If you think that TensorFlow and PyTorch are the only ways to Neural Networks with Python then I would tell you that you are wrong. As there is another package to build Neural Networks with Scikit-Learn and Python. In this article, I will take you through the easiest way to build Neural Networks with Scikit-Learn.
Before I introduce you to how we can build a Neural Network using Scikit-Learn, let’s start this from scratch for all the beginners in Machine Learning who are reading this article.
Also, Read – Binary Search Algorithm with Python.
What are Neural Networks?
Networks that mimic the functioning of the human brain; computer programs that actually learn patterns; forecasts without having to know the statistics are neural networks. Building a Neural Network takes a lot of time as compared to training a classification or prediction model.
Tensorflow and PyTorch are the two common methods for building Neural Networks, and it would not be wrong if I say that using TensorFlow or PyTorch to build a Neural Network are the best methods. Because a neural network needs to be explicitly trained to make predictions as it does not relies on statistics to make predictions.
TensorFlow and PyTorch do not lack anywhere to build a neural network, so why I am introducing you to another way to build neural networks using scikit-learn. The answer is that not every beginner is capable of using the workflow of TensorFlow and PyTorch. As both of these methods require deep knowledge of every concept of Deep Learning.
Neural Networks with Scikit-Learn
When it comes to classification models, I have always preferred to use Scikit-Learn, and I hope other machine learning experts prefer it too. But for the tasks of Deep Learning Scikit-Learn needs more introduction to be used globally.
For the implementation of neural networks with Scikit-Learn, scikit-neuralnetwork package is used. So here we will learn how we can use this package to build Neural Networks.
To build neural networks using Scikit-Learn you need to install scikit-neuralnetwork package, which can be easily installed by using the pip command – pip install scikit-neuralnetwork. Now, if you have installed this package successfully, let’s get started to build neural networks with scikit-learn.
Building Neural Networks with Scikit-Learn
scikit-neuralnetwork offers an easy way to create a custom neural network. Scikit-learn users will feel right at home with a familiar API:
from sknn.mlp import Classifier, Layer
nn = Classifier(
layers=[
Layer("Maxout", units=100, pieces=2),
Layer("Softmax")],
learning_rate=0.001,
n_iter=25)
nn.fit(X_train, y_train)Code language: JavaScript (javascript)X_train and y_train are NumPy arrays, so you can simply replace your scikit-learn model with a Neural Net of sknn. It even supports sparse data sets.
Also, Read – One Hot Encoding in Machine Learning.
I hope you liked this article on implementing a neural network with scikit-learn. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.
