diff --git a/platypush/message/response/tensorflow.py b/platypush/message/response/tensorflow.py
index 31bf318a1..75656e5e1 100644
--- a/platypush/message/response/tensorflow.py
+++ b/platypush/message/response/tensorflow.py
@@ -10,12 +10,12 @@ class TensorflowResponse(Response):
     """
     Generic Tensorflow response.
     """
-    def __init__(self, *args, model: Model, **kwargs):
+    def __init__(self, *args, model: Model, model_name: Optional[str] = None, **kwargs):
         """
         :param model: Name of the model.
         """
         super().__init__(*args, output={
-            'model': model.name,
+            'model': model_name or model.name,
         }, **kwargs)
 
         self.model = model
diff --git a/platypush/plugins/tensorflow/__init__.py b/platypush/plugins/tensorflow/__init__.py
index 8e6fbdbf4..c903fa4d4 100644
--- a/platypush/plugins/tensorflow/__init__.py
+++ b/platypush/plugins/tensorflow/__init__.py
@@ -610,7 +610,11 @@ class TensorflowPlugin(Plugin):
                                  'Found: {}'.format(colors[0]))
 
         img = image.load_img(image_file, target_size=size, color_mode=color_mode)
-        return image.img_to_array(img)
+        data = image.img_to_array(img)
+        if data.shape[-1] == 1:
+            # Squeeze extra color channels
+            data = np.squeeze(data)
+        return data
 
     @classmethod
     def _get_dir(cls, directory: str, model: Model) -> Dict[str, Iterable]:
@@ -847,7 +851,7 @@ class TensorflowPlugin(Plugin):
             use_multiprocessing=use_multiprocessing,
         )
 
-        return TensorflowTrainResponse(model=model, epochs=ret.epoch, history=ret.history)
+        return TensorflowTrainResponse(model=model, model_name=name, epochs=ret.epoch, history=ret.history)
 
     @action
     def evaluate(self,
@@ -1071,7 +1075,8 @@ class TensorflowPlugin(Plugin):
             use_multiprocessing=use_multiprocessing
         )
 
-        return TensorflowPredictResponse(model=model, prediction=ret, output_labels=model.output_labels)
+        return TensorflowPredictResponse(model=model, model_name=name, prediction=ret,
+                                         output_labels=model.output_labels)
 
     @action
     def save(self, model: str, overwrite: bool = True, **opts) -> None: