data
 
@@ -668,6 +687,8 @@ class TensorflowPlugin(Plugin):
         elif os.path.isdir(data_file):
             return cls._get_dir(data_file, model)
 
+        return data
+
     @classmethod
     def _get_dataset(cls,
                      inputs: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]],
@@ -677,12 +698,13 @@ class TensorflowPlugin(Plugin):
                      Optional[Union[np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]]]]:
         inputs = cls._get_data(inputs, model)
         if outputs:
-            outputs = cls._get_data(inputs, model)
+            outputs = cls._get_outputs(outputs, model)
         elif isinstance(inputs, dict) and model.output_labels:
             pairs = []
             for i, label in enumerate(model.output_labels):
                 data = inputs.get(label, [])
-                pairs.extend([(d, i) for d in data])
+                pairs.extend([(d, tuple(1 if i == j else 0 for j, _ in enumerate(model.output_labels)))
+                              for d in data])
 
             random.shuffle(pairs)
             inputs = np.asarray([p[0] for p in pairs])
@@ -1091,19 +1113,20 @@ class TensorflowPlugin(Plugin):
         model_name = model
         model_dir = None
 
-        if os.path.isdir(os.path.join(self._work_dir, model_name)):
+        if os.path.isdir(os.path.join(self._models_dir, model_name)) or model_name in self.models:
-            model_dir = os.path.join(self._work_dir, model_name)
+            model_dir = os.path.join(self._models_dir, model_name)
         else:
-            model_name = os.path.abspath(os.path.expanduser(model_name))
+            model_file = os.path.abspath(os.path.expanduser(model_name))
-            if os.path.isfile(model_name):
+            if os.path.isfile(model_file):
-                model_dir = str(pathlib.Path(model_name).parent)
+                model_dir = str(pathlib.Path(model_file).parent)
-            elif os.path.isdir(model_name):
+            elif os.path.isdir(model_file):
-                model_dir = model_name
+                model_dir = model_file
 
-        assert model_dir and model_name in self.models, 'No such model loaded: {}'.format(model)
+        model = self.models.get(model_name, self.models.get(model_dir))
+        assert model, 'No such model loaded: {}'.format(model_name)
+        pathlib.Path(model_dir).mkdir(parents=True, exist_ok=True)
 
         with self._lock_model(model_name):
-            model = self.models[model_name]
             labels = {}
             labels_file = os.path.join(model_dir, 'labels.json')