Neuron | Neuron - Electron , ES6 , React , PouchDB , Sass , Webpack | Frontend Framework library

 by   JamesTheHacker JavaScript Version: Current License: No License

kandi X-RAY | Neuron Summary

kandi X-RAY | Neuron Summary

Neuron is a JavaScript library typically used in User Interface, Frontend Framework, React, Webpack, Electron applications. Neuron has no bugs, it has no vulnerabilities and it has medium support. You can download it from GitHub.

Note: This project is an active work in progress. Contributions, support and feedback is very much welcomed.
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              Neuron has a medium active ecosystem.
              It has 986 star(s) with 54 fork(s). There are 26 watchers for this library.
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              It had no major release in the last 6 months.
              There are 1 open issues and 5 have been closed. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Neuron is current.

            kandi-Quality Quality

              Neuron has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

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

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              Neuron releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              Neuron saves you 18 person hours of effort in developing the same functionality from scratch.
              It has 52 lines of code, 0 functions and 9 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            Neuron Examples and Code Snippets

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            Community Discussions

            QUESTION

            How to design a model for contour recognition? In particular, how to shape the output layer?
            Asked 2021-Jun-10 at 10:11

            I want to design and train a neural network for the automatic recognition of the edges, in some microscopic images. I am using Keras for a start, I may consider PyTorch later.

            The structure of the images is rather simple, with some dark areas, and some clear areas, relatively easy to distinguish, and the task is to select the pixels of the contour between dark and clear areas. The transition between dark and clear is gradual, so my result is not a single line of edge pixels, but rather a 10 or 15 pixels wide "ribbon" at the edge.

            I have manually annotated 200-something images, so for each image I have another image, of the same size, where the pixels of the contours are black, and all the other pixels are white.

            I have seen many tutorials on how to design, compile and fit a model (a neural network), and then how to test it, using the manually annotated data.

            However, most of the tutorials work on problems of classification, where the number of neurons in the output layer is the number of categories.

            My problem is not a problem of classification, and ideally my output should be an image of the same size of the input.

            So, here is my question:

            What is the best way to design the output layer? Is a layer with a number of neurons equal to the number of pixels the best idea? Or this is a waste, and there is a more efficient way?

            Addendum

            1. The images are "easy", but it is still difficult to find the contour pixels, so I believe that it is worth using the machine learning approach.
            2. The transition between dark and clear is a little gradual, so my result is not a single line of pixels on the edge, but rather a band, a 10 or 15 wide ribbon of edge pixels. Since I am after a ribbon of pixels, my categories should be "edge" and "not-edge". If I use the categories "dark pixels" and "clear pixels", and then numerically find the pixels between the two areas I do not get the "ribbon" result, which I need.
            ...

            ANSWER

            Answered 2021-Jun-10 at 10:11

            The short answer is "yes": it is a good idea to have as many neurons in output as you have in input, i.e. to output an image with the same resolution of the input images.

            The network architecture will have an input layer with a neuron for each pixel, then typically the hidden layers will shrink to less neurons, probably with convolutional layers, and then some more layers will re-expand the number of neurons, up to the output layer, which in principle may have the same number of neurons as the input layer.

            The most common architecture in this type of problem is the U-net architecture, described in the article "U-Net: Convolutional Networks for Biomedical Image Segmentation", by Ronneberger, Fischer, and Brox, published on the open arxiv: https://arxiv.org/abs/1505.04597.

            [

            Source https://stackoverflow.com/questions/67874428

            QUESTION

            PyTorch NN does not learn or learns poorly
            Asked 2021-Jun-04 at 17:03

            I'm working with PyTorch tutorial, slightly modified to use Titanic dataset. I'm using very simple network of Linear(Dense) with ReLU... I'd like to predict survival status based on age, fare and sex for example.

            I experienced a strange behavior with a simple neural network (I'm experimenting on Google Colab). Sometimes when I execute training, the accuracy doesn't change at all. It's strange because I'm recreating the model...

            ...

            ANSWER

            Answered 2021-Jun-04 at 17:03

            As this is a classification problem, your neural network's last layer should not have a relu activation function.

            Code Snippet:

            Source https://stackoverflow.com/questions/67840919

            QUESTION

            Python-coded neural network does not learn properly
            Asked 2021-May-30 at 08:52

            My network is not trained to recognize inputs separately, it either outputs the averaged result or becomes biased to one particular output. What am I doing wrong?

            ...

            ANSWER

            Answered 2021-May-30 at 08:52

            The matrix math of backpropagation is quite tough. It is especially confusing that the length of the lists of weight matrices and deltas (actually the list of bias arrays too) should be one less than the amount of layers in a network which makes indexing confusing. Apparently, the problem was due to misindexing. Finally it works!

            Source https://stackoverflow.com/questions/67744827

            QUESTION

            How do I change activation function parametrs within Keras models
            Asked 2021-May-30 at 06:14

            I am trying to add a neuron layer to my model which has tf.keras.activations.relu() with max_value = 1 as its activation function. When I try doing it like this:

            ...

            ANSWER

            Answered 2021-May-30 at 06:06

            QUESTION

            model.summary() and plot_model() showing nothing from the built model in tensorflow.keras
            Asked 2021-May-27 at 08:40

            I am testing something which includes building a FCNN network Dynamically. Idea is to build Number of layers and it's neurons based on a given list and the dummy code is:

            ...

            ANSWER

            Answered 2021-May-27 at 05:48

            About model.summary(), don't mix tf 2.x and standalone keras at a time. If I ran you model in tf 2.x, I get the expected results.

            Source https://stackoverflow.com/questions/67715646

            QUESTION

            ValueError: Input 0 of layer max_pooling2d_2 is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: [1, None, 64, 64, 8]
            Asked 2021-May-26 at 15:11

            I am doing a CNN online course assignment which builds a convolutional model. The instructions are listed as following:

            Exercise 2 - convolutional_model

            Implement the convolutional_model function below to build the following model: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> DENSE. Use the functions above!

            Also, plug in the following parameters for all the steps:

            • Conv2D: Use 8 4 by 4 filters, stride 1, padding is "SAME"
            • ReLU
            • MaxPool2D: Use an 8 by 8 filter size and an 8 by 8 stride, padding is "SAME"
            • Conv2D: Use 16 2 by 2 filters, stride 1, padding is "SAME"
            • ReLU
            • MaxPool2D: Use a 4 by 4 filter size and a 4 by 4 stride, padding is "SAME"
            • Flatten the previous output.
            • Fully-connected (Dense) layer: Apply a fully connected layer with 6 neurons and a softmax activation.

            The code is here:

            ...

            ANSWER

            Answered 2021-May-26 at 15:11

            You (accidentally) use commas (,) at the end of each layer which is not right when you build the model. Remove the commas and it should work.

            Source https://stackoverflow.com/questions/67707495

            QUESTION

            ValueError : Weights for model sequential have not yet been created
            Asked 2021-May-26 at 13:44

            I'm testing a basic neural network model. But before go any further I've encountered this error shown in the screenshot.

            Here is my code:

            ...

            ANSWER

            Answered 2021-May-26 at 13:44

            You should not mix tf 2.x and standalone keras. You should import as follows

            Source https://stackoverflow.com/questions/67706092

            QUESTION

            Loss function for comparing two vectors for categorization
            Asked 2021-May-26 at 04:57

            I am performing a NLP task where I analyze a document and classify it into one of six categories. However, I do this operation at three different time periods. So the final output is an array of three integers (sparse), where each integer is the category 0-5. So a label looks like this: [1, 4, 5].

            I am using BERT and am trying to decide what type of head I should attach to it, as well as what type of loss function I should use. Would it make sense to use BERT's output of size 1024 and run it through a Dense layer with 18 neurons, then reshape into something of size (3,6)?

            Finally, I assume I would use Sparse Categorical Cross-Entropy as my loss function?

            ...

            ANSWER

            Answered 2021-May-24 at 15:46

            In a typical setup you take a CLS output of BERT (a vector of length 768 in case of bert-base and 1024 in case of bert-large) and add a classification head (it may be a simple Dense layer with dropout). In this case the inputs are word tokens and the output of the classification head is a vector of logits for each class, and usually a regular Cross-Entropy loss function is used. Then you apply softmax to it and get probability-like scores for each class, or if you apply argmax you will get the winning class. So the result might be either vector of classification scores [1x6] or the dominant class index (an integer).

            Image taken from d2l.ai

            You can simply concatenate 3 such networks (for each time period) to get the desired result.

            Obviously, I have described only one possible solution. But as it is usually provide good results I suggest you try it before moving over to more complex ones.

            Finally, Sparse Categorical Cross-Entropy loss is used when output is sparse (say [4]) and regular Categorical Cross-Entropy loss is used when output is one-hot encoded (say [0 0 0 0 1 0]). Otherwise they are absolutely the same.

            Source https://stackoverflow.com/questions/67337774

            QUESTION

            How to find the predicted output of a classification neural network in python?
            Asked 2021-May-22 at 16:29

            I a newbie to python and learning neural networks. I have a trained 3 layer feed forward neural network with 2 neurons in the hidden layer and 3 in the output layer. I am wondering that how to calculate the output layer values/ predicted output

            I have weights and biases extracted from the network and activation values calculated of the hidden layer. I just want to confirm that how can I use softmax function to calculate the out put of the output layer neurons?

            My implementation is as follows:

            ...

            ANSWER

            Answered 2021-May-22 at 16:29

            Your output would be the matrix multiplication of weights_from_hiddenLayer_to_OutputLayer and the previous activations. You can then pass it through the softmax function to get a probability distribution and use argmax as you guessed to get the corresponding class.

            Source https://stackoverflow.com/questions/67650412

            QUESTION

            How to use a learned embedding layer from a Keras ANN as an input feature in an XGBoost model?
            Asked 2021-May-21 at 18:33

            I am attempting to reduce the dimensionality of a categorical feature by extracting an embedding layer from a neural net and using it as an input feature in a separate XGBoost model.

            An embedding layer has the dimensions (nr. unique categories + 1, chosen output size). How can it be concatenated to the continuous variables in the original training data with the dimensions (nr. observations, nr. features)?

            Below is a reproducible example of regression with a neural net, in which a categorical feature is encoded as a learned embedding layer. The example is closely adapted from: http://machinelearningmechanic.com/keras/2018/03/09/keras-regression-with-categorical-variable-embeddings-md.html#Define-the-input-layers

            At the end I have printed the embedding layer and its shape. How can this layer be merged with the continuous features in the original training data (X_train_continuous)? If the number of rows were equal to the number of categories and if we knew the order in which categories are represented in the embedding layer, the embedding array could perhaps be joined to the training observations on category, but instead the number of rows equals the number of categories + 1 (in the code: len(values) + 1).

            ...

            ANSWER

            Answered 2021-May-19 at 20:56

            One thing you can do is to run your 'pretrained' model with each layer having a unique name and save it

            Then, create your new model, with the same named layers you want to keep, and use Model.load_weights(file_path, by_name=True)

            This will let you keep all of the layers that you want and let you change everything afterwards

            Source https://stackoverflow.com/questions/67610396

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

            Vulnerabilities

            No vulnerabilities reported

            Install Neuron

            If you’re running Windows remove the following line from the package.json file and use browser adapters instead of Node’s levelDB adapter ([See here](https://github.com/nolanlawson/hello-electron-with-pouchdb#browser-vs-node) for more info):.
            and set the browser adapter in app.js like so: const localDB = new PouchDB('app', { adapter: 'websql' })

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