[GitHub] indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM
indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on
visualizing CNN decisions using Grad-CAM
URL: https://github.com/apache/incubator-mxnet/pull/10900#discussion_r189387494
##
File path: docs/tutorials/vision/cnn_visualization.md
##
@@ -0,0 +1,250 @@
+# Visualizing Decisions of Convolutional Neural Networks
+
+Convolutional Neural Networks have made a lot of progress in Computer Vision.
Their accuracy is as good as humans in some tasks. However it remains hard to
explain the predictions of convolutional neural networks.
+
+It is often helpful to be able to explain why a model made the prediction it
made. For example when a model misclassifies an image, it is hard to say why
without visualizing the network's decision.
+
+https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png;
alt="Explaining the misclassification of volcano as spider" width=500px/>
+
+Visualizations also help build confidence about the predictions of a model.
For example, even if a model correctly predicts birds as birds, we would want
to confirm that the model bases its decision on the features of bird and not on
the features of some other object that might occur together with birds in the
dataset (like leaves).
+
+In this tutorial, we show how to visualize the predictions made by
convolutional neural networks using Gradient-weighted Class Activation Mapping.
Unlike many other visualization methods, Grad-CAM can be used on a wide variety
of CNN model families - CNNs with fully connected layers, CNNs used for
structural outputs (e.g. captioning), CNNs used in tasks with multi-model input
(e.g. VQA) or reinforcement learning without architectural changes or
re-training.
+
+In the rest of this notebook, we will explain how to visualize predictions
made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the
required dependencies. `gradcam` module contains the implementation of
visualization techniques used in this notebook.
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+from mxnet import gluon
+
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+
+gradcam_file = "gradcam.py"
+base_url =
"https://raw.githubusercontent.com/indhub/mxnet/cnnviz/example/cnn_visualization/{}?raw=true;
+mx.test_utils.download(base_url.format(gradcam_file), fname=gradcam_file)
+import gradcam
+```
+
+## Building the network to visualize
+
+Next, we build the network we want to visualize. For this example, we will use
the [VGG-16](https://arxiv.org/abs/1409.1556) network. This code was taken from
the Gluon [model
zoo](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/model_zoo/vision/alexnet.py)
and refactored to make it easy to switch between `gradcam`'s and Gluon's
implementation of ReLU and Conv2D. Same code can be used for both training and
visualization with a minor (one line) change.
+
+Notice that we import ReLU and Conv2D from `gradcam` module instead of
mxnet.gluon.nn.
+- We use a modified ReLU because we use guided backpropagation for
visualization and guided backprop requires ReLU layer to block the backward
flow of negative gradients corresponding to the neurons which decrease the
activation of the higher layer unit we aim to visualize. Check
[this](https://arxiv.org/abs/1412.6806) paper to learn more about guided
backprop.
+- We use a modified Conv2D (a wrapper on top of Gluon's Conv2D) because we
want to capture the output of a given convolutional layer and its gradients.
This is needed to implement Grad-CAM. Check
[this](https://arxiv.org/abs/1610.02391) paper to learn more about Grad-CAM.
+
+When you train the network, you could just import `Activation` and `Conv2D`
from `gluon.nn` instead. No other part of the code needs any change to switch
between training and visualization.
+
+```python
+import os
+from mxnet.gluon.model_zoo import model_store
+
+from mxnet.initializer import Xavier
+from mxnet.gluon.nn import MaxPool2D, Flatten, Dense, Dropout, BatchNorm
+from gradcam import Activation, Conv2D
+
+class VGG(mx.gluon.HybridBlock):
+def __init__(self, layers, filters, classes=1000, batch_norm=False,
**kwargs):
+super(VGG, self).__init__(**kwargs)
+assert len(layers) == len(filters)
+with self.name_scope():
+self.features = self._make_features(layers, filters, batch_norm)
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.output
[GitHub] indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM
indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on
visualizing CNN decisions using Grad-CAM
URL: https://github.com/apache/incubator-mxnet/pull/10900#discussion_r189123063
##
File path: docs/tutorials/vision/cnn_visualization.md
##
@@ -0,0 +1,250 @@
+# Visualizing Decisions of Convolutional Neural Networks
+
+Convolutional Neural Networks have made a lot of progress in Computer Vision.
Their accuracy is as good as humans in some tasks. However it remains hard to
explain the predictions of convolutional neural networks.
+
+It is often helpful to be able to explain why a model made the prediction it
made. For example when a model misclassifies an image, it is hard to say why
without visualizing the network's decision.
+
+https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png;
alt="Explaining the misclassification of volcano as spider" width=500px/>
+
+Visualizations also help build confidence about the predictions of a model.
For example, even if a model correctly predicts birds as birds, we would want
to confirm that the model bases its decision on the features of bird and not on
the features of some other object that might occur together with birds in the
dataset (like leaves).
+
+In this tutorial, we show how to visualize the predictions made by
convolutional neural networks using Gradient-weighted Class Activation Mapping.
Unlike many other visualization methods, Grad-CAM can be used on a wide variety
of CNN model families - CNNs with fully connected layers, CNNs used for
structural outputs (e.g. captioning), CNNs used in tasks with multi-model input
(e.g. VQA) or reinforcement learning without architectural changes or
re-training.
+
+In the rest of this notebook, we will explain how to visualize predictions
made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the
required dependencies. `gradcam` module contains the implementation of
visualization techniques used in this notebook.
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+from mxnet import gluon
+
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+
+gradcam_file = "gradcam.py"
+base_url =
"https://raw.githubusercontent.com/indhub/mxnet/cnnviz/example/cnn_visualization/{}?raw=true;
+mx.test_utils.download(base_url.format(gradcam_file), fname=gradcam_file)
+import gradcam
+```
+
+## Building the network to visualize
+
+Next, we build the network we want to visualize. For this example, we will use
the [VGG-16](https://arxiv.org/abs/1409.1556) network. This code was taken from
the Gluon [model
zoo](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/model_zoo/vision/alexnet.py)
and refactored to make it easy to switch between `gradcam`'s and Gluon's
implementation of ReLU and Conv2D. Same code can be used for both training and
visualization with a minor (one line) change.
+
+Notice that we import ReLU and Conv2D from `gradcam` module instead of
mxnet.gluon.nn.
+- We use a modified ReLU because we use guided backpropagation for
visualization and guided backprop requires ReLU layer to block the backward
flow of negative gradients corresponding to the neurons which decrease the
activation of the higher layer unit we aim to visualize. Check
[this](https://arxiv.org/abs/1412.6806) paper to learn more about guided
backprop.
+- We use a modified Conv2D (a wrapper on top of Gluon's Conv2D) because we
want to capture the output of a given convolutional layer and its gradients.
This is needed to implement Grad-CAM. Check
[this](https://arxiv.org/abs/1610.02391) paper to learn more about Grad-CAM.
+
+When you train the network, you could just import `Activation` and `Conv2D`
from `gluon.nn` instead. No other part of the code needs any change to switch
between training and visualization.
+
+```python
+import os
+from mxnet.gluon.model_zoo import model_store
+
+from mxnet.initializer import Xavier
+from mxnet.gluon.nn import MaxPool2D, Flatten, Dense, Dropout, BatchNorm
+from gradcam import Activation, Conv2D
+
+class VGG(mx.gluon.HybridBlock):
+def __init__(self, layers, filters, classes=1000, batch_norm=False,
**kwargs):
+super(VGG, self).__init__(**kwargs)
+assert len(layers) == len(filters)
+with self.name_scope():
+self.features = self._make_features(layers, filters, batch_norm)
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.output
[GitHub] indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM
indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on
visualizing CNN decisions using Grad-CAM
URL: https://github.com/apache/incubator-mxnet/pull/10900#discussion_r189122590
##
File path: docs/tutorials/vision/cnn_visualization.md
##
@@ -0,0 +1,250 @@
+# Visualizing Decisions of Convolutional Neural Networks
+
+Convolutional Neural Networks have made a lot of progress in Computer Vision.
Their accuracy is as good as humans in some tasks. However it remains hard to
explain the predictions of convolutional neural networks.
+
+It is often helpful to be able to explain why a model made the prediction it
made. For example when a model misclassifies an image, it is hard to say why
without visualizing the network's decision.
+
+https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png;
alt="Explaining the misclassification of volcano as spider" width=500px/>
+
+Visualizations also help build confidence about the predictions of a model.
For example, even if a model correctly predicts birds as birds, we would want
to confirm that the model bases its decision on the features of bird and not on
the features of some other object that might occur together with birds in the
dataset (like leaves).
+
+In this tutorial, we show how to visualize the predictions made by
convolutional neural networks using Gradient-weighted Class Activation Mapping.
Unlike many other visualization methods, Grad-CAM can be used on a wide variety
of CNN model families - CNNs with fully connected layers, CNNs used for
structural outputs (e.g. captioning), CNNs used in tasks with multi-model input
(e.g. VQA) or reinforcement learning without architectural changes or
re-training.
+
+In the rest of this notebook, we will explain how to visualize predictions
made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the
required dependencies. `gradcam` module contains the implementation of
visualization techniques used in this notebook.
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+from mxnet import gluon
+
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+
+gradcam_file = "gradcam.py"
+base_url =
"https://raw.githubusercontent.com/indhub/mxnet/cnnviz/example/cnn_visualization/{}?raw=true;
+mx.test_utils.download(base_url.format(gradcam_file), fname=gradcam_file)
+import gradcam
+```
+
+## Building the network to visualize
+
+Next, we build the network we want to visualize. For this example, we will use
the [VGG-16](https://arxiv.org/abs/1409.1556) network. This code was taken from
the Gluon [model
zoo](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/model_zoo/vision/alexnet.py)
and refactored to make it easy to switch between `gradcam`'s and Gluon's
implementation of ReLU and Conv2D. Same code can be used for both training and
visualization with a minor (one line) change.
+
+Notice that we import ReLU and Conv2D from `gradcam` module instead of
mxnet.gluon.nn.
+- We use a modified ReLU because we use guided backpropagation for
visualization and guided backprop requires ReLU layer to block the backward
flow of negative gradients corresponding to the neurons which decrease the
activation of the higher layer unit we aim to visualize. Check
[this](https://arxiv.org/abs/1412.6806) paper to learn more about guided
backprop.
+- We use a modified Conv2D (a wrapper on top of Gluon's Conv2D) because we
want to capture the output of a given convolutional layer and its gradients.
This is needed to implement Grad-CAM. Check
[this](https://arxiv.org/abs/1610.02391) paper to learn more about Grad-CAM.
+
+When you train the network, you could just import `Activation` and `Conv2D`
from `gluon.nn` instead. No other part of the code needs any change to switch
between training and visualization.
+
+```python
+import os
+from mxnet.gluon.model_zoo import model_store
+
+from mxnet.initializer import Xavier
+from mxnet.gluon.nn import MaxPool2D, Flatten, Dense, Dropout, BatchNorm
+from gradcam import Activation, Conv2D
+
+class VGG(mx.gluon.HybridBlock):
+def __init__(self, layers, filters, classes=1000, batch_norm=False,
**kwargs):
+super(VGG, self).__init__(**kwargs)
+assert len(layers) == len(filters)
+with self.name_scope():
+self.features = self._make_features(layers, filters, batch_norm)
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.output
[GitHub] indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM
indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on
visualizing CNN decisions using Grad-CAM
URL: https://github.com/apache/incubator-mxnet/pull/10900#discussion_r189122599
##
File path: docs/tutorials/vision/cnn_visualization.md
##
@@ -0,0 +1,250 @@
+# Visualizing Decisions of Convolutional Neural Networks
+
+Convolutional Neural Networks have made a lot of progress in Computer Vision.
Their accuracy is as good as humans in some tasks. However it remains hard to
explain the predictions of convolutional neural networks.
+
+It is often helpful to be able to explain why a model made the prediction it
made. For example when a model misclassifies an image, it is hard to say why
without visualizing the network's decision.
+
+https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png;
alt="Explaining the misclassification of volcano as spider" width=500px/>
+
+Visualizations also help build confidence about the predictions of a model.
For example, even if a model correctly predicts birds as birds, we would want
to confirm that the model bases its decision on the features of bird and not on
the features of some other object that might occur together with birds in the
dataset (like leaves).
+
+In this tutorial, we show how to visualize the predictions made by
convolutional neural networks using Gradient-weighted Class Activation Mapping.
Unlike many other visualization methods, Grad-CAM can be used on a wide variety
of CNN model families - CNNs with fully connected layers, CNNs used for
structural outputs (e.g. captioning), CNNs used in tasks with multi-model input
(e.g. VQA) or reinforcement learning without architectural changes or
re-training.
+
+In the rest of this notebook, we will explain how to visualize predictions
made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the
required dependencies. `gradcam` module contains the implementation of
visualization techniques used in this notebook.
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+from mxnet import gluon
+
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+
+gradcam_file = "gradcam.py"
+base_url =
"https://raw.githubusercontent.com/indhub/mxnet/cnnviz/example/cnn_visualization/{}?raw=true;
+mx.test_utils.download(base_url.format(gradcam_file), fname=gradcam_file)
+import gradcam
+```
+
+## Building the network to visualize
+
+Next, we build the network we want to visualize. For this example, we will use
the [VGG-16](https://arxiv.org/abs/1409.1556) network. This code was taken from
the Gluon [model
zoo](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/model_zoo/vision/alexnet.py)
and refactored to make it easy to switch between `gradcam`'s and Gluon's
implementation of ReLU and Conv2D. Same code can be used for both training and
visualization with a minor (one line) change.
+
+Notice that we import ReLU and Conv2D from `gradcam` module instead of
mxnet.gluon.nn.
+- We use a modified ReLU because we use guided backpropagation for
visualization and guided backprop requires ReLU layer to block the backward
flow of negative gradients corresponding to the neurons which decrease the
activation of the higher layer unit we aim to visualize. Check
[this](https://arxiv.org/abs/1412.6806) paper to learn more about guided
backprop.
+- We use a modified Conv2D (a wrapper on top of Gluon's Conv2D) because we
want to capture the output of a given convolutional layer and its gradients.
This is needed to implement Grad-CAM. Check
[this](https://arxiv.org/abs/1610.02391) paper to learn more about Grad-CAM.
+
+When you train the network, you could just import `Activation` and `Conv2D`
from `gluon.nn` instead. No other part of the code needs any change to switch
between training and visualization.
+
+```python
+import os
+from mxnet.gluon.model_zoo import model_store
+
+from mxnet.initializer import Xavier
+from mxnet.gluon.nn import MaxPool2D, Flatten, Dense, Dropout, BatchNorm
+from gradcam import Activation, Conv2D
+
+class VGG(mx.gluon.HybridBlock):
+def __init__(self, layers, filters, classes=1000, batch_norm=False,
**kwargs):
+super(VGG, self).__init__(**kwargs)
+assert len(layers) == len(filters)
+with self.name_scope():
+self.features = self._make_features(layers, filters, batch_norm)
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.output
[GitHub] indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM
indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on
visualizing CNN decisions using Grad-CAM
URL: https://github.com/apache/incubator-mxnet/pull/10900#discussion_r189122530
##
File path: docs/tutorials/vision/cnn_visualization.md
##
@@ -0,0 +1,250 @@
+# Visualizing Decisions of Convolutional Neural Networks
+
+Convolutional Neural Networks have made a lot of progress in Computer Vision.
Their accuracy is as good as humans in some tasks. However it remains hard to
explain the predictions of convolutional neural networks.
+
+It is often helpful to be able to explain why a model made the prediction it
made. For example when a model misclassifies an image, it is hard to say why
without visualizing the network's decision.
+
+https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png;
alt="Explaining the misclassification of volcano as spider" width=500px/>
+
+Visualizations also help build confidence about the predictions of a model.
For example, even if a model correctly predicts birds as birds, we would want
to confirm that the model bases its decision on the features of bird and not on
the features of some other object that might occur together with birds in the
dataset (like leaves).
+
+In this tutorial, we show how to visualize the predictions made by
convolutional neural networks using Gradient-weighted Class Activation Mapping.
Unlike many other visualization methods, Grad-CAM can be used on a wide variety
of CNN model families - CNNs with fully connected layers, CNNs used for
structural outputs (e.g. captioning), CNNs used in tasks with multi-model input
(e.g. VQA) or reinforcement learning without architectural changes or
re-training.
+
+In the rest of this notebook, we will explain how to visualize predictions
made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the
required dependencies. `gradcam` module contains the implementation of
visualization techniques used in this notebook.
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+from mxnet import gluon
+
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+
+gradcam_file = "gradcam.py"
+base_url =
"https://raw.githubusercontent.com/indhub/mxnet/cnnviz/example/cnn_visualization/{}?raw=true;
+mx.test_utils.download(base_url.format(gradcam_file), fname=gradcam_file)
+import gradcam
+```
+
+## Building the network to visualize
+
+Next, we build the network we want to visualize. For this example, we will use
the [VGG-16](https://arxiv.org/abs/1409.1556) network. This code was taken from
the Gluon [model
zoo](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/model_zoo/vision/alexnet.py)
and refactored to make it easy to switch between `gradcam`'s and Gluon's
implementation of ReLU and Conv2D. Same code can be used for both training and
visualization with a minor (one line) change.
+
+Notice that we import ReLU and Conv2D from `gradcam` module instead of
mxnet.gluon.nn.
+- We use a modified ReLU because we use guided backpropagation for
visualization and guided backprop requires ReLU layer to block the backward
flow of negative gradients corresponding to the neurons which decrease the
activation of the higher layer unit we aim to visualize. Check
[this](https://arxiv.org/abs/1412.6806) paper to learn more about guided
backprop.
+- We use a modified Conv2D (a wrapper on top of Gluon's Conv2D) because we
want to capture the output of a given convolutional layer and its gradients.
This is needed to implement Grad-CAM. Check
[this](https://arxiv.org/abs/1610.02391) paper to learn more about Grad-CAM.
+
+When you train the network, you could just import `Activation` and `Conv2D`
from `gluon.nn` instead. No other part of the code needs any change to switch
between training and visualization.
+
+```python
+import os
+from mxnet.gluon.model_zoo import model_store
+
+from mxnet.initializer import Xavier
+from mxnet.gluon.nn import MaxPool2D, Flatten, Dense, Dropout, BatchNorm
+from gradcam import Activation, Conv2D
+
+class VGG(mx.gluon.HybridBlock):
+def __init__(self, layers, filters, classes=1000, batch_norm=False,
**kwargs):
+super(VGG, self).__init__(**kwargs)
+assert len(layers) == len(filters)
+with self.name_scope():
+self.features = self._make_features(layers, filters, batch_norm)
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.features.add(Dense(4096, activation='relu',
+ weight_initializer='normal',
+ bias_initializer='zeros'))
+self.features.add(Dropout(rate=0.5))
+self.output
[GitHub] indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM
indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM URL: https://github.com/apache/incubator-mxnet/pull/10900#discussion_r189122496 ## File path: docs/tutorials/vision/cnn_visualization.md ## @@ -0,0 +1,250 @@ +# Visualizing Decisions of Convolutional Neural Networks + +Convolutional Neural Networks have made a lot of progress in Computer Vision. Their accuracy is as good as humans in some tasks. However it remains hard to explain the predictions of convolutional neural networks. + +It is often helpful to be able to explain why a model made the prediction it made. For example when a model misclassifies an image, it is hard to say why without visualizing the network's decision. + +https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png; alt="Explaining the misclassification of volcano as spider" width=500px/> + +Visualizations also help build confidence about the predictions of a model. For example, even if a model correctly predicts birds as birds, we would want to confirm that the model bases its decision on the features of bird and not on the features of some other object that might occur together with birds in the dataset (like leaves). + +In this tutorial, we show how to visualize the predictions made by convolutional neural networks using Gradient-weighted Class Activation Mapping. Unlike many other visualization methods, Grad-CAM can be used on a wide variety of CNN model families - CNNs with fully connected layers, CNNs used for structural outputs (e.g. captioning), CNNs used in tasks with multi-model input (e.g. VQA) or reinforcement learning without architectural changes or re-training. + +In the rest of this notebook, we will explain how to visualize predictions made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the required dependencies. `gradcam` module contains the implementation of visualization techniques used in this notebook. Review comment: VGG is used for a couple of reasons: 1. ResNet is too big to include in a notebook without spoiling easiness to read. 2. VGG16 the network used in the original paper. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on visualizing CNN decisions using Grad-CAM
indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on
visualizing CNN decisions using Grad-CAM
URL: https://github.com/apache/incubator-mxnet/pull/10900#discussion_r189122505
##
File path: docs/tutorials/vision/cnn_visualization.md
##
@@ -0,0 +1,250 @@
+# Visualizing Decisions of Convolutional Neural Networks
+
+Convolutional Neural Networks have made a lot of progress in Computer Vision.
Their accuracy is as good as humans in some tasks. However it remains hard to
explain the predictions of convolutional neural networks.
+
+It is often helpful to be able to explain why a model made the prediction it
made. For example when a model misclassifies an image, it is hard to say why
without visualizing the network's decision.
+
+https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/cnn_visualization/volcano_barn_spider.png;
alt="Explaining the misclassification of volcano as spider" width=500px/>
+
+Visualizations also help build confidence about the predictions of a model.
For example, even if a model correctly predicts birds as birds, we would want
to confirm that the model bases its decision on the features of bird and not on
the features of some other object that might occur together with birds in the
dataset (like leaves).
+
+In this tutorial, we show how to visualize the predictions made by
convolutional neural networks using Gradient-weighted Class Activation Mapping.
Unlike many other visualization methods, Grad-CAM can be used on a wide variety
of CNN model families - CNNs with fully connected layers, CNNs used for
structural outputs (e.g. captioning), CNNs used in tasks with multi-model input
(e.g. VQA) or reinforcement learning without architectural changes or
re-training.
+
+In the rest of this notebook, we will explain how to visualize predictions
made by [VGG-16](https://arxiv.org/abs/1409.1556). We begin by importing the
required dependencies. `gradcam` module contains the implementation of
visualization techniques used in this notebook.
+
+```python
+from __future__ import print_function
+
+import mxnet as mx
+from mxnet import gluon
+
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+
+gradcam_file = "gradcam.py"
+base_url =
"https://raw.githubusercontent.com/indhub/mxnet/cnnviz/example/cnn_visualization/{}?raw=true;
Review comment:
Will remember to switch.
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