Deep-Learning | Deep learning practice : handwritten digit recognition

 by   Jack-Cherish Python Version: Current License: No License

kandi X-RAY | Deep-Learning Summary

kandi X-RAY | Deep-Learning Summary

Deep-Learning is a Python library. Deep-Learning has no bugs, it has no vulnerabilities and it has medium support. However Deep-Learning build file is not available. You can download it from GitHub.

:computer: Deep learning practice: handwritten digit recognition, Discuz verification code recognition, garbage classification, semantic segmentation
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              Deep-Learning has a medium active ecosystem.
              It has 1689 star(s) with 969 fork(s). There are 27 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 3 open issues and 11 have been closed. On average issues are closed in 7 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Deep-Learning is current.

            kandi-Quality Quality

              Deep-Learning has 0 bugs and 92 code smells.

            kandi-Security Security

              Deep-Learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Deep-Learning code analysis shows 0 unresolved vulnerabilities.
              There are 5 security hotspots that need review.

            kandi-License License

              Deep-Learning does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Deep-Learning releases are not available. You will need to build from source code and install.
              Deep-Learning has no build file. You will be need to create the build yourself to build the component from source.
              Deep-Learning saves you 1348 person hours of effort in developing the same functionality from scratch.
              It has 3021 lines of code, 315 functions and 36 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Deep-Learning and discovered the below as its top functions. This is intended to give you an instant insight into Deep-Learning implemented functionality, and help decide if they suit your requirements.
            • Train CRACK Captcha
            • Crack capture
            • Get next batch of images
            • Get training images
            • Train a model
            • Compute accuracy
            • Update the statistics
            • Get next batch of images
            • Validate the given model
            • Mask a video
            • Gradient check function
            • Calculates the gradient of the function
            • Train the given net
            • NNN layer
            • Download random captcha
            • Train and evaluate and evaluate the network
            • Calculate the derivative of the filter
            • Test the model
            • Performs the forward computation
            • Train a LinearUnit
            • Train and return a Perceptron
            • Get a logger
            • Add a video to ffmpeg
            • Convert video to mp3
            • Forward through the input array
            • Calculates the gradient for the given label
            Get all kandi verified functions for this library.

            Deep-Learning Key Features

            No Key Features are available at this moment for Deep-Learning.

            Deep-Learning Examples and Code Snippets

            Trax — Deep Learning with Clear Code and Speed-3. Walkthrough-Supervised training
            Pythondot img1Lines of Code : 58dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            from trax.supervised import training
            
            # Training task.
            train_task = training.TrainTask(
                labeled_data=train_batches_stream,
                loss_layer=tl.WeightedCategoryCrossEntropy(),
                optimizer=trax.optimizers.Adam(0.01),
                n_steps_per_checkpoint=500,  
            Trax — Deep Learning with Clear Code and Speed-3. Walkthrough-Layers
            Pythondot img2Lines of Code : 53dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            class Embedding(base.Layer):
              """Trainable layer that maps discrete tokens/IDs to vectors."""
            
              def __init__(self,
                           vocab_size,
                           d_feature,
                           kernel_initializer=init.RandomNormalInitializer(1.0)):
                """Ret  
            Keras: Deep Learning for humans-First contact with Keras
            Pythondot img3Lines of Code : 37dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            from tensorflow.keras.models import Sequential
            
            model = Sequential()
            
            from tensorflow.keras.layers import Dense
            
            model.add(Dense(units=64, activation='relu'))
            model.add(Dense(units=10, activation='softmax'))
            
            model.compile(loss='categorical_crossentr  

            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

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Deep-Learning

            You can download it from GitHub.
            You can use Deep-Learning 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

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/Jack-Cherish/Deep-Learning.git

          • CLI

            gh repo clone Jack-Cherish/Deep-Learning

          • sshUrl

            git@github.com:Jack-Cherish/Deep-Learning.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link