Fixing the typo in attention_mil_classification.ipynb (#2021)

* Update attention_mil_classification.ipynb * Update
keras-team · Jan 13, 2025 · 04d7cb3 · 04d7cb3
1 parent d780ebb
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Showing 3 changed files with 4 additions and 4 deletions.
diff --git a/examples/vision/attention_mil_classification.py b/examples/vision/attention_mil_classification.py
@@ -393,7 +393,7 @@ class weighting should be considered.
 
 
 def compute_class_weights(labels):
-    # Count number of postive and negative bags.
+    # Count number of positive and negative bags.
     negative_count = len(np.where(labels == 0)[0])
     positive_count = len(np.where(labels == 1)[0])
     total_count = negative_count + positive_count

diff --git a/examples/vision/ipynb/attention_mil_classification.ipynb b/examples/vision/ipynb/attention_mil_classification.ipynb
@@ -502,7 +502,7 @@
    "source": [
     "\n",
     "def compute_class_weights(labels):\n",
-    "    # Count number of postive and negative bags.\n",
+    "    # Count number of positive and negative bags.\n",
     "    negative_count = len(np.where(labels == 0)[0])\n",
     "    positive_count = len(np.where(labels == 1)[0])\n",
     "    total_count = negative_count + positive_count\n",
@@ -737,4 +737,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
diff --git a/examples/vision/md/attention_mil_classification.md b/examples/vision/md/attention_mil_classification.md
@@ -416,7 +416,7 @@ Using class weights, the model will tend to give a higher weight to the rare cla
 ```python
 
 def compute_class_weights(labels):
-    # Count number of postive and negative bags.
+    # Count number of positive and negative bags.
     negative_count = len(np.where(labels == 0)[0])
     positive_count = len(np.where(labels == 1)[0])
     total_count = negative_count + positive_count