docs: update according to new jina api

README.md

@@ -148,10 +148,11 @@ tuned_model, _ = finetuner.fit(
 
     def data_gen():
         for d in from_files('./img_align_celeba/*.jpg', size=100, to_dataturi=True):
-            d.convert_image_uri_to_blob()
-            d.normalize_image_blob()
-            d.set_image_blob_channel_axis(-1, 0)  #: no need of this line if you are using tf/keras
-            yield d
+            yield (d.convert_uri_to_image_blob()
+               .set_image_blob_normalization()
+               .set_image_blob_shape(shape=(224, 224))
+               .set_image_blob_channel_axis(-1, new_channel_axis=0)  #: no need of this line if you are using tf/keras
+               )
     ```
 3. Load pretrained ResNet50 using PyTorch/Keras/Paddle:
     - PyTorch

docs/get-started/celeba.md

@@ -26,15 +26,14 @@ Note that Finetuner accepts Jina `DocumentArray`/`DocumentArrayMemmap`, so we fi
 ```python
 from jina.types.document.generators import from_files
 
-
 def data_gen():
     # please change the file path to your data path
     for d in from_files('/Users/jina/Downloads/img_align_celeba/*.jpg', size=100, to_dataturi=True):
-        d.convert_image_uri_to_blob()
-        d.normalize_image_blob()
-        d.resize_image_blob(width=224, height=224) #: no need of this line if you are using pytorch/paddle
-        d.set_image_blob_channel_axis(-1, 0)  #: no need of this line if you are using tf/keras
-        yield d
+        yield (d.convert_uri_to_image_blob()
+               .set_image_blob_normalization()
+               .set_image_blob_shape(shape=(224, 224))
+               .set_image_blob_channel_axis(-1, new_channel_axis=0)
+               )
 ```
 
 ## Load the pretrained model