Update imagenet prediction decoding utilities

docs/templates/applications.md

@@ -26,6 +26,7 @@ All of these architectures are compatible with both TensorFlow and Theano, and u
 from keras.applications.resnet50 import ResNet50
 from keras.preprocessing import image
 from keras.applications.resnet50 import preprocess_input, decode_predictions
+import numpy as np
 
 model = ResNet50(weights='imagenet')
 
@@ -36,8 +37,10 @@ x = np.expand_dims(x, axis=0)
 x = preprocess_input(x)
 
 preds = model.predict(x)
-print('Predicted:', decode_predictions(preds))
-# print: [[u'n02504458', u'African_elephant']]
+# decode the results into a list of tuples (class, description, probability)
+# (one such list for each sample in the batch)
+print('Predicted:', decode_predictions(preds, top=3)[0])
+# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]
 ```
 
 ### Extract features with VGG16

keras/applications/imagenet_utils.py

@@ -28,16 +28,21 @@ def preprocess_input(x, dim_ordering='default'):
     return x
 
 
-def decode_predictions(preds):
+def decode_predictions(preds, top=5):
     global CLASS_INDEX
-    assert len(preds.shape) == 2 and preds.shape[1] == 1000
+    if len(preds.shape) != 2 or preds.shape[1] != 1000:
+        raise ValueError('`decode_predictions` expects '
+                         'a batch of predictions '
+                         '(i.e. a 2D array of shape (samples, 1000)). '
+                         'Found array with shape: ' + str(preds.shape))
     if CLASS_INDEX is None:
         fpath = get_file('imagenet_class_index.json',
                          CLASS_INDEX_PATH,
                          cache_subdir='models')
         CLASS_INDEX = json.load(open(fpath))
-    indices = np.argmax(preds, axis=-1)
     results = []
-    for i in indices:
-        results.append(CLASS_INDEX[str(i)])
+    for pred in preds:
+        top_indices = np.argpartition(pred, -top)[-top:][::-1]
+        result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices]
+        results.append(result)
     return results