AlexNet | implement AlexNet with C / convolutional nerual network | Machine Learning library

 by   Dynmi C Version: Current License: MIT

kandi X-RAY | AlexNet Summary

kandi X-RAY | AlexNet Summary

AlexNet is a C library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. AlexNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

! ! ! It needs an evaluation on ImageNet. This project is an unofficial implementation of AlexNet, using C Program Language Without Any 3rd Library, according to the paper "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky,et al.

            kandi-support Support

              AlexNet has a low active ecosystem.
              It has 153 star(s) with 12 fork(s). There are 4 watchers for this library.
              It had no major release in the last 6 months.
              There are 3 open issues and 1 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of AlexNet is current.

            kandi-Quality Quality

              AlexNet has no bugs reported.

            kandi-Security Security

              AlexNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              AlexNet 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

              AlexNet releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of AlexNet
            Get all kandi verified functions for this library.

            AlexNet Key Features

            No Key Features are available at this moment for AlexNet.

            AlexNet Examples and Code Snippets

            No Code Snippets are available at this moment for AlexNet.

            Community Discussions


            How to calculate the f1-score?
            Asked 2021-Jun-14 at 07:07

            I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

            My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)



            Answered 2021-Jun-13 at 15:17

            You can use sklearn to calculate f1_score



            GoogleNet Implantation ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size
            Asked 2021-Jun-08 at 08:22

            I am trying to implement GoogleNet inception network to classify images for classification project that I am working on, I used the same code before but with AlexNet network and the training was fine, but once I changed the network to GoogleNet architecture the code kept throwing the following error:



            Answered 2021-Jun-08 at 08:22

            GoogleNet is different than Alexnet, in GoogleNet your model has 3 outputs, 1 main and 2 auxiliary outputs connected in intermediate layers during training:



            Pytorch transfer learning error: The size of tensor a (16) must match the size of tensor b (128) at non-singleton dimension 2
            Asked 2021-May-13 at 16:00

            Currently, I'm working on an image motion deblurring problem with PyTorch. I have two kinds of images: Blurry images (variable = blur_image) that are the input image and the sharp version of the same images (variable = shar_image), which should be the output. Now I wanted to try out transfer learning, but I can't get it to work.

            Here is the code for my dataloaders:



            Answered 2021-May-13 at 16:00

            Here your you can't use alexnet for this task. becouse output from your model and sharp_image should be shame. because convnet encode your image as enbeddings you and fully connected layers can not convert these images to its normal size you can not use fully connected layers for decoding, for obtain the same size you need to use ConvTranspose2d() for this task.

            your encoder should be:



            difference between initializing bias by nn.init.constant and (bias=1) in pytorch
            Asked 2021-May-06 at 09:05

            I'm making a code for AlexNet and i'm confused with how to initialize the weights

            what is the difference between:



            Answered 2021-May-06 at 06:45

            The method nn.init.constant_ receives a parameter to initialize and a constant value to initialize it with. In your case, you use it to initialize the bias parameter of a convolution layer with the value 0.

            The method nn.Linear the bias parameter is a boolean stating weather you want the layer to have a bias or not. By setting it to be 0, you're actually creating a linear layer with no bias at all.

            A good practice is to start with PyTorch's default initialization techniques for each layer. This is done by just creating the layers, pytorch initializes them implicitly. In more advanced development stages you can also explicitly change it if necessary.

            For more info see the official documentation of nn.Linear and nn.Conv2d.



            How can I train my neural network model with tow or more .npy files simultaneously?
            Asked 2021-Apr-19 at 07:04

            I am a beginner of AlexNet neural network.I'm making an autopilot for a simple racing game.Here is the code I used to train my model with a file named training_data_n1.npy. I currently have three .npy files, so how can I modify my code so that I can train my model with three or more .npy files simultaneously (without merging these .npy files into one .npy file).I would appreciate it if you could provide the code. :)



            Answered 2021-Apr-19 at 07:04

            Supposing that they are all named 'training_data_nXX.npy' you could try something like this:



            Combine CNN with LSTM
            Asked 2021-Apr-11 at 13:45

            I'm looking to implement a RNN along with a CNN in order to make a prediction based on two images instead of one alone with a CNN. I'm trying to modify the alexnet model code:



            Answered 2021-Apr-11 at 13:45

            If I understood you correctly, you need to do the following. Let model be the network taking series of images as input and returning the predictions. Using finctional API, this schematically looks as follows:



            Python TypeError: 'Tensor' object is not callable when sorting dictionary
            Asked 2021-Apr-09 at 06:50

            Here is my code. The packages imported are not shown. I am trying to feed the CIFAR-10 test data into alexnet. The dictionary at the end needs to be sorted so I can find the most common classification. Please help, I have tried everything!




            Answered 2021-Apr-09 at 06:50

            This line is the problem



            Backbone network in Object detection
            Asked 2021-Apr-02 at 11:06

            I am trying to understand the training process of a object deetaction deeplearng algorithm and I am having some problems understanding how the backbone network (the network that performs feature extraction) is trained.

            I understand that it is common to use CNNs like AlexNet, VGGNet, and ResNet but I don't understand if these networks are pre-trained or not. If they are not trained what does the training consist of?



            Answered 2021-Apr-02 at 11:06

            We directly use a pre-trained VGGNet or ResNet backbone. Although the backbone is pre-trained for classification task, the hidden layers learn features which can be used for object detection also. Initial layers will learn low level features such as lines, dots, curves etc. Next layer will learn learn high-level features that are built on top of low-level features to detect objects and larger shapes in the image.

            Then the last layers are modified to output the object detection coordinates rather than class.

            There are object detection specific backbones too. Check these papers:

            Lastly, the pretrained weights will be useful only if you are using them for similar images. E.g.: weights trained on Image-net will be useless on ultrasound medical image data. In this case we would rather train from scratch.



            Running GluonCV object detection model on Android
            Asked 2021-Mar-03 at 20:01

            I need to run a custom GluonCV object detection module on Android.

            I already fine-tuned the model (ssd_512_mobilenet1.0_custom) on a custom dataset, I tried running inference with it (loading the .params file produced during the training) and everything works perfectly on my computer. Now, I need to export this to Android.

            I was referring to this answer to figure out the procedure, there are 3 suggested options:

            1. You can use ONNX to convert models to other runtimes, for example [...] NNAPI for Android
            2. You can use TVM
            3. You can use SageMaker Neo + DLR runtime [...]

            Regarding the first one, I converted my model to ONNX. However, in order to use it with NNAPI, it is necessary to convert it to daq. In the repository, they provide a precomplied AppImage of onnx2daq to make the conversion, but the script returns an error. I checked the issues section, and they report that "It actually fails for all onnx object detection models".

            Then, I gave a try to DLR, since it's suggested to be the easiest way. As I understand, in order to use my custom model with DLR, I would first need to compile it with TVM (which also covers the second point mentioned in the linked post). In the repo, they provide a Docker image with some conversion scripts for different frameworks. I modified the '' script, and now I have:



            Answered 2021-Mar-03 at 10:33

            The error message is self-explanatory - there is no model "ssd_512_mobilenet1.0_custom" supported by You are confusing GluonCV's get_model with MXNet Gluon's get_model.




            specializing configuration with files instead of variables in Hydra config
            Asked 2021-Feb-22 at 09:44

            I'd like to use specialized configuration as per Hydra documentation in Common Patterns -> Specializing Configuration. The difference is that my specialized configuration is in a file, not just one variable. In the example below I want to choose transform based on the model and the dataset. The configs for different transforms are in files. This would work if I specified all the transform configuration in dataset_model/cifar10_alexnet.yaml file, but that would defeat the purpose because I can't reuse the transform config in this case. Alsewhere in Hydra if you specify the name of the file it would automatically pick up the config in that file, but it does not seem to work in the specialized configuration.

            I've modified the example in documentation as follows:




            Answered 2021-Feb-21 at 18:24

            Your config is trying to merge the string "resize" into a dictionary like:


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


            No vulnerabilities reported

            Install AlexNet

            You can download it from GitHub.


            + Linux + Windows with MinGW-w64(build pass but can not load images yet).
            Find more information at:

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

            Find more libraries
          • HTTPS


          • CLI

            gh repo clone Dynmi/AlexNet

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link