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A Keras like deep learning library works on top of PyTorch

Table of contents:

• Introduction
• PyTorch
• Install
• Dependencies
• Get Started
• Documentation
• Examples
• Blogs and Tutorials
• Support
• Contributing
• License

Introduction

NeuralPy is a High-Level Keras like deep learning library that works on top of PyTorch written in pure Python. NeuralPy can be used to develop state-of-the-art deep learning models in a few lines of code. It provides a Keras like simple yet powerful interface to build and train models.

Here are some highlights of NeuralPy

• Provides an easy interface that is suitable for fast prototyping, learning, and research
• Can run on both CPU and GPU
• Works on top of PyTorch
• Cross-Compatible with PyTorch models

PyTorch

PyTorch is an open-source machine learning framework that accelerates the path from research prototyping to production deployment developed by Facebook runs on both CPU and GPU.

According to Wikipedia,

PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab (FAIR). It is free and open-source software released under the Modified BSD license.

NeuralPy is a high-level library that works on top of PyTorch. As it works on top of PyTorch, NerualPy supports both CPU and GPU and can perform numerical operations very efficiently.

If you want to learn more about PyTorch, then please check the PyTorch documentation.

Install

To install NeuralPy, open terminal window type the following command:

pip install neuralpy-torch

If you have multiple versions of it, then you might need to use pip3.

pip3 install neuralpy-torch
//or
python3 -m pip install neuralpy-torch

NeuralPy requires Pytorch and Numpy, first install those

Check the documentation for Installation related information

Dependencies

The only dependencies of NeuralPy are Pytorch (used as backend) and Numpy.

Get Started

Let's create a linear regression model in 100 seconds.

Importing the dependencies

import numpy as np

from neuralpy.models import Sequential
from neuralpy.layers import Dense
from neuralpy.optimizer import Adam
from neuralpy.loss_functions import MSELoss

Making some random data

# Random seed for numpy
np.random.seed(1969)

# Generating the data
X_train = np.random.rand(100, 1) * 10
y_train = X_train + 5 *np.random.rand(100, 1)

X_validation = np.random.rand(100, 1) * 10
y_validation = X_validation + 5 * np.random.rand(100, 1)

X_test = np.random.rand(10, 1) * 10
y_test = X_test + 5 * np.random.rand(10, 1)

Making the model

# Making the model
model = Sequential()
model.add(Dense(n_nodes=1, n_inputs=1, bias=True, name="Input Layer"))

# Compiling the model
model.compile(optimizer=Adam(), loss_function=MSELoss())

# Printing model summary
model.summary()

Training the model

model.fit(train_data=(X_train, y_train), test_data=(X_validation, y_validation), epochs=300, batch_size=4)

Predicting using the trained model

model.predict(X=X_test, batch_size=4)

Documentation

The documentation for NeuralPy is available at https://neuralpy.imdeepmind.com/

Examples

Several example projects in NeuralPy are available at https://github.com/imdeepmind/NeuralPy-Examples. Please check the above link.

Blogs and Tutorials

For now, there are no blogs or tutorials available, I'll update once I post something. Please also have a look at the examples currently available.

Support

If you are facing any issues using NeuralPy, then please raise an issue on GitHub or contact with me.

Contributing

Feel free to contribute to this project. If you need some help to get started, then reach me or open a GitHub issue. Check the CONTRIBUTING.MD file for more information and guidelines.

License

MIT