Update integrated gradients example for Keras 3. (keras-team#1587)

nkovela1 · Nov 9, 2023 · 26caa15 · 26caa15
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diff --git a/examples/vision/img/integrated_gradients/integrated_gradients_3_1.jpeg b/examples/vision/img/integrated_gradients/integrated_gradients_3_1.jpeg
diff --git a/examples/vision/img/integrated_gradients/integrated_gradients_9_1.png b/examples/vision/img/integrated_gradients/integrated_gradients_9_1.png
diff --git a/examples/vision/img/integrated_gradients/integrated_gradients_9_2.png b/examples/vision/img/integrated_gradients/integrated_gradients_9_2.png
diff --git a/examples/vision/img/integrated_gradients/integrated_gradients_9_3.png b/examples/vision/img/integrated_gradients/integrated_gradients_9_3.png
diff --git a/examples/vision/img/integrated_gradients/integrated_gradients_9_4.png b/examples/vision/img/integrated_gradients/integrated_gradients_9_4.png
diff --git a/examples/vision/integrated_gradients.py b/examples/vision/integrated_gradients.py
@@ -4,7 +4,7 @@
 Date created: 2020/06/02
 Last modified: 2020/06/02
 Description: How to obtain integrated gradients for a classification model.
-Accelerator: NONE
+Accelerator: None
 """
 
 """
@@ -55,12 +55,13 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy import ndimage
-from IPython.display import Image
+from IPython.display import Image, display
 
 import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-from tensorflow.keras.applications import xception
+import keras
+from keras import layers
+from keras.applications import xception
+
 
 # Size of the input image
 img_size = (299, 299, 3)
@@ -284,7 +285,10 @@ def morphological_cleanup_fn(self, attributions, structure=np.ones((4, 4))):
         return opened
 
     def draw_outlines(
-        self, attributions, percentage=90, connected_component_structure=np.ones((3, 3))
+        self,
+        attributions,
+        percentage=90,
+        connected_component_structure=np.ones((3, 3)),
     ):
         # 1. Binarize the attributions.
         attributions = self.binarize(attributions)
@@ -293,7 +297,7 @@ def draw_outlines(
         attributions = ndimage.binary_fill_holes(attributions)
 
         # 3. Compute connected components
-        connected_components, num_comp = ndimage.measurements.label(
+        connected_components, num_comp = ndimage.label(
             attributions, structure=connected_component_structure
         )
 
@@ -449,7 +453,6 @@ def visualize(
 
 """
 ## Let's test-drive it
-You can try the model on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/integrated_gradients).
 """
 
 # 1. Convert the image to numpy array

diff --git a/examples/vision/ipynb/integrated_gradients.ipynb b/examples/vision/ipynb/integrated_gradients.ipynb
@@ -56,7 +56,7 @@
     "**References:**\n",
     "\n",
     "- Integrated Gradients original [paper](https://arxiv.org/abs/1703.01365)\n",
-    "- [Original implementation](https://github.com/ankurtaly/Integrated-Gradients)\n"
+    "- [Original implementation](https://github.com/ankurtaly/Integrated-Gradients)"
    ]
   },
   {
@@ -65,7 +65,7 @@
     "colab_type": "text"
    },
    "source": [
-    "## Setup\n"
+    "## Setup"
    ]
   },
   {
@@ -80,12 +80,13 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import ndimage\n",
-    "from IPython.display import Image\n",
+    "from IPython.display import Image, display\n",
     "\n",
     "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras import layers\n",
-    "from tensorflow.keras.applications import xception\n",
+    "import keras\n",
+    "from keras import layers\n",
+    "from keras.applications import xception\n",
+    "\n",
     "\n",
     "# Size of the input image\n",
     "img_size = (299, 299, 3)\n",
@@ -95,7 +96,7 @@
     "\n",
     "# The local path to our target image\n",
     "img_path = keras.utils.get_file(\"elephant.jpg\", \"https://i.imgur.com/Bvro0YD.png\")\n",
-    "display(Image(img_path))\n"
+    "display(Image(img_path))"
    ]
   },
   {
@@ -104,7 +105,7 @@
     "colab_type": "text"
    },
    "source": [
-    "## Integrated Gradients algorithm\n"
+    "## Integrated Gradients algorithm"
    ]
   },
   {
@@ -232,7 +233,7 @@
     "    # 3. Return the average integrated gradients for the image\n",
     "    integrated_grads = tf.convert_to_tensor(integrated_grads)\n",
     "    return tf.reduce_mean(integrated_grads, axis=0)\n",
-    "\n"
+    ""
    ]
   },
   {
@@ -241,7 +242,7 @@
     "colab_type": "text"
    },
    "source": [
-    "## Helper class for visualizing gradients and integrated gradients\n"
+    "## Helper class for visualizing gradients and integrated gradients"
    ]
   },
   {
@@ -337,7 +338,10 @@
     "        return opened\n",
     "\n",
     "    def draw_outlines(\n",
-    "        self, attributions, percentage=90, connected_component_structure=np.ones((3, 3))\n",
+    "        self,\n",
+    "        attributions,\n",
+    "        percentage=90,\n",
+    "        connected_component_structure=np.ones((3, 3)),\n",
     "    ):\n",
     "        # 1. Binarize the attributions.\n",
     "        attributions = self.binarize(attributions)\n",
@@ -346,7 +350,7 @@
     "        attributions = ndimage.binary_fill_holes(attributions)\n",
     "\n",
     "        # 3. Compute connected components\n",
-    "        connected_components, num_comp = ndimage.measurements.label(\n",
+    "        connected_components, num_comp = ndimage.label(\n",
     "            attributions, structure=connected_component_structure\n",
     "        )\n",
     "\n",
@@ -498,7 +502,7 @@
     "        ax[1].set_title(\"Normal gradients\")\n",
     "        ax[2].set_title(\"Integrated gradients\")\n",
     "        plt.show()\n",
-    "\n"
+    ""
    ]
   },
   {
@@ -507,9 +511,7 @@
     "colab_type": "text"
    },
    "source": [
-    "## Let's test-drive it\n",
-    "\n",
-    "You can try the model on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/integrated_gradients)."
+    "## Let's test-drive it"
    ]
   },
   {
@@ -560,12 +562,12 @@
     "    clip_below_percentile=28,\n",
     "    morphological_cleanup=True,\n",
     "    outlines=True,\n",
-    ")\n"
+    ")"
    ]
   }
  ],
  "metadata": {
-  "accelerator": "GPU",
+  "accelerator": "None",
   "colab": {
    "collapsed_sections": [],
    "name": "integrated_gradients",

diff --git a/examples/vision/md/integrated_gradients.md b/examples/vision/md/integrated_gradients.md
@@ -49,23 +49,22 @@ consider reading this excellent
 - Integrated Gradients original [paper](https://arxiv.org/abs/1703.01365)
 - [Original implementation](https://github.com/ankurtaly/Integrated-Gradients)
 
-
 ---
 ## Setup
 
 
-
 ```python
 
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy import ndimage
-from IPython.display import Image
+from IPython.display import Image, display
 
 import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-from tensorflow.keras.applications import xception
+import keras
+from keras import layers
+from keras.applications import xception
+
 
 # Size of the input image
 img_size = (299, 299, 3)
@@ -76,24 +75,18 @@ model = xception.Xception(weights="imagenet")
 # The local path to our target image
 img_path = keras.utils.get_file("elephant.jpg", "https://i.imgur.com/Bvro0YD.png")
 display(Image(img_path))
-
 ```
 
-<div class="k-default-codeblock">
-```
-Downloading data from https://i.imgur.com/Bvro0YD.png
-4218880/4217496 [==============================] - 0s 0us/step
 
-```
-</div>
-![jpeg](/img/examples/vision/integrated_gradients/integrated_gradients_3_1.jpeg)
+    
+![jpeg](/img/examples/vision/integrated_gradients/integrated_gradients_3_0.jpg)
+    
 
 
 ---
 ## Integrated Gradients algorithm
 
 
-
 ```python
 
 def get_img_array(img_path, size=(299, 299)):
@@ -213,14 +206,12 @@ def random_baseline_integrated_gradients(
     integrated_grads = tf.convert_to_tensor(integrated_grads)
     return tf.reduce_mean(integrated_grads, axis=0)
 
-
 ```
 
 ---
 ## Helper class for visualizing gradients and integrated gradients
 
 
-
 ```python
 
 class GradVisualizer:
@@ -307,7 +298,10 @@ class GradVisualizer:
         return opened
 
     def draw_outlines(
-        self, attributions, percentage=90, connected_component_structure=np.ones((3, 3))
+        self,
+        attributions,
+        percentage=90,
+        connected_component_structure=np.ones((3, 3)),
     ):
         # 1. Binarize the attributions.
         attributions = self.binarize(attributions)
@@ -316,7 +310,7 @@ class GradVisualizer:
         attributions = ndimage.binary_fill_holes(attributions)
 
         # 3. Compute connected components
-        connected_components, num_comp = ndimage.measurements.label(
+        connected_components, num_comp = ndimage.label(
             attributions, structure=connected_component_structure
         )
 
@@ -469,12 +463,10 @@ class GradVisualizer:
         ax[2].set_title("Integrated gradients")
         plt.show()
 
-
 ```
 
 ---
 ## Let's test-drive it
-You can try the model on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/integrated_gradients).
 
 
 ```python
@@ -519,17 +511,26 @@ vis.visualize(
     morphological_cleanup=True,
     outlines=True,
 )
-
 ```
 
 <div class="k-default-codeblock">
 ```
+ 1/1 ━━━━━━━━━━━━━━━━━━━━ 5s 5s/step
+
+WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
+I0000 00:00:1699486705.534012   86541 device_compiler.h:187] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
+
 Predicted: tf.Tensor(386, shape=(), dtype=int64) [('n02504458', 'African_elephant', 0.8871446)]
 
 ```
 </div>
-![png](/img/examples/vision/integrated_gradients/integrated_gradients_9_1.png)
+
+![png](/img/examples/vision/integrated_gradients/integrated_gradients_9_3.png)
+
+
 
 
+
+![png](/img/examples/vision/integrated_gradients/integrated_gradients_9_4.png)
+
 
-![png](/img/examples/vision/integrated_gradients/integrated_gradients_9_2.png)