Body: <p><strong>My datasets are not actual images, so using methods with ImageDataGenerator or pre-trained networks might not apply in this case.</strong></p>
  
  <p>Data Structure: Each "image" is a 2048-long vector that has float values between 0 and 1.</p>
  
  <p><a href="https://i.sstatic.net/S3UfL.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/S3UfL.png" alt="an &quot;image&quot; plotted"></a></p>
  
  <p>Each "image" was associated with a label (multi-label classifcation) and the goal is to perform classification via Keras 2D CNN's.</p>
  
  <p>What are common techniques for finding which parts of the "images" contribute most to classification via convolutional neural nets?</p>
  
  <p>I already implemented the CNNs in keras and have already successfully trained on my images.</p>
  
  <p>*No my data is not time series; however, my model works with either the keras Conv1D and Conv2D layers. </p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-17T20:29:47.760Z
Comments.Text: What is the premise to consider the data as **image**? The input vector is essentially a feature vector of size 2048.
Comments.UserDisplayName: Karan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-17T20:33:14.137Z
Comments.Text: I do not explicitly consider it as an image (hence using the quotes around image); however, I used a CNN as it has image-like qualities. The data has spatial features that determine its label. Regardless, is it possible to get any insight to what the CNN learns on? (whether it may be a CAM or something else)
Comments.UserDisplayName: rajkarthikkumar
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-17T20:51:47.347Z
Comments.Text: if your data has signal property then CNN would work. What you are looking for is Saliency Map, you need to do guided back prop to see what your CNN is focusing on. Check out [raghakot/keras-vis](https://github.com/raghakot/keras-vis) Github repo.
Comments.UserDisplayName: Karan
Tags: <machine-learning><convolutional-neural-networks><classification><keras>
Title: CNN attention maps on non-images
UserDisplayName: rajkarthikkumar
ThreadText: # CNN attention maps on non-images
  
  (Asked by: username_0 on 2017-07-17)
  
  **My datasets are not actual images, so using methods with ImageDataGenerator or pre-trained networks might not apply in this case.**
  
  Data Structure: Each "image" is a 2048-long vector that has float values between 0 and 1.
  
  [![an ](https://i.sstatic.net/S3UfL.png)](https://i.sstatic.net/S3UfL.png)
  
  Each "image" was associated with a label (multi-label classifcation) and the goal is to perform classification via Keras 2D CNN's.
  
  What are common techniques for finding which parts of the "images" contribute most to classification via convolutional neural nets?
  
  I already implemented the CNNs in keras and have already successfully trained on my images.
  
  \*No my data is not time series; however, my model works with either the keras Conv1D and Conv2D layers.
  
  **Tags:** machine-learning, convolutional-neural-networks, classification, keras
  > **Comments:**
  > **username_1**: What is the premise to consider the data as **image**? The input vector is essentially a feature vector of size 2048.  
  > **username_0**: I do not explicitly consider it as an image (hence using the quotes around image); however, I used a CNN as it has image-like qualities. The data has spatial features that determine its label. Regardless, is it possible to get any insight to what the CNN learns on? (whether it may be a CAM or something else)  
  > **username_1**: if your data has signal property then CNN would work. What you are looking for is Saliency Map, you need to do guided back prop to see what your CNN is focusing on. Check out [raghakot/keras-vis](https://github.com/raghakot/keras-vis) Github repo.  
  
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  ## 0 answers
  
  