simple-neural-network | neural network written in Python | Machine Learning library

by miloharper Python Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | simple-neural-network Summary

simple-neural-network is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Neural Network applications. simple-neural-network has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However simple-neural-network build file is not available. You can download it from GitHub.

A neural network written in Python, consisting of a single neuron that uses gradient descent to learn.

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simple-neural-network has a low active ecosystem.

It has 340 star(s) with 174 fork(s). There are 28 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 1 have been closed. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of simple-neural-network is current.

Quality

simple-neural-network has 0 bugs and 0 code smells.

Security

simple-neural-network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

simple-neural-network code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

simple-neural-network is licensed under the MIT License. This license is Permissive.

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

Reuse

simple-neural-network releases are not available. You will need to build from source code and install.

simple-neural-network has no build file. You will be need to create the build yourself to build the component from source.

simple-neural-network saves you 13 person hours of effort in developing the same functionality from scratch.

It has 37 lines of code, 5 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Train the neural network .
Initialize the model .
Simify the input .
Sigmoid function
Derivative of sigmoid derivative .

Get all kandi verified functions for this library.

simple-neural-network Key Features

No Key Features are available at this moment for simple-neural-network.

simple-neural-network Examples and Code Snippets

First contact with Keras

pypi

Lines of Code : 37

License : No License

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from tensorflow.keras.models import Sequential

model = Sequential()


from tensorflow.keras.layers import Dense

model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=10, activation='softmax'))


model.compile(loss='categorical_crossen

Auto-vectorization with

pypi

Lines of Code : 14

License : No License

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def predict(params, input_vec):
  assert input_vec.ndim == 1
  activations = input_vec
  for W, b in params:
    outputs = jnp.dot(W, activations) + b  # `activations` on the right-hand side!
    activations = jnp.tanh(outputs)        # inputs to the

Community Discussions

Trending Discussions on simple-neural-network

Endless Confusion Deriving the Sigmoid Function

How to test a trained Neural network in python?

Correct practice and approach for reporting the training and generalization performance

QUESTION

Endless Confusion Deriving the Sigmoid Function

Asked 2022-Jan-02 at 18:38

def sigmoid(x):
  return 1 / (1 + np.exp(-x))

def sigmoid_derivative(x):
  return x * (1 - x)

...

ANSWER

Answered 2022-Jan-02 at 18:38

As it is known from calculus, the statement

slope * x = y

is true only for a linear function, which sigmoid obviously isn't, especially around 2. The only thing you should expect is that

sigmoid_derivative(sigmoid(x0)) * (x1 - x0) + sigmoid(x0) = sigmoid(x1) + eps

where eps is a small error such that the smaller is |x1 - x0|, the smaller is |eps|.

You can easily check it yourself.

Source https://stackoverflow.com/questions/70558435

QUESTION

How to test a trained Neural network in python?

Asked 2020-Aug-25 at 11:10

I have trained a simple NN by modifying the following code

https://www.kaggle.com/ancientaxe/simple-neural-network-from-scratch-in-python

I would now like to test it on another sample dataset. how should i proceed with it ?

...

ANSWER

Answered 2020-Aug-25 at 11:10

I see you use a model from scratch. In this case, you should run this code, as indicated in the notebook, after setting your X and y for your new test set. For more information, see the the notebook as I did not put here everything:

Source https://stackoverflow.com/questions/63577390

QUESTION

Correct practice and approach for reporting the training and generalization performance

Asked 2020-Jan-27 at 08:33

I am trying to learn the correct procedure for training a neural network for classification. Many tutorials are there but they never explain how to report for the generalization performance. Can somebody please tell me if the following is the correct method or not. I am using first 100 examples from the fisheriris data set that has labels 1,2 and call them as X and Y respectively. Then I split X into trainData and Xtest with a 90/10 split ratio. Using trainData I trained the NN model. Now the NN internally further splits trainData into tr,val,test subsets. My confusion is which one is usually used for generalization purpose when reporting the performance of the model to unseen data in conferences/Journals? The dataset can be found in the link: https://www.mathworks.com/matlabcentral/fileexchange/71468-simple-neural-networks-with-k-fold-cross-validation-manner

...

ANSWER

Answered 2020-Jan-27 at 08:33

There are a few issues with the code. Let's deal with them before answering your question. First, you set a threshold of 0.5 for making decisions (Yhat_train = (train_predict >= 0.5);) while all points of your net prediction are above 0.5. This means you only get zeros in your confusion matrices. You can plot the scores to choose a better threshold:

Source https://stackoverflow.com/questions/59905814

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

Vulnerabilities

No vulnerabilities reported

Install simple-neural-network

You can download it from GitHub.
You can use simple-neural-network like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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