Python can be said as one of the most widely used languages because of its multiple features which include a large variety of useful libraries, extremely vast community, and other such things. The libraries mentioned here provide basic and neural network variants for accessing the neural network and deep learning based research codes.
In this article, we list down the top 7 Python Neural Network libraries to work on.
PyTorch is a Python package that provides two high-level features, tensor computation (like NumPy) with strong GPU acceleration, deep neural networks built on a tape-based autograd system. Usually one uses PyTorch either as a replacement for NumPy to use the power of GPUs or a deep learning research platform that provides maximum flexibility and speed. PyTorch has a unique way of building neural networks: using and replaying a tape recorder.
NeuroLab is a simple and powerful Neural Network Library for Python. This library contains based neural networks, train algorithms and flexible framework to create and explore other networks. It supports neural network types such as single layer perceptron, multilayer feedforward perceptron, competing layer (Kohonen Layer), Elman Recurrent network, Hopfield Recurrent network, etc.
The features of this library are mentioned below
- It is pure a Python + NumPy library
- API like Neural Network Toolbox (NNT) from MATLAB
- Interface to use train algorithms form Scipy.optimize
- Flexible network configurations and learning algorithms. You may change: train, error, initialisation as well as activation functions
- Variety of supported types of Artificial Neural Network and other learning algorithms
- It has Python 3 support
ffnet or feedforward neural network for Python is fast and easy to use feed-forward neural network training solution for Python. You can use it to train, test, save, load and use an artificial neural network with sigmoid activation functions.
The features of this library are mentioned below
- Any network connectivity without cycles is allowed (not only layered).
- Training can be performed with the use of several optimisation schemes including genetic algorithm based optimization.
- There is access to exact partial derivatives of network outputs vs. its inputs.
- Normalization of data is handled automatically by ffnet.
This library implements multi-layer perceptrons, auto-encoders and recurrent neural networks with a stable future proof interface as a wrapper for the powerful existing libraries such as lasagne currently, with plans for blocks which is compatible with Scikit-learn for a more user-friendly and Pythonic interface. By importing the sknn package provided by this library, you can easily train deep neural networks as regressors (to estimate continuous outputs from inputs) and classifiers (to predict discrete labels from features).
Due to the underlying Lasagne implementation, the code supports the following neural network features
- Activation Functions: Sigmoid, Tanh, Rectifier, Softmax, Linear.
- Layer Types: Convolution (greyscale and color, 2D), Dense (standard, 1D).
- Learning Rules: sgd, momentum, nesterov, adadelta, adagrad, rmsprop, adam.
- Regularization: L1, L2, dropout, and batch normalization.
- Dataset Formats: Numpy.ndarray, scipy.sparse, pandas.DataFrame and iterators (via callback).
Lasagne is a lightweight library to build and train neural networks in Theano. The design of this library is governed by six principles, simplicity, transparency, modularity, pragmatism, restraint and focus. Its main features are mentioned below
- Supports feed-forward networks such as Convolutional Neural Networks (CNNs), recurrent networks including Long Short-Term Memory (LSTM), and any combination thereof
- Allows architectures of multiple inputs and multiple outputs, including auxiliary classifiers
- Many optimization methods including Nesterov momentum, RMSprop, and ADAM
- Freely definable cost function and no need to derive gradients due to Theano’s symbolic differentiation
- Transparent support of CPUs and GPUs due to Theano’s expression compiler
pyrenn is a recurrent neural network toolbox for Python and Matlab. The important features of pyrenn are mentioned below
- pyrenn allows creating a wide range of (recurrent) neural network configurations
- It is very easy to create, train and use neural networks
- It uses the Levenberg–Marquardt algorithm (a second-order Quasi-Newton optimization method) for training, which is much faster than first-order methods like gradient descent. In the Matlab version additionally the Broyden–Fletcher–Goldfarb–Shanno algorithm is implemented
- The python version is written in pure python and Numpy and the Matlab version in pure Matlab (no toolboxes needed)
- Real-Time Recurrent Learning (RTRL) algorithm and Backpropagation Through Time (BPTT) algorithm are implemented and can be used to implement further training algorithms
- It comes with various examples which show how to create, train and use the neural network