trainset | lightweight web application for brushing labels onto time | Data Labeling library

 by   Geocene JavaScript Version: Current License: MIT

kandi X-RAY | trainset Summary

kandi X-RAY | trainset Summary

trainset is a JavaScript library typically used in Artificial Intelligence, Data Labeling, Deep Learning applications. trainset has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

TRAINSET is a graphical tool for labeling time series data. You can upload multiple series and apply one or many labels. In the GIF below, series_a is being labled with bar and biz labels while series_b is serving as a reference.
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              trainset has a low active ecosystem.
              It has 109 star(s) with 27 fork(s). There are 8 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 16 open issues and 64 have been closed. On average issues are closed in 138 days. There are 8 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of trainset is current.

            kandi-Quality Quality

              trainset has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              trainset 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

              trainset releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

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            trainset Key Features

            No Key Features are available at this moment for trainset.

            trainset Examples and Code Snippets

            No Code Snippets are available at this moment for trainset.

            Community Discussions

            QUESTION

            How to use MSELoss function for Fashion_MNIST in pytorch?
            Asked 2021-May-30 at 12:28

            I want to get through Fashion_Mnist data, I would like to see the output gradient which might be mean squared sum between first and second layer

            My code first below

            ...

            ANSWER

            Answered 2021-May-30 at 12:28

            The error is caused by the number of samples in the dataset and the batch size.

            In more detail, the training MNIST dataset includes 60,000 samples, your current batch_size is 128 and you will need 60000/128=468.75 loops to finish training on one epoch. So the problem comes from here, for 468 loops, your data will have 128 samples but the last loop just contains 60000 - 468*128 = 96 samples.

            To solve this problem, I think you need to find the suitable batch_size and the number of neural in your model as well.

            I think it should work for computing loss

            Source https://stackoverflow.com/questions/67760590

            QUESTION

            How to check the output gradient by each layer in pytorch in my code?
            Asked 2021-May-29 at 11:31

            I am working on the pytorch to learn.

            And There is a question how to check the output gradient by each layer in my code.

            My code is below

            ...

            ANSWER

            Answered 2021-May-29 at 11:31

            Well, this is a good question if you need to know the inner computation within your model. Let me explain to you!

            So firstly when you print the model variable you'll get this output:

            Source https://stackoverflow.com/questions/67722328

            QUESTION

            Hot to parallelize for loof in for loop? Python
            Asked 2021-May-17 at 09:37

            I am trying to parallelize this equation:

            ...

            ANSWER

            Answered 2021-May-17 at 09:37

            The expensive operation here seems to be the code following the computation of the cosine similarity. You may want to use heap data structure to get the top ten.

            Here is an attempt to improve the performance (while ensuring low space complexity) by parallelizing cosine similarity computation. Reference: https://docs.python.org/3/library/multiprocessing.html

            Source https://stackoverflow.com/questions/67566417

            QUESTION

            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:

            ...

            ANSWER

            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:

            Source https://stackoverflow.com/questions/67519746

            QUESTION

            pytorch change input image size
            Asked 2021-May-02 at 12:34

            I am new to pytorch and I am following a tutorial but when i try to modify the code to use 64x64x3 images instead of 32x32x3 images, i get a buch of errors. Here is the code from the tutorial:

            ...

            ANSWER

            Answered 2021-May-02 at 11:41

            I think this should work because after performing 2nd Pooling operation the output feature map is coming N x C x 13 x 13

            self.fc1 = nn.Linear(16 * 13 * 13, 120)

            x = x.view(-1, 16 * 13 * 13)

            Source https://stackoverflow.com/questions/67355392

            QUESTION

            The .data attribute for a dataset in torchvision.datasets doesn't work for ImageFolder?
            Asked 2021-Apr-20 at 19:56

            I can access the training data set of an MNIST object like so:

            ...

            ANSWER

            Answered 2021-Apr-19 at 20:36

            There isn't any.

            Nowadays, everyone assumes that MNIST fits in memory, thus it is preloaded to the data attribute. However, this is usually not possible for ImageDatasets. Therefore, the images are loaded on-the-fly which means, no data attribute for them. You can access the image paths and labels using the self.imgs or self.samples.

            Source https://stackoverflow.com/questions/67166330

            QUESTION

            [python]Colab crashes for unknown reason while training gan(pytorch)
            Asked 2021-Apr-15 at 02:34

            I tried to train gan on some monkey pics but it crashes colab for unknown reason if try to train it. I am using 1370 128*128 monkey images.

            I have no idea where the issue might be, please respond

            btw the runtime is gpu, so the problem doesn't linked to that

            ...

            ANSWER

            Answered 2021-Apr-15 at 02:34

            I've debugged your code a bit, and found that the crash is happening at line:

            Source https://stackoverflow.com/questions/67096033

            QUESTION

            ValueError with Shapes using Bidirectional LSTM
            Asked 2021-Apr-01 at 16:13

            I am trying to implement a Bidirectional LSTM for a sequence-to-sequence model. I have already one-hot-encoded my sequences with 12 total features. The input is 11 steps while the output is 23 steps. First, I coded this LSTM implementation that works with the first LSTM as the encoder and the second as the decoder.

            ...

            ANSWER

            Answered 2021-Apr-01 at 16:13

            Simply setting return_sequences=False in your first bidirectional LSTM and adding as before RepeatVector(23) works fine

            Source https://stackoverflow.com/questions/66907936

            QUESTION

            AttributeError: 'numpy.ndarray' object has no attribute 'score' error
            Asked 2021-Feb-22 at 15:16

            I have tried to look for a problem but there is nothing Im seeing wrong here. What could it be? This is for trying binary classification in SVM for the fashion MNIST data set but only classifying 5 and 7.

            ...

            ANSWER

            Answered 2021-Feb-22 at 15:16

            ypred is an array of predicted class labels, so the exception makes sense.

            What you should do is use the classifier’s score method:

            Source https://stackoverflow.com/questions/66318181

            QUESTION

            Display number of images per class using Pytorch
            Asked 2021-Feb-05 at 12:06

            I am using Pytorch with FashionMNIST dataset I would like to display 8 image sample from each of the 10 classes. However, I did not figure how to split the training test into train_labels since I need to loop on the labels(class) and print 8 of each class. any idea how I can achieve this?

            ...

            ANSWER

            Answered 2021-Jan-01 at 15:18

            If I understand you correctly you want to group your dataset by labels then display them.

            You can start by constructing a dictionnary to store examples by label:

            Source https://stackoverflow.com/questions/65528954

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

            Vulnerabilities

            No vulnerabilities reported

            Install trainset

            For a detailed explanation on how things work, check out the guide and docs for vue-loader.

            Support

            The development of TRAINSET was funded by the NIH Clean Cooking Implementation Science Network with funding from the NIH Common Fund for Global Health. The development team and cookstove community are grateful for this support.
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          • HTTPS

            https://github.com/Geocene/trainset.git

          • CLI

            gh repo clone Geocene/trainset

          • sshUrl

            git@github.com:Geocene/trainset.git

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