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Neural network written in Python (NumPy)

This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.

Visit the project page or Read the documentation.

The code has been tested.

Implemented learning algorithms:

  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize

Installation

pip install nimblenet

Requirements

  • Python
  • NumPy
  • Optionally: SciPy

This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.

Features:

  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions

Example Usage

fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)

About

This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.

Resources

Readme
BSD-2-Clause license
Activity

Stars

298 stars

Watchers

19 watching

Forks

95 forks
Report repository

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Used by

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
Neural network written in Python (NumPy)
This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.
Visit the project page or Read the documentation.
The code has been tested.
Implemented learning algorithms:
  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize
Installation
pip install nimblenet

Requirements
  • Python
  • NumPy
  • Optionally: SciPy
This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.
Features:
  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions
Example Usage
fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)
About
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
Resources
Readme
BSD-2-Clause license
Activity
Stars
298 stars
Watchers
19 watching
Forks
95 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Neural network written in Python (NumPy)
This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.
Visit the project page or Read the documentation.
The code has been tested.
Implemented learning algorithms:
  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize
Installation
pip install nimblenet

Requirements
  • Python
  • NumPy
  • Optionally: SciPy
This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.
Features:
  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions
Example Usage
fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)
About
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
Resources
Readme
BSD-2-Clause license
Activity
Stars
298 stars
Watchers
19 watching
Forks
95 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Neural network written in Python (NumPy)
This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.
Visit the project page or Read the documentation.
The code has been tested.
Implemented learning algorithms:
  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize
Installation
pip install nimblenet

Requirements
  • Python
  • NumPy
  • Optionally: SciPy
This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.
Features:
  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions
Example Usage
fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)
About
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
Resources
Readme
BSD-2-Clause license
Activity
Stars
298 stars
Watchers
19 watching
Forks
95 forks
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
Neural network written in Python (NumPy)
This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.
Visit the project page or Read the documentation.
The code has been tested.
Implemented learning algorithms:
  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize
Installation
pip install nimblenet

Requirements
  • Python
  • NumPy
  • Optionally: SciPy
This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.
Features:
  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions
Example Usage
fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)
About
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
Resources
Readme
BSD-2-Clause license
Activity
Stars
298 stars
Watchers
19 watching
Forks
95 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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setup.py
setup.py
Repository files navigation
Neural network written in Python (NumPy)
This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.
Visit the project page or Read the documentation.
The code has been tested.
Implemented learning algorithms:
  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize
Installation
pip install nimblenet

Requirements
  • Python
  • NumPy
  • Optionally: SciPy
This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.
Features:
  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions
Example Usage
fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)
About
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
Resources
Readme
BSD-2-Clause license
Activity
Stars
298 stars
Watchers
19 watching
Forks
95 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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setup.py
setup.py
Repository files navigation
Neural network written in Python (NumPy)
This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.
Visit the project page or Read the documentation.
The code has been tested.
Implemented learning algorithms:
  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize
Installation
pip install nimblenet

Requirements
  • Python
  • NumPy
  • Optionally: SciPy
This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.
Features:
  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions
Example Usage
fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)
About
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
Resources
Readme
BSD-2-Clause license
Activity
Stars
298 stars
Watchers
19 watching
Forks
95 forks
Report repository
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Neural network written in Python (NumPy)
This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.
Visit the project page or Read the documentation.
The code has been tested.
Implemented learning algorithms:
  • Vanilla Backpropagation
  • Backpropagation with classical momentum
  • Backpropagation with Nesterov momentum
  • RMSprop
  • Adagrad
  • Adam
  • Resilient Backpropagation
  • Scaled Conjugate Gradient
  • SciPy’s Optimize
Installation
pip install nimblenet

Requirements
  • Python
  • NumPy
  • Optionally: SciPy
This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.
Features:
  • Implemented with matrix operations to ensure high performance.
  • Dropout regularization is available to reduce overfitting. Implemented as desribed here.
  • Martin Møller's Scaled Conjugate Gradient for Fast Supervised Learning as published here.
  • PYPY friendly (requires pypy-numpy).
  • Features a selection of cost functions (error functions) and activation functions
Example Usage
fromnimblenet.activation_functionsimportsigmoid_functionfromnimblenet.cost_functionsimportcross_entropy_costfromnimblenet.learning_algorithmsimportRMSpropfromnimblenet.data_structuresimportInstancefromnimblenet.neuralnetimportNeuralNetdataset= [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings= {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network=NeuralNet( settings )
training_set=datasettest_set=datasetcost_function=cross_entropy_costRMSprop(
network, # the network to traintraining_set, # specify the training settest_set, # specify the test setcost_function, # specify the cost function to calculate error
)
About
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
Resources
Readme
BSD-2-Clause license
Activity
Stars
298 stars
Watchers
19 watching
Forks
95 forks
Report repository
Releases
Packages
Used by
Contributors
Languages