neuralpy - The most intuitive Neural Network Model
Within this package is the most intuitive fully-connected multilayer neural network model. Data science shouldn’t have a high barrier to entry. neuralpy handles the math and overhead while you focus on the data.
neuralpy is a neural network model written in python based on Michael Nielsen’s neural networks and deep learning book.
- Visit the neuralpy website
- Get detailed examples and explanations in the Official Documentation
- Contribute on Github
Getting Started (quick start)
The following demonstrates how to download and install neuralpy and how to create and train a simple neural network. Run the following command to download and install:
$ pip install neuralpy
Create a neural network in your project by specifying the number of nodes in each layer. Random weights and biases will automatically be generated:
import neuralpy net = neuralpy.Network([2, 3, 1])
The network feeds input vectors as python lists forward and returns the output vector as a list:
x = [1, 1] output = net.forward(x) print output # ex: [0.11471727263613461]
Train the neural network by first generating training data in the form of a list of tuples. Each tuple has two components and each component is a list representing the input and output respectively. This training set represents the simple OR function and it can be generated for you to save typing:
training_data = neuralpy.load_or() # [ # ([1, 1], ), # ([1, 0], ), # ... # ]
Then we must specify the remaining hyperparameters. Let’s say we want to limit it to 100 epochs and give it a learning rate of 1:
epochs = 100 learning_rate = 1
Then run the train method with the parameters. We’re telling the network to conform to training data:
net.train(training_data, epochs, learning_rate)
Now feed forward the input from earlier and the output should be closer to 1.0, which is what we trained the network to do:
output = net.forward(x) print output # ex: [0.9542129706170075]
There is more information about advanced options such as monitoring the cost in the official documentation.
Since, this is a multilayer feedforward neural network, it is a universal approximator (Hornik, Stinchcombe and White, 1989). Neural Networks can be used for a wide range of applications from image processing to time series prediction.
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