deep-learning | The goals of these exercices | Machine Learning library

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kandi X-RAY | deep-learning Summary

kandi X-RAY | deep-learning Summary

deep-learning is a HTML library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. deep-learning has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

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              deep-learning has a low active ecosystem.
              It has 49 star(s) with 22 fork(s). There are 11 watchers for this library.
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              It had no major release in the last 6 months.
              deep-learning has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of deep-learning is current.

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              deep-learning has no bugs reported.

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              deep-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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            Community Discussions

            QUESTION

            Does it make sense to backpropagate a loss calculated from an earlier layer through the entire network?
            Asked 2021-Jun-09 at 10:56

            Suppose you have a neural network with 2 layers A and B. A gets the network input. A and B are consecutive (A's output is fed into B as input). Both A and B output predictions (prediction1 and prediction2) Picture of the described architecture You calculate a loss (loss1) directly after the first layer (A) with a target (target1). You also calculate a loss after the second layer (loss2) with its own target (target2).

            Does it make sense to use the sum of loss1 and loss2 as the error function and back propagate this loss through the entire network? If so, why is it "allowed" to back propagate loss1 through B even though it has nothing to do with it?

            This question is related to this question https://datascience.stackexchange.com/questions/37022/intuition-importance-of-intermediate-supervision-in-deep-learning but it does not answer my question sufficiently. In my case, A and B are unrelated modules. In the aforementioned question, A and B would be identical. The targets would be the same, too.

            (Additional information) The reason why I'm asking is that I'm trying to understand LCNN (https://github.com/zhou13/lcnn) from this paper. LCNN is made up of an Hourglass backbone, which then gets fed into MultiTask Learner (creates loss1), which in turn gets fed into a LineVectorizer Module (loss2). Both loss1 and loss2 are then summed up here and then back propagated through the entire network here.

            Even though I've visited several deep learning lectures, I didn't know this was "allowed" or makes sense to do. I would have expected to use two loss.backward(), one for each loss. Or is the pytorch computational graph doing something magical here? LCNN converges and outperforms other neural networks which try to solve the same task.

            ...

            ANSWER

            Answered 2021-Jun-09 at 10:56
            Yes, It is "allowed" and also makes sense.

            From the question, I believe you have understood most of it so I'm not going to details about why this multi-loss architecture can be useful. I think the main part that has made you confused is why does "loss1" back-propagate through "B"? and the answer is: It doesn't. The fact is that loss1 is calculated using this formula:

            Source https://stackoverflow.com/questions/67902284

            QUESTION

            Can't create GPU instances on GCE
            Asked 2021-Jun-07 at 14:15

            I am trying to create a GPU instance (n1-standard-2 with 1 NVIDIA T4 GPU) on Compute Engine and I have been getting this error since yesterday:

            ...

            ANSWER

            Answered 2021-Jun-06 at 19:50

            Finally, I was able to launch a preemptible GPU instance without a problem. So it really seems like Google Cloud doesn't have enough GPU resources to reserve an on-demand GPU VM at the moment.

            Source https://stackoverflow.com/questions/67853781

            QUESTION

            Illegal instruction(core dumped) error on Jetson Nano
            Asked 2021-May-06 at 23:26

            Sorry if my description is long and boring but I want to give you most important details to solve my problem. Recently I bought a Jetson Nano Developer Kit with 4Gb of RAM, finally!, and in order to get, which I consider, the best configuration for object detection I am following this guide made by Adrian Rosebrock from Pyimagesearch:

            https://www.pyimagesearch.com/2020/03/25/how-to-configure-your-nvidia-jetson-nano-for-computer-vision-and-deep-learning/ Date:March, 2020. A summary of this guide is the following:

            • 1: Flash Jetson Pack 4.2 .img inside a microSD for Jetson Nano(mine is 32GB 'A' Class)
            • 2: Once inserted on the Nano board, configure Ubuntu 18.04 and get rid of Libreoffice entirely to get more available space
            • 3: Step #5: Install system-level dependencies( Including cmake, python3, and nano editor)
            • 4: Update CMake (without any errors)
            • 5: Install OpenCV system-level dependencies and other development dependencies
            • 6: Set up Python virtual environments on your Jetson Nano( succesfully installed virtualenv and virtualenvwrapper without errors including the bash file edition with nano)
            • 7: Create virtaul env with python 3 and install protobuf and libprotobuf to get an more efficient Tensorflow. Succesfully installed. It took an hour to finish, that's normal
            • 8: Here comes the headbreaker: install numpy and cython inside this env and check it importing numpy library When I try to do this step I get: Illegal instruction(core dumped) as you can see in the image: [Error with Python3.6.9]: https://i.stack.imgur.com/rAZhm.png

            I said, well let's continue with this tutorial anyway:

            • 9: Install Scipy v1.3.3: everything is ok with first three lines, but when I have to use python to execute the stup.py file, IT shows up again(not the clown). [Can't execute this line either]: https://i.stack.imgur.com/wFmnt.jpg

            Then I ran an experiment, I have created this "p2cv4" env with Python 2, installed numpy and tested it: [With Python 2]: https://i.stack.imgur.com/zCWif.png

            I can exit() whenever I want and execute other lines that use python So I concluded that is a python version issue. When I want to execute any python code, terminal ends the program with core dumping, apt-get or pip DO NOT show any errors. And I want to use python 3 because someday in the future a package or library will require python 3.

            For python 3 last version for the Jetson Nano is 3.6.9, and idk which version was currently active in March, 2020, like the one Adrian used at that time

            In other posts I read that this SIGILL appears when a package or library version like Numpy of TF is not friendly anymore with a specific old or low power CPU, like in this posts: Illegal hardware instruction when trying to import tensorflow, https://github.com/numpy/numpy/issues/9532

            So I want to downgrade to a older python version like 3.6.5 or 3.5 but I can't find clear steps to do so in Ubuntu. I thinks this will fix this error and let me continue with configurations on the Jetson Nano.

            The pyimageseach guide uses Python 3.6 but it do not specifies if is last 3.6.9 or another. If is not python causing this error let me know. HELP please!

            ...

            ANSWER

            Answered 2021-Jan-09 at 15:30

            I had this very same problem following the same guide. BTW, in this scenario, numpy worked just fine in python when NOT in a virtualenv. GDB pointed to a problem in libopenblas.

            My solution was to start from scratch with a fresh image of jetson-nano-4gb-jp441-sd-card-image.zip and repeat that guide without using virtualenv. More than likely you are the sole developer on that Nano and can live without virtualenv.

            I have followed these guides with success: https://qengineering.eu/install-opencv-4.5-on-jetson-nano.html

            Skip the virtualenv portions https://www.pyimagesearch.com/2019/05/06/getting-started-with-the-nvidia-jetson-nano/

            I found this to also be required at this point: "..install the official Jetson Nano TensorFlow by.."

            Source https://stackoverflow.com/questions/65631801

            QUESTION

            Keras.NET Using a Model as a Layer
            Asked 2021-May-06 at 09:21

            In Python you can use a pretrained model as a layer as shown below (source here)

            ...

            ANSWER

            Answered 2021-May-06 at 09:21

            Solved using this API modification in Sequential.cs:

            Source https://stackoverflow.com/questions/67105434

            QUESTION

            What does the 4D array returned by net.forward() in OpenCV DNN means? I have little knowledge about deep learning
            Asked 2021-May-02 at 15:05

            I need to use face detection to finish my homework and then I searched on the Internet and I think that using a pre-trained deep learning face detector model with OpenCV's DNN module is easy and good, it works well. Where I learnt it is here: https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/ , but I am really confused about the 4D array returned by net.forward():

            ...

            ANSWER

            Answered 2021-May-02 at 15:05

            3rd dimension helps you iterate over predictions and

            in the 4th dimension, there are actual results

            class_lable = int(inference_results[0, 0, i,1]) --> gives one hot encoded class label for ith box

            conf = inference_results[0, 0, i, 2] --> gives confidence of ith box prediction

            TopLeftX,TopLeftY, BottomRightX, BottomRightY = inference_results[0, 0, i, 3:7] -->gives co-ordinates bounding boxes for resized small image

            and 2nd dimension is used when the predictions are made in more than one stages, for example in YOLO the predictions are done at 3 different layers. you can iterate over these predictions using 2nd dimension like [:,i,:,:]

            Source https://stackoverflow.com/questions/67355960

            QUESTION

            How to save the image with the red bounding boxes on it detected by mtcnn?
            Asked 2021-Apr-28 at 01:21

            I have this code in which mtcnn detects faces on an image, draws a red rectangle around each face and prints on the screen.

            Code taken from: https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

            But I want to save the image with the red boxes arround each face. So that i can do some preprocessing on it. Any help is good.

            ...

            ANSWER

            Answered 2021-Apr-28 at 01:21

            You can use matplotlib.pyplot.savefig. For example:

            Source https://stackoverflow.com/questions/67284840

            QUESTION

            Keras.NET How to Use KerasIterator
            Asked 2021-Apr-13 at 13:27

            I want to do the same as F. Chollet's notebook but in C#.

            However, I can't find a way to iterate over my KerasIterator object:

            ...

            ANSWER

            Answered 2021-Apr-13 at 13:15

            As of April 19. 2020 it is not possible with the .NET Wrapper as documented in this issue on the GitHub page for Keras.NET

            Source https://stackoverflow.com/questions/67075485

            QUESTION

            Numpy.NET Getting Values
            Asked 2021-Apr-13 at 12:47

            I'm trying to "convert" the Keras notebooks made by F. Chollet to C# / .NET applications. You can find them here. I am specifically working on "3.5 - Movie Reviews" as of right now.

            The problem is, I can't convert my NDarrays to C# arrays to use the values. I tried this method (in README - section Performance Considerations), but I get random values or Python Runtime errors.

            ...

            ANSWER

            Answered 2021-Apr-13 at 12:47

            Solved the issue parsing manually the attribute '.str' of 'line0' into an array of ints.

            Source https://stackoverflow.com/questions/66866731

            QUESTION

            How to find the direction of triangles in an image using OpenCV
            Asked 2021-Apr-06 at 12:33

            I am trying to find the direction of triangles in an image. below is the image:

            These triangles are pointing upward/downward/leftward/rightward. This is not the actual image. I have already used canny edge detection to find edges then contours and then the dilated image is shown below.

            My logic to find the direction:

            The logic I am thinking to use is that among the three corner coordinates If I can identify the base coordinates of the triangle (having the same abscissa or ordinates values coordinates), I can make a base vector. Then angle between unit vectors and base vectors can be used to identify the direction. But this method can only determine if it is up/down or left/right but cannot differentiate between up and down or right and left. I tried to find the corners using cv2.goodFeaturesToTrack but as I know it's giving only the 3 most effective points in the entire image. So I am wondering if there is other way to find the direction of triangles.

            Here is my code in python to differentiate between the triangle/square and circle:

            ...

            ANSWER

            Answered 2021-Apr-05 at 18:03

            Well, Mark has mentioned a solution that may not be as efficient but perhaps more accurate. I think this one should be equally efficient but perhaps less accurate. But since you already have a code that finds triangles, try adding the following code after you have found triangle contour:

            Source https://stackoverflow.com/questions/66953166

            QUESTION

            Scikit-learn cross_val_score throws ValueError: The first argument to `Layer.call` must always be passed
            Asked 2021-Apr-06 at 09:24

            I'm working on a deep learning project and I tried following a tutorial to evaluate my model with Cross-Validation.

            I was looking at this tutorial: https://machinelearningmastery.com/use-keras-deep-learning-models-scikit-learn-python/

            I started by first splitting my dataset into features and labels:

            ...

            ANSWER

            Answered 2021-Apr-06 at 06:08
            model = KerasClassifier(build_fn=create_model(), ...) 
            

            Source https://stackoverflow.com/questions/66963373

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