Neural-Network

by Ajay191191 Java Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | Neural-Network Summary

Neural-Network is a Java library. Neural-Network has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Neural-Network build file is not available. You can download it from GitHub.

Neural-Network

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Security

License

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Support

Neural-Network has a low active ecosystem.

It has 1 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Neural-Network has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Neural-Network is current.

Quality

Neural-Network has 0 bugs and 0 code smells.

Security

Neural-Network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Neural-Network code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Neural-Network is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Neural-Network releases are not available. You will need to build from source code and install.

Neural-Network has no build file. You will be need to create the build yourself to build the component from source.

It has 690 lines of code, 92 functions and 11 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Neural-Network and discovered the below as its top functions. This is intended to give you an instant insight into Neural-Network implemented functionality, and help decide if they suit your requirements.

Main program
Trains the neural network
Train the network
Get training set from a string
Gets a reader
Get the index of the layer
Gets the neuron index
Sets whether or not the neural network features are features
Sets the value of the estimated output
Returns the deltas

Get all kandi verified functions for this library.

Neural-Network Key Features

No Key Features are available at this moment for Neural-Network.

Neural-Network Examples and Code Snippets

No Code Snippets are available at this moment for Neural-Network.

Community Discussions

Trending Discussions on Neural-Network

Explain (T,) tensor shape

How to override a method and chose which one to call

Why SparseCategoricalCrossentropy is not working with this machine learning model?

Cannot run docker container. Error response from daemon pull

module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'

RuntimeError: DataLoader worker exited unexpectedly

making GRU/LSTM states trainable in Tensorflow/Keras and add some random noise

LSTM/GRU setting states to random noise instead or resetting to zero

FailedPreconditionError: Table not initialized

How to reproduce Keras SimpleRNN behaviour

QUESTION

Explain (T,) tensor shape

Asked 2022-Apr-04 at 08:33

In the following d2l tutorial:

...

ANSWER

Answered 2022-Apr-04 at 08:33

Because (T) is equal to T having type of int, while torch.normal requires a tuple. (T,) is a Python way to pass one-element tuple.

Source https://stackoverflow.com/questions/71732913

QUESTION

How to override a method and chose which one to call

Asked 2022-Mar-29 at 15:19

I am trying to implement Neural Network from scratch. By default, it works as i expected, however, now i am trying to add L2 regularization to my model. To do so, I need to change three methods-

cost() #which calculate cost, cost_derivative , backward_prop # propagate networt backward

You can see below that, I have L2_regularization = None as an input to the init function

...

ANSWER

Answered 2022-Mar-22 at 20:30

General

Overall you should not create an object inside an object for the purpose of overriding a single method, instead you can just do

Source https://stackoverflow.com/questions/71576367

QUESTION

Why SparseCategoricalCrossentropy is not working with this machine learning model?

Asked 2022-Mar-29 at 07:03

I have a .csv database file which looks like this:

...

ANSWER

Answered 2022-Feb-06 at 19:25

Assuming the labels are integers, they have the wrong shape for SparseCategoricalCrossentropy. Check the docs. Try converting your y to one-hot encoded labels:

Source https://stackoverflow.com/questions/71008696

QUESTION

Cannot run docker container. Error response from daemon pull

Asked 2022-Mar-23 at 12:26

To use TensorFlow serving, I had to use docker.

I downloaded the TensorFlow image using

...

ANSWER

Answered 2022-Mar-23 at 12:26

The volume path contained spaces, putting "" around the path could solve the error

In this case, I changed the name of the directory.

Answered by @ai2ys

Source https://stackoverflow.com/questions/71491735

QUESTION

module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'

Asked 2022-Mar-17 at 10:50

I'm trying to study the neural-network-and-deep-learning (http://neuralnetworksanddeeplearning.com/chap1.html). Using the updated version for Python 3 by MichalDanielDobrzanski (https://github.com/MichalDanielDobrzanski/DeepLearningPython). Tried to run it in my command console and it gives an error below. I've tried uninstalling and reinstalling setuptools, theano, and numpy but none have worked thus far. Any help is very appreciated!!

Here's the full error log:

...

ANSWER

Answered 2022-Feb-17 at 14:12

I had the same issue and solved it downgrading numpy to version 1.20.3 by:

Source https://stackoverflow.com/questions/70839312

QUESTION

RuntimeError: DataLoader worker exited unexpectedly

Asked 2022-Feb-25 at 06:42

I am new to PyTorch and Machine Learning so I try to follow the tutorial from here: https://medium.com/@nutanbhogendrasharma/pytorch-convolutional-neural-network-with-mnist-dataset-4e8a4265e118

By copying the code step by step I got the following error for no reason. I tried the program on another computer and it gives syntax error. However, my IDE didn't warn my anything about syntax. I am really confused how I can fix the issue. Any help is appreciated.

...

ANSWER

Answered 2022-Feb-25 at 06:42

If you are working on jupyter notebook. The problem is more likely to be num_worker. You should set num_worker=0. You can find here some solutions to follow. Because unfortunately, jupyter notebook has some issues with running multiprocessing.

Source https://stackoverflow.com/questions/71261347

QUESTION

making GRU/LSTM states trainable in Tensorflow/Keras and add some random noise

Asked 2022-Feb-23 at 20:13

I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder.

...

ANSWER

Answered 2022-Feb-22 at 09:55

You could try defining a custom GRU layer with a trainable variable for the states but not sure how the performance will be:

Source https://stackoverflow.com/questions/71218323

QUESTION

LSTM/GRU setting states to random noise instead or resetting to zero

Asked 2022-Feb-22 at 08:05

I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder.

...

ANSWER

Answered 2022-Feb-22 at 08:05

You can try using tf.random.normal:

Source https://stackoverflow.com/questions/71212927

QUESTION

FailedPreconditionError: Table not initialized

Asked 2022-Feb-13 at 11:58

I am trying to create an NLP neural-network using the following code:

imports:

...

ANSWER

Answered 2022-Feb-13 at 11:58

The TextVectorization layer is a preprocessing layer that needs to be instantiated before being called. Also as the docs explain:

The vocabulary for the layer must be either supplied on construction or learned via adapt().

Another important information can be found here:

Crucially, these layers are non-trainable. Their state is not set during training; it must be set before training, either by initializing them from a precomputed constant, or by "adapting" them on data

Furthermore, it is important to note, that the TextVectorization layer uses an underlying StringLookup layer that also needs to be initialized beforehand. Otherwise, you will get the FailedPreconditionError: Table not initialized as you posted.

Source https://stackoverflow.com/questions/71099545

QUESTION

How to reproduce Keras SimpleRNN behaviour

Asked 2022-Jan-08 at 03:13

I'm trying to learn how keras.layers.SimpleRNN works by following a relatively straightforward tutorial (https://machinelearningmastery.com/understanding-simple-recurrent-neural-networks-in-keras/). However, this tutorial assumes the input is scalar, and I've been unable to scale this to higher-dimensional inputs. Here is my attempt to reproduce the behaviour of a simple RNN with 1x2 input for 3 time steps:

...

ANSWER

Answered 2022-Jan-08 at 03:13

The matrix multiplications are correct, but they need to be performed at every timestep. And if return_sequences=False Keras returns the output of the last timestep of shape ( 1 , 4 ). So, we can create a for-loop which can perform the matrix multiplications for 3 timesteps.

Note: The input shape ( 3 , 2 ) denotes ( time_steps , num_features )

Here's the code to replicate the Keras RNN model,

Source https://stackoverflow.com/questions/70629215

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

Vulnerabilities

No vulnerabilities reported

Install Neural-Network

You can download it from GitHub.
You can use Neural-Network 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 Neural-Network 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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