Python Libraries for Neural Networks
In this post, you will find brief information about the following Python libraries that can be used for creating Neural Networks:
It is worth noting, here, that this list is not comprehensive in that sense that there are more Python machine learning libraries that can used to create neural networks.
One of the most popular Python libraries for neural networks today appears to be Google’s Tensorflow. Tensorflow is, many times, called a framework, rather than a code library. Now, the difference between the two concepts may seem unimportant, but the creators of Tensorflow call it ”an open code library for machine intelligence”.
Using Tensorflow, you can train and run so called deep neural networks. Examples of applications include interpreting handwriting, machine translation, management of natural language, and various types of simulations. Furthermore, you work with predictions on a large scale, which is perhaps the kind of applications most people think about in the context of machine learning and neural networks.
Finally, there is broad support at all for related technologies and products. For example, one of them is the next in the list of Python libraries for neural networks; Keras. Now, TensorFlow might be a contender to the title “best Python library for neural networks”.
How to Install TensorFlow
Now, to use TensorFlow to create Neural Networks you can install it using pip:
pip install tensorflow
It is, of course, also possible to install TensorFlow using the package manager of your Python distribution. For example, if you want to install the package using conda you can use this code:
conda install tensorflow
Keras is a high-level neural networks library, described as an API, written in Python. Noteworthy, Keras can be run on top of TensorFlow, CNTK, or Theano.
Interestingly, it is stated at Keras’ website that the library is designed to adhere to best practices for reducing cognitive load. This means that Keras:
- have consistent & simple APIs
- minimizes the number of user actions needed for common use cases
- lends us clear & actionable error messages
- Has extensive documentation and developer guides
Great news! In the next subsection, you will learn how to install Keras.
How to Install Keras
To install Keras you can use pip:
pip install keras
As with TensorFlow, Keras can be found in many Python distributions. For instance, to install Keras using conda you do like this:
conda install tensorflow
PyTorch is one of the latest packages that can be used for creating neural networks, among other things. It is fair to say, I think that it is a close relative of the Lua-based Torch framework, where Torch has been rewritten to fit Python to provide a faster experience. This also make it feel more native. Furethermore, PyTorch is an optimized tensor library that runs on GPU and CPU.
Now, because you can use GPUs many choose PyTorch as a replacement for NumPy, but it also because it provides a very flexible, and fast, platform for carrying out deep learning research with. If you happen to be without a powerfull GPU, I suggest that you use Google Colab instead to harness its free GPU power.
How to Install PyTorch
You can use pip to install PyTorch:
pip install torch===1.5.0 torchvision===0.6.0 -f https://download.pytorch.org/whl/torch_stable.html
PyTorch can also be installed if you’re using a Python distribution such as Anaconda:
conda install -c pytorch pytorch
Now, as the name implies NeuroLab is a library of basic neural networks algorithms. It offers flexible network configurations and learning algorithms for Python. According to the documentation, the developers have created the NeuroLab’s interface similar to the package of Neural Network Toolbox (NNT) of MATLAB. Finally, the neural network library is based on the package NumPy, and some of the learning algorithms are use scipy.optimize.
How to Install NeuroLab
If you want to install NeuroLab with pip you just type:
pip install neurolab
If you, on the other hand, want to install Neurolab using conda you’ll use this command:
conda install -c labfabulous neurolab
Even though Lasagne (also) is a great dish (I am getting hungry writing this), this Python library is light-weight and can be used for building and training neural networks.
Lasagne is designed folliwing the six principles of 1) simplicity, 2) transparency, 3) modularity, 4) pragmatism, 5) restraint, and 6) focus. Furthermore, this neural networks library has the following main featuers:
- It has support for feed-forward networks. For example, you can create Convolutional Neural Networks (CNNs), recurrent networks including Long Short-Term Memory (LSTM).
- Lasagne allows architectures of multiple inputs and multiple outputs, including auxiliary classifiers
- The package supports optimization methods such as Nesterov momentum, RMSprop, and ADAM, to name a few
- It has a freely definable cost function and there is no need to derive gradients due to Theano’s symbolic differentiation
- Lsagne has transparent support of CPUs and GPUs due to Theano’s expression compiler
How to Install Lasagne
Here’s how to install Lasagne using pip:
pip install lasagne
If you, on the other hand, want to install Lasagne using conda here’s how:
conda install -c toli lasagne
Note, Lasagne cannot be used in Python 3, as far as I know anyway.
Theano is a Python library that enables you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Here are some of its features:
- It is thighly integrated with NumPy. Functions compiled in THeano will use NumPy arrays (numpy.ndarray),
- Theano has a transparent use of a GPU. The library perform data-intensive computations way faster using a GPU than a CPU.
- efficient symbolic differentiation
- speed and stability optimizations
- Code is dynamically generated in C which means that Theano evaluate expressions faster.
- Has an extensive unit-testing and self-verification. It is, thus, very easy to detect and diagnose errors
All this means that Theano “knows” how to take structures and convert them into very efficient code. Theano was mainly designed to take care the types of computation needed for large Neural Network algorithms that is commonly used in Deep Learning. This is one of the reasons behind its popularity in the field of Deep Learning.
Here’s a preprint for you to learn more about Theano. Finally, Theano is an open source project that was primarily developed by a Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal.
How to Install Theano
Now, the last Python library for Neural Networks, can also be installed using pip:
pip install theano
Finally, you can install Theano with conda:
conda install -c conda-forge theano
One neat thing is that you can find a lot of Theano tutorials, on their webpage, to get you started.
Are you unsure whether you have any of these Neural Network libraries installed? Learn how to list all installed Python packages.
In this post you learned about Python libraries that can be used for the creation of Neural Networks. It is worth noting, that you should install the Neural Network libraries in a virtual environment. I know, that I didn’t show how to do this because this was not the focus of the post.
In conclusion, the packages listed in this post may very-well be some of the best Python libraries for Neural Networks. What do you think? Which Python library is the absolutely best one for Neural Networks? Leave a comment below!
More Python Tutorials:
Here are some Python tutorials on this site: