Fixed the broken link in handwriting_recognition.py (#1757)

* Fixed the broken link in handwriting_recognition.py * Fixed the broken link in handwriting_recognition.md and handwriting_recognition.ipynb * Update handwriting_recognition.ipynb
keras-team · Feb 13, 2024 · ff4276a · ff4276a
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diff --git a/examples/vision/handwriting_recognition.py b/examples/vision/handwriting_recognition.py
@@ -173,7 +173,7 @@ def clean_labels(labels):
 ### Building the character vocabulary
 
 Keras provides different preprocessing layers to deal with different modalities of data.
-[This guide](https://keras.io/guides/preprocessing_layers/) provides a comprehensive introduction.
+[This guide](https://keras.io/api/layers/preprocessing_layers/) provides a comprehensive introduction.
 Our example involves preprocessing labels at the character
 level. This means that if there are two labels, e.g. "cat" and "dog", then our character
 vocabulary should be {a, c, d, g, o, t} (without any special tokens). We use the

diff --git a/examples/vision/ipynb/handwriting_recognition.ipynb b/examples/vision/ipynb/handwriting_recognition.ipynb
@@ -299,7 +299,7 @@
     "### Building the character vocabulary\n",
     "\n",
     "Keras provides different preprocessing layers to deal with different modalities of data.\n",
-    "[This guide](https://keras.io/guides/preprocessing_layers/) provides a comprehensive introduction.\n",
+    "[This guide](https://keras.io/api/layers/preprocessing_layers/) provides a comprehensive introduction.\n",
     "Our example involves preprocessing labels at the character\n",
     "level. This means that if there are two labels, e.g. \"cat\" and \"dog\", then our character\n",
     "vocabulary should be {a, c, d, g, o, t} (without any special tokens). We use the\n",

diff --git a/examples/vision/md/handwriting_recognition.md b/examples/vision/md/handwriting_recognition.md
@@ -234,7 +234,7 @@ test_labels_cleaned = clean_labels(test_labels)
 ### Building the character vocabulary
 
 Keras provides different preprocessing layers to deal with different modalities of data.
-[This guide](https://keras.io/guides/preprocessing_layers/) provides a comprehensive introduction.
+[This guide](https://keras.io/api/layers/preprocessing_layers/) provides a comprehensive introduction.
 Our example involves preprocessing labels at the character
 level. This means that if there are two labels, e.g. "cat" and "dog", then our character
 vocabulary should be {a, c, d, g, o, t} (without any special tokens). We use the