BaguetteAI
An AI development library written in python.
Install
pip install baguetteai
Example
from baguetteai import *
'''This is an example for a simple XOR Gate Neural Network'''
TotalL = 3 #This is the total number of layers in the Network
NumberHiddenN = 4 #This is the number of neurons that should be in each hidden layer
NumberO = 1 #This is the number of the output layer neurons
data = [([0, 0], [0]),
([0, 1], [1]),
([1, 0], [1]),
([1, 1], [0])
]
W = neural.constructW(data[0][0], TotalL, NumberHiddenN, NumberO, randomize=True)
print(W)
NN = neural.constructL(TotalL, NumberHiddenN, NumberO, data[0][0], W)
neural.training(NN, data, 10000, 0.5,1000,True)
utils.save_json(neural.extract(NN), "XOR_weights.json")
print(W)
Metadata
Release files for BaguetteAI 0.1.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| baguetteai-0.1.1.tar.gz | 40.7 kB | With provenance | Details for baguetteai-0.1.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| baguetteai-0.1.1-py3-none-any.whl | Python 3 | none | any | Details for baguetteai-0.1.1-py3-none-any.whl |
Total release size: 70.4 kB
Release files / baguetteai-0.1.1.tar.gz
| Download URL | baguetteai-0.1.1.tar.gz |
|---|---|
| Size | 40.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Download URL | baguetteai-0.1.1-py3-none-any.whl |
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| Size | 29.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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