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diff --git a/examples/vision/integrated_gradients.py b/examples/vision/integrated_gradients.py
index 1efe9f6da9..e7d012a8b4 100644
--- 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
index 38f43cd924..d1ce118c0b 100644
--- 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
index 6febfa3392..b1496cc3b4 100644
--- 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))
-
```
-
-```
-Downloading data from https://i.imgur.com/Bvro0YD.png
-4218880/4217496 [==============================] - 0s 0us/step
-```
-
-
+
+
+
---
## 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,
)
-
```
```
+ 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)]
```
-
+
+
+
+
+
+
+
-
\ No newline at end of file