(keras) Make image argument required

docs/source/_notebooks/keras-image-classifiers.rst

@@ -70,11 +70,11 @@ Loading our sample image:
 
     # we start from a path / URI. 
     # If you already have an image loaded, follow the subsequent steps
-    image = 'imagenet-samples/cat_dog.jpg'
+    image_uri = 'imagenet-samples/cat_dog.jpg'
     
     # this is the original "cat dog" image used in the Grad-CAM paper
     # check the image with Pillow
-    im = Image.open(image)
+    im = Image.open(image_uri)
     print(type(im))
     display(im)
 
@@ -96,14 +96,14 @@ dimensions! Let's resize it:
     # we could resize the image manually
     # but instead let's use a utility function from `keras.preprocessing`
     # we pass the required dimensions as a (height, width) tuple
-    im = keras.preprocessing.image.load_img(image, target_size=dims) # -> PIL image
-    print(type(im))
+    im = keras.preprocessing.image.load_img(image_uri, target_size=dims) # -> PIL image
+    print(im)
     display(im)
 
 
 .. parsed-literal::
 
-    <class 'PIL.Image.Image'>
+    <PIL.Image.Image image mode=RGB size=224x224 at 0x7FD4FC485DD8>
 
 
 
@@ -143,7 +143,6 @@ Looking good. Now we need to convert the image to a numpy array.
 
 .. code:: ipython3
 
-    # one last thing
     # `keras.applications` models come with their own input preprocessing function
     # for best results, apply that as well
     
@@ -171,6 +170,28 @@ inputting
 .. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_13_0.png
 
 
+One last thing, to explain image based models, we need to pass the image
+as a PIL object explicitly. However, it must have mode 'RGBA'
+
+.. code:: ipython3
+
+    print(im) # current mode
+    
+    image = im.convert(mode='RGBA') # add alpha channel
+    print(image)
+    display(image)
+
+
+.. parsed-literal::
+
+    <PIL.Image.Image image mode=RGB size=224x224 at 0x7FD4FC485DD8>
+    <PIL.Image.Image image mode=RGBA size=224x224 at 0x7FD4DB62EF28>
+
+
+
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_15_1.png
+
+
 Ready to go!
 
 2. Explaining our model's prediction
@@ -218,12 +239,15 @@ for a dog with ELI5:
 
 .. code:: ipython3
 
-    eli5.show_prediction(model, doc)
+    # we need to pass the network
+    # the input as a numpy array
+    # and the corresponding input image (RGBA mode)
+    eli5.show_prediction(model, doc, image=image)
 
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_19_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_21_0.png
 
 
 
@@ -241,12 +265,12 @@ classifier looks to find those objects.
 .. code:: ipython3
 
     cat_idx = 282 # ImageNet ID for "tiger_cat" class, because we have a cat in the picture
-    eli5.show_prediction(model, doc, targets=[cat_idx]) # pass the class id
+    eli5.show_prediction(model, doc, image=image, targets=[cat_idx]) # pass the class id
 
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_22_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_24_0.png
 
 
 
@@ -259,16 +283,16 @@ Currently only one class can be explained at a time.
 
     window_idx = 904 # 'window screen'
     turtle_idx = 35 # 'mud turtle', some nonsense
-    display(eli5.show_prediction(model, doc, targets=[window_idx]))
-    display(eli5.show_prediction(model, doc, targets=[turtle_idx]))
+    display(eli5.show_prediction(model, doc, image=image, targets=[window_idx]))
+    display(eli5.show_prediction(model, doc, image=image, targets=[turtle_idx]))
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_24_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_26_0.png
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_24_1.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_26_1.png
 
 
 That's quite noisy! Perhaps the model is weak at classifying 'window
@@ -345,7 +369,7 @@ Rough print but okay. Let's pick a few convolutional layers that are
 
     for l in ['block_2_expand', 'block_9_expand', 'Conv_1']:
         print(l)
-        display(eli5.show_prediction(model, doc, layer=l)) # we pass the layer as an argument
+        display(eli5.show_prediction(model, doc, image=image, layer=l)) # we pass the layer as an argument
 
 
 .. parsed-literal::
@@ -354,7 +378,7 @@ Rough print but okay. Let's pick a few convolutional layers that are
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_29_1.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_31_1.png
 
 
 .. parsed-literal::
@@ -363,7 +387,7 @@ Rough print but okay. Let's pick a few convolutional layers that are
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_29_3.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_31_3.png
 
 
 .. parsed-literal::
@@ -372,7 +396,7 @@ Rough print but okay. Let's pick a few convolutional layers that are
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_29_5.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_31_5.png
 
 
 These results should make intuitive sense for Convolutional Neural
@@ -393,7 +417,7 @@ better understand what is going on.
 
 .. code:: ipython3
 
-    expl = eli5.explain_prediction(model, doc)
+    expl = eli5.explain_prediction(model, doc, image=image)
 
 Examining the structure of the ``Explanation`` object:
 
@@ -417,7 +441,7 @@ Examining the structure of the ``Explanation`` object:
            [0.        , 0.        , 0.        , 0.        , 0.        ,
             0.        , 0.05308531],
            [0.        , 0.        , 0.        , 0.        , 0.        ,
-            0.01124764, 0.06864655]]))], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=<PIL.Image.Image image mode=RGBA size=224x224 at 0x7FCA6FD17CC0>)
+            0.01124764, 0.06864655]]))], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=<PIL.Image.Image image mode=RGBA size=224x224 at 0x7FD4DB62EF28>)
 
 
 We can check the score (raw value) or probability (normalized score) of
@@ -446,7 +470,7 @@ We can also access the original image and the Grad-CAM heatmap:
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_39_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_41_0.png
 
 
 .. parsed-literal::
@@ -476,7 +500,7 @@ Visualizing the heatmap:
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_41_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_43_0.png
 
 
 That's only 7x7! This is the spatial dimensions of the
@@ -494,7 +518,7 @@ resampling):
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_43_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_45_0.png
 
 
 Now it's clear what is being highlighted. We just need to apply some
@@ -508,7 +532,7 @@ colors and overlay the heatmap over the original image, exactly what
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_45_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_47_0.png
 
 
 6. Extra arguments to ``format_as_image()``
@@ -525,7 +549,7 @@ colors and overlay the heatmap over the original image, exactly what
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_48_0.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_50_0.png
 
 
 The ``alpha_limit`` argument controls the maximum opacity that the
@@ -554,7 +578,7 @@ check the explanation:
 
     # first check the explanation *with* softmax
     print('with softmax')
-    display(eli5.show_prediction(model, doc))
+    display(eli5.show_prediction(model, doc, image=image))
     
     
     # remove softmax
@@ -566,7 +590,7 @@ check the explanation:
     model = keras.models.load_model('tmp_model_save_rmsoftmax')
     
     print('without softmax')
-    display(eli5.show_prediction(model, doc))
+    display(eli5.show_prediction(model, doc, image=image))
 
 
 .. parsed-literal::
@@ -575,7 +599,7 @@ check the explanation:
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_51_1.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_53_1.png
 
 
 .. parsed-literal::
@@ -584,7 +608,7 @@ check the explanation:
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_51_3.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_53_3.png
 
 
 We see some slight differences. The activations are brighter. Do
@@ -610,9 +634,9 @@ loading another model and explaining a classification of the same image:
     nasnet.preprocess_input(doc2)
     
     print(model.name)
-    display(eli5.show_prediction(model, doc))
+    display(eli5.show_prediction(model, doc, image=image))
     print(model2.name)
-    display(eli5.show_prediction(model2, doc2))
+    display(eli5.show_prediction(model2, doc2, image=image))
 
 
 .. parsed-literal::
@@ -621,7 +645,7 @@ loading another model and explaining a classification of the same image:
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_54_1.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_56_1.png
 
 
 .. parsed-literal::
@@ -630,7 +654,7 @@ loading another model and explaining a classification of the same image:
 
 
 
-.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_54_3.png
+.. image:: ../_notebooks/keras-image-classifiers_files/keras-image-classifiers_56_3.png
 
 
 Wow ``show_prediction()`` is so robust!

docs/source/libraries/keras.rst

@@ -40,6 +40,12 @@ Important arguments to :func:`eli5.explain_prediction` for ``Model`` and ``Seque
 
     - Check ``model.input_shape`` to confirm the required dimensions of the input tensor.
 
+* ``image`` Pillow image, corresponds to doc input.
+
+    - **Must be passed for image explanations.**
+
+    - **Must have mode "RGBA".**
+
 * ``target_names`` are the names of the output classes. 
 
     - *Currently not implemented*.

eli5/keras/explain_prediction.py

@@ -1,6 +1,8 @@
 # -*- coding: utf-8 -*-
 from __future__ import absolute_import
-from typing import Union, Optional, Callable, Tuple, List
+from typing import Union, Optional, Callable, Tuple, List, TYPE_CHECKING
+if TYPE_CHECKING:
+    import PIL # type: ignore
 
 import numpy as np # type: ignore
 import keras # type: ignore
@@ -22,10 +24,11 @@
 @explain_prediction.register(Model)
 def explain_prediction_keras(estimator, # type: Model
                              doc, # type: np.ndarray
+                             image=None, # type: Optional['PIL.Image.Image']
                              target_names=None,
                              targets=None, # type: Optional[list]
                              layer=None, # type: Optional[Union[int, str, Layer]]
-                            ):
+                             ):
     # type: (...) -> Explanation
     """
     Explain the prediction of a Keras image classifier.
@@ -63,6 +66,16 @@ def explain_prediction_keras(estimator, # type: Model
         :raises TypeError: if ``doc`` is not a numpy array.
         :raises ValueError: if ``doc`` shape does not match.
 
+
+    :param image:
+        Pillow image over which to overlay the heatmap.
+
+        Corresponds to the input ``doc``.
+        
+        Must have mode 'RGBA'.
+    :type image: PIL.Image.Image, optional
+
+
     :param target_names:         
         *Not Implemented*.
         Names for classes in the final output layer.
@@ -108,6 +121,7 @@ def explain_prediction_keras(estimator, # type: Model
             * ``target`` ID of target class.
             * ``score`` value for predicted class.
     """
+    assert image is not None
     _validate_doc(estimator, doc)
     activation_layer = _get_activation_layer(estimator, layer)
 
@@ -119,10 +133,6 @@ def explain_prediction_keras(estimator, # type: Model
     weights, activations, grads, predicted_idx, predicted_val = values
     heatmap = gradcam(weights, activations)
 
-    doc, = doc # rank 4 batch -> rank 3 single image
-    image = keras.preprocessing.image.array_to_img(doc) # -> RGB Pillow image
-    image = image.convert(mode='RGBA')
-
     return Explanation(
         estimator.name,
         description=DESCRIPTION_KERAS,

tests/test_keras.py

@@ -125,7 +125,9 @@ def test_get_target_prediction_invalid(simple_seq):
 
 
 def test_explain_prediction_score(simple_seq):
-    expl = explain_prediction(simple_seq, np.zeros((1, 32, 32, 1)))
+    expl = explain_prediction(simple_seq,
+                              np.zeros((1, 32, 32, 1)),
+                              image=True) # TODO: dummy image
     assert expl.targets[0].score is not None
     assert expl.targets[0].proba is None