keras-grad-cam | An implementation of Grad-CAM with keras | Data Visualization library
kandi X-RAY | keras-grad-cam Summary
kandi X-RAY | keras-grad-cam Summary
An implementation of Grad-CAM with keras
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Compute the gradient of an image
- Normalize a tensor
- Computes the target category loss
- Deprocessing image
- Modify backprop
- Load an image
- Registers gradient function
- Compute saliency function
keras-grad-cam Key Features
keras-grad-cam Examples and Code Snippets
Community Discussions
Trending Discussions on keras-grad-cam
QUESTION
I'm trying to build a workflow that uses tf.data.dataset batches and an iterator. For performance reasons, I am really trying to avoid using the placeholder->feed_dict loop workflow.
The process I'm trying to implement involves grad-cam (which requires the gradient of the loss with respect to the final convolutional layer of a CNN) as an intermediate step, and ideally I'd like to be able to try it out on several Keras pre-trained models, including non-sequential ones like ResNet.
Most implementations of grad-cam that I've found rely on hand-crafting the CNN of interest in tensorflow. I found one implementation, https://github.com/jacobgil/keras-grad-cam, that is made for keras models, and following that example, I get
...ANSWER
Answered 2019-Aug-16 at 19:17Yes, K.function returns numpy arrays because it evaluates the symbolic computation in your graph. What I think you should do is to keep everything symbolic up to K.function, and after getting the gradients, perform all computations of the Grad-CAM weights and final saliency map using numpy.
Then you can iterate on your dataset, evaluate gradient_function on a new batch of data, and compute the saliency map.
If you want to keep everything symbolic, then you should not use K.function to produce the gradient function, but use the symbolic gradient (the output of K.gradient, without lambda) and convolutional feature maps (conv_output) and perform the saliency map computation on top of that, and then build a function (using K.function) that takes the model input, and outputs the saliency map.
Hope the explanation is enough.
QUESTION
I am producing heatmaps for my convolutional neural networks made via Keras, as described here. When I run that algorithm for a vanilla VGG16 net, the heatmap looks fine:
Then I created my own custom model, based on that VGG16 network, but with custom top layers:
ANSWER
Answered 2019-Jun-18 at 17:06Short answer: Since you have labeled a dog as 1 and a cat as 0 in training phase, you need to replace model_prediction with 1 - model_prediction to find the areas relevant to a cat:
QUESTION
I have a (float32) heatmap matrix in my Python program like this:
ANSWER
Answered 2019-May-23 at 13:43If it is for visualization, you will need to make a bigger separation between the numbers. You have basically 3 numbers and when you scale them (multiply 255) they become basically 2 numbers (254 and 255). When it applies the color, both of the numbers are almost the same, and it is really hard to know which one is which...
Solution:
Create a new scale with the current numbers. You can easily do that with cv2.normalize
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install keras-grad-cam
You can use keras-grad-cam like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
