DeepLearning | Deep Learning

 by   yusugomori Java Version: Current License: MIT

kandi X-RAY | DeepLearning Summary

kandi X-RAY | DeepLearning Summary

DeepLearning is a Java library typically used in Institutions, Learning, Education applications. DeepLearning has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However DeepLearning build file is not available. You can download it from GitHub.

Deep Learning (Python, C, C++, Java, Scala, Go)
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            kandi-support Support

              DeepLearning has a medium active ecosystem.
              It has 2992 star(s) with 1389 fork(s). There are 416 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 22 open issues and 2 have been closed. On average issues are closed in 228 days. There are 12 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of DeepLearning is current.

            kandi-Quality Quality

              DeepLearning has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              DeepLearning is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              DeepLearning releases are not available. You will need to build from source code and install.
              DeepLearning has no build file. You will be need to create the build yourself to build the component from source.
              DeepLearning saves you 1754 person hours of effort in developing the same functionality from scratch.
              It has 3882 lines of code, 232 functions and 38 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed DeepLearning and discovered the below as its top functions. This is intended to give you an instant insight into DeepLearning implemented functionality, and help decide if they suit your requirements.
            • Trains the input model
            • Make a prediction on the input data
            • Reconstructs the sigmoid
            • Calculate softmax function
            • Make a logistic prediction
            • Make a prediction on the data
            • Generate a binomial from the given rng
            • Compute the hidden values
            • Converts the input into an image
            • Predict the prediction
            • Calculates the output
            • Calculates the Sigmoid
            • Computes thepropagation of the Sigmoid
            • Estimate the sigmoid activation
            • Create a binomial mask for each binomial
            • Parses the input into tilde_x
            • Samples the mean and variance
            • Pretens the layers with the given p_dropout parameter
            • Trains the hidden layers
            • Computes the backward layer
            • Retrain a training set
            • Test the sda
            • Trains training data
            • Performs a pretrained test
            • Calculate the dropout
            • Retrain a set of data
            • Train RBM
            • Test model for training data
            • Trains the neural network
            • Set the training model
            • Test algorithm
            • Train MLP implementation
            • Finishes training iterations
            • Performs a contrastive divergence
            • Trains the model for classification loss
            • B binomial sample function
            Get all kandi verified functions for this library.

            DeepLearning Key Features

            No Key Features are available at this moment for DeepLearning.

            DeepLearning Examples and Code Snippets

            Enable mixed precision graph rewrite .
            pythondot img1Lines of Code : 130dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def enable_mixed_precision_graph_rewrite_v1(opt, loss_scale='dynamic'):
              """Enable mixed precision via a graph rewrite.
            
              Mixed precision is the use of both float32 and float16 data types when
              training a model to improve performance. This is achi  

            Community Discussions

            QUESTION

            The repository 'https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64 Release' does not have a Release file
            Asked 2022-Mar-31 at 13:53

            I'm trying to use a Google Container Registry public image as a base image for a Docker build. I'm currently using gcr.io/deeplearning-platform-release/tf-gpu.2-8, but the same issue occurs with gcr.io/deeplearning-platform-release/tf-gpu.2-6 and gcr.io/deeplearning-platform-release/base-cu113; see this Google page for reference.

            If I just have the following 2 lines in my Dockerfile it crashes on the 2nd line:

            ...

            ANSWER

            Answered 2022-Mar-31 at 13:53

            The site developer.download.nvidia.com must have been down. Trying again this morning works.

            Source https://stackoverflow.com/questions/71684989

            QUESTION

            Tensorboard Profiler: Failed to load libcupti (is it installed and accessible?)
            Asked 2022-Mar-21 at 18:36

            I'm trying to analyze my tensorflow application. The training runs well, but I get Failed to load libcupti (is it installed and accessible?) if I open the Profile-Tab in Tensorboard.

            My configuration is:

            • Windows 10
            • Python 3.9.7
            • Tensorflow 2.6.0
            • CUDA Toolkit 11.2
            • cuDNN 8.1.1 (installed as here by copying files as described)
            • Visual Studio Professional 2019

            CUDA_PATH is C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2

            My Path-Variable contains:

            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin
            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp
            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64
            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include
            • C:\Program Files\NVIDIA Corporation\Nsight Systems 2020.4.3\target-windows-x64

            conda list (only relevant packages):

            ...

            ANSWER

            Answered 2022-Mar-21 at 18:36

            Hidden in the log output of jupyter I found an this error message: Could not load dynamic library 'cupti64_113.dll': dlerror: cupti64_113.dll not found

            With this error message and that hint I was able to solve the problem: I copied cupti64_2020.3.0.dll in C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64 and renamed it to cupti64_113.dll and now the profiler works.

            Source https://stackoverflow.com/questions/71023977

            QUESTION

            Easy to detect shapes/patterns to put on corners of a form
            Asked 2022-Mar-10 at 11:22

            I am trying to create a form which will be filled and photographed later on. An issue that I am facing is that of alignment. I came across some deep learning solutions which detect the corners of form. But this is a lot of times inaccurate in my use case where the sheet of paper is folded-reopened/crumpled. I also don't have a lot of flexibility/hard-coding options in the deeplearning process.

            Are there any patterns which OpenCV can detect with ~100% accuracy no matter the orientation of the pattern? I will be putting different patterns on 4 corners of the sheet. I am thinking of using the inbuilt template matching function or other pattern recognition algorithms. There are some common patters like a big '+' sign or a star etc that I am trying to avoid. I also tried putting barcodes on the corners because they are also detected fairly easily(Not concerned with the contents of the barcode only their relative positioning). But depending on the quality of image the barcode isn't always detected.

            ...

            ANSWER

            Answered 2022-Mar-10 at 11:22

            ArUco markers sound like the best option for you, they can easily be implemented in OpenCV.

            Aruco example and documentation:https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html

            Python example: https://pyimagesearch.com/2020/12/21/detecting-aruco-markers-with-opencv-and-python/

            Source https://stackoverflow.com/questions/71421508

            QUESTION

            GCP Vertex AI Training: Auto-packaged Custom Training Job Yields Huge Docker Image
            Asked 2022-Mar-01 at 08:34

            I am trying to run a Custom Training Job in Google Cloud Platform's Vertex AI Training service.

            The job is based on a tutorial from Google that fine-tunes a pre-trained BERT model (from HuggingFace).

            When I use the gcloud CLI tool to auto-package my training code into a Docker image and deploy it to the Vertex AI Training service like so:

            ...

            ANSWER

            Answered 2022-Mar-01 at 08:34

            The image size shown in the UI is the virtual size of the image. It is the compressed total image size that will be downloaded over the network. Once the image is pulled, it will be extracted and the resulting size will be bigger. In this case, the PyTorch image's virtual size is 6.8 GB while the actual size is 17.9 GB.

            Also, when a docker push command is executed, the progress bars show the uncompressed size. The actual amount of data that’s pushed will be compressed before sending, so the uploaded size will not be reflected by the progress bar.

            To cut down the size of the docker image, custom containers can be used. Here, only the necessary components can be configured which would result in a smaller docker image. More information on custom containers here.

            Source https://stackoverflow.com/questions/71284125

            QUESTION

            Expected shape=(None, 30), found shape=(None, 4) with sequential model
            Asked 2022-Jan-22 at 18:07

            My dataset is formed by 32 columns, but after seeking for the important features I found that 4 of them are the most important ones so I wanted to work just on them but this error faced me:

            This is my code:

            ...

            ANSWER

            Answered 2022-Jan-22 at 18:07

            You have this line of code

            Source https://stackoverflow.com/questions/70815411

            QUESTION

            Install tensorrt with custom plugins
            Asked 2022-Jan-18 at 13:25

            I'm able to install the desired version of TensorRT from official nvidia guide (https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#maclearn-net-repo-install)

            ...

            ANSWER

            Answered 2022-Jan-18 at 13:25

            It's quite easy to "install" custom plugin if you registered it. So the steps are the following:

            1. Install tensorRT

            Source https://stackoverflow.com/questions/70533826

            QUESTION

            Flattening image matrixes for Deep Learning
            Asked 2022-Jan-09 at 14:08

            I have a question about flattening image matrixes in this case (64 x 64 pix x 3) to a vector (12288 x 1).

            I understand that each image pixel is in a (64 X 64) matrix, and if I'm right, each element of this matrix is a vector of length 3, holding R,G,B data for that single pixel. So the first following row is R, G, B values for the top-left pixel:

            ...

            ANSWER

            Answered 2022-Jan-07 at 17:47

            I think that it depends on your model design. If you design your model inputs with three arrays for three channels (R, G, B), you can try my way below. We need to separate it first and reshape it later.

            Source https://stackoverflow.com/questions/70625122

            QUESTION

            H2O Stacked Ensemble Prediction ArrayIndexOutOfBoundsException
            Asked 2022-Jan-06 at 14:54

            Using the h2o package for R, I created a set of base models using AutoML with StackedEnsemble's disabled. Thus, the set of models only contains the base models that AutoML generates by default (GLM, GBM, XGBoost, DeepLearning, and DRF). Using these base models I was able to successfully train a default stacked ensemble manually using the h2o.stackedEnsemble function (i.e., a GLM with default params). I exported the model as a MOJO, shutdown the H2O cluster, restarted R, initialized a new H2O cluster, imported the stacked ensemble MOJO, and successfully generated predictions on a new validation set.

            So far so good.

            Next, I did the exact same thing following the exact same process, but this time I made one change: I trained the stacked ensemble with all pairwise interactions between the base models. The interactions were created automatically by feeding a list of the base model Ids to the interaction metalearner_parameter. The model appeared to train without issue and (as I described above) was able to export it as a MOJO, restart the h2o cluster, restart R, and import the MOJO. However, when I attempt to generate predictions on the same validation set I used above I get the following error:

            ...

            ANSWER

            Answered 2022-Jan-06 at 14:54

            Unfortunately, H2O-3 doesn't currently support exporting GLM with interactions as MOJO. There's a bug that allows the GLM to be exported with interactions but the MOJO doesn't work correctly - the interactions are replaced by missing values. This should be fixed in the next release (3.36.0.2) - it will not allow to export that MOJO in the first place.

            There's not much other than writing the stacked ensemble in R (base model predictions preprocessing (e.g., interaction creation) and then feeding it to the h2o.glm) that you can do. There is now an unmaintained package h2oEnsemble that might be helpful for that. You can also use another metalearner model that is more flexible, e.g., GBM.

            Source https://stackoverflow.com/questions/70597370

            QUESTION

            Stop TensorFlow from printing warning message
            Asked 2021-Dec-09 at 15:47

            I am working on a Kaggle notebook and whenever I run a cell that references the TensorFlow module at all, it prints out a huge warning about some sort of settings but still works. I looked up how to suppress warnings from TensorFlow, and everything I found said to do the following:

            ...

            ANSWER

            Answered 2021-Dec-09 at 15:47

            So I managed to fix the problem with the following line:

            Source https://stackoverflow.com/questions/70250304

            QUESTION

            How to change batch size in VGG16?
            Asked 2021-Dec-04 at 00:39

            How do I change the batch size in VGG16? I'm trying to address an issue of exceeding memory constraints by 10% by doing this.

            Error:

            ...

            ANSWER

            Answered 2021-Dec-03 at 22:24

            you are already using batch_size = 1.

            1. check if you are using gpu by checking the logs when you are importing tensorflow.
            2. try to resize the image before predicting with tf.image.resize(image, [small_height,small_width,N_channels])

            Source https://stackoverflow.com/questions/70220813

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

            Vulnerabilities

            No vulnerabilities reported

            Install DeepLearning

            You can download it from GitHub.
            You can use DeepLearning like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the DeepLearning component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

            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 .
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