indhub commented on a change in pull request #10900: [MXNET-414] Tutorial on
visualizing CNN decisions using Grad-CAM
File path: docs/tutorials/vision/cnn_visualization.md
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+# 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.
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
+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.
+from __future__ import print_function
+import mxnet as mx
+from mxnet import gluon
+from matplotlib import pyplot as plt
+import numpy as np
+gradcam_file = "gradcam.py"
Will remember to switch.
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