Artificial neural network python github

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Artificial neural network python github

This is the code repository for Neural Network Projects with Python, published by Packt. The ultimate guide to using Python to explore the true power of neural networks through six projects What is this book about? Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. This book covers the following exciting features: Learn various neural network architectures and its advancements in AI Master deep learning in Python by building and training neural network Master neural networks for regression and classification Discover convolutional neural networks for image recognition Learn sentiment analysis on textual data using Long Short-Term Memory If you feel this book is for you, get your copy today! Instructions and Navigations All of the code is organized into folders. For example, Chapter02. The code will look like the following: def detect_faces(img, draw_box=True): # convert image to grayscale grayscale_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Following is what you need for this book: This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks. With the following software and hardware list you can run all code files present in the book (Chapter 1-7). Software and Hardware List Chapter Software required OS required 1-7 Python, Jupyter Notebook Windows, Mac OS X, and Linux (Any) We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it. Related products Get to Know the Author James Loy has more than five years, expert experience in data science in the finance and healthcare industries. He has worked with the largest bank in Singapore to drive innovation and improve customer loyalty through predictive analytics. He has also experience in the healthcare sector, where he applied data analytics to improve decision-making in hospitals. He has a master's degree in computer science from Georgia Tech, with a specialization in machine learning. His research interest includes deep learning and applied machine learning, as well as developing computer-vision-based AI agents for automation in industry. He writes on Towards Data Science, a popular machine learning website with more than 3 million views per month. Suggestions and Feedback Click here if you have any feedback or suggestions. About the Case Study In this Business Case Study we predict the churning rate of the customers from the bank. In order to learn about bank's customers we will make use of one pf the Deep Learning techniques, the Artificial Neural Networks (ANN).From the millions of customers we have randomly selected 10K customers. We will use customer's characteristics to determine his/her probability of leaving the bank. In order to learn about bank's customers we will make use of one pf the Deep Learning techniques, the Artificial Neural Networks (ANN). Moreover, we will use popular Python libraries such as Tensorflow, Keras and Machine Learning techniques such as Adam Optimizer to train the ANN model and predict the churn rates. Little background in ANN Neural networks adapt themselves to the changing input so that the network generates the best possible result without the need to redesign the output criteria. The functionality of neural networks is often compared to the one of the multiple linear regression, where one uses multiple input features, also called independent variables, to predict the output variable, the dependent variable. In case of Neural Network we also use input features, referred as Input Layer Neurons to get information and learn about the outcome variable, referred as Output Layer.The main difference between such regression and Neural Network is that, in the case the former the process runs in one iteration by minimizing the sum of the squared residuals (similar to cost function), whereas in case of Neural Network there is an intermediate step portrayed by the Hidden Layer Neurons which are used to get signals from the input layers and learn about the observations over and over again until the goal is achieved, the cost is minimized and no improvement is possible. So, one can say that ANNs are much more sophisticated than multiple linear regression. Model setting The combination of the Rectifier and Sigmoid activation functions is quite popular and this exact combination will be used in this case study as well, given that our goal is to estimate the probability that the customer will leave the bank. Given that the output variable is binary we use cost function Binary Cross Entroopy. Following topics and technical are covered in the paper and in the rest of the files: Activation function for Hidden Layer: Rectifier Activation function for Output Layer: Sigmoid Optimization method: Adam Optimizer Cost function: Binary Cross Entropy Number of epochs: 100 Batch size: 25 Sample outputs (screenshots) Training the ANN model True versus Predicted values References Glorot, X., Bordes, A., Bengio, Y. (2011). Deep Sparse Rectifier Neural NetworksInternational Conference on Artificial Intelligence and Statistics. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 15(15), 315 - 323 Rogerson, R. J. (2015). Adam: A Method For Stochastic Optimization. 3rd Interna-tional Conference on Learning Representations (ICLR2015), 36(1), 1?13 LeCun, Y., Bengio, Y., Hinton, G., (2015). Deep learning.Nature, 521, 436 ? 444 Wiegerinck, W. and Komoda, A. and Heskes, T. (1999). Stochastic dynamics of learn-ing with momentum in neural networks. Journal of Physics A: Mathematical andGeneral, 27(13), 4425 ? 4425 Page 2 You can't perform that action at this time. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. Page 2 The project was done for an introductory course in artificial intelligence. The work was done in groups of two. The project aimed to implement a simple artificial neural network in Python using Numpy. To then evaluate the implementation, the famous MNIST dataset was used where we achieved a 96% accuracy. To further investigate the topic, we created our own very small dataset using paint and with that, we achieved a 70% accuracy. More information about how the neural network was implemented can be found in the project report. Usage example First, you need to install the requirements found in the requirements.txt file. This is preferably done inside a virtual environment. pip install -r requirements.txt After that, the following command can be run to construct and train a network. python main.py 784 100 10 NN.bin Development setup Python 3 is required. Meta Erik B?venstrand ? Portfolio ? erik@bavenstrand.se Distributed under the MIT license. See LICENSE for more information. ErikBavenstrand Page 2 The project was done for an introductory course in artificial intelligence. The work was done in groups of two. The project aimed to implement a simple artificial neural network in Python using Numpy. To then evaluate the implementation, the famous MNIST dataset was used where we achieved a 96% accuracy. To further investigate the topic, we created our own very small dataset using paint and with that, we achieved a 70% accuracy. More information about how the neural network was implemented can be found in the project report. Usage example First, you need to install the requirements found in the requirements.txt file. This is preferably done inside a virtual environment. pip install -r requirements.txt After that, the following command can be run to construct and train a network. python main.py 784 100 10 NN.bin Development setup Python 3 is required. Meta Erik B?venstrand ? Portfolio ? erik@bavenstrand.se Distributed under the MIT license. See LICENSE for more information. ErikBavenstrand Proof of concept implementations of various sparse artificial neural network models with adaptive sparse connectivity trained with the Sparse Evolutionary Training (SET) procedure. The following implementations are distributed in the hope that they may be useful, but without any warranties; Their use is entirely at the user's own risk. Proof of concept implementation of Sparse Evolutionary Training (SET) for Multi Layer Perceptron (MLP) on CIFAR10 using Keras and a mask over weights. This implementation can be used to test SET in varying conditions, using the Keras framework versatility, e.g. various optimizers, activation layers, tensorflow. Also it can be easily adapted for Convolutional Neural Networks or other models which have dense layers. Variants of this implementation have been used to perform the experiments from Reference 1 with MLP and CNN. However, due the fact that the weights are stored in the standard Keras format (dense matrices), this implementation can not scale properly. If you would like to build an SET-MLP with over 100000 neurons, please use Implementation 2. An improved version of this Implementation can be found here Proof of concept implementation of Sparse Evolutionary Training (SET) for Multi Layer Perceptron (MLP) on lung dataset using Python, SciPy sparse data structures, and (optionally) Cython. This implementation was developed just in the last stages of the reviewing process, and we are briefly discussing about it in the "Peer Review File" which can be downloaded from Reference 1 website. This implementation can be used to create SET-MLP with hundred of thousands of neurons on a standard laptop. It was made starting from the vanilla fully connected MLP implementation of Ritchie Vink ( and we would like to acknowledge his work and thank him. Also, we would like to thank Thomas Hagebols for analyzing the performance of SciPy sparse matrix operations. We thank also to Amarsagar Reddy Ramapuram Matavalam from Iowa State University (amar@iastate.edu), who provided us a faster implementation of the "weightsEvolution" method, after the initial release of this code. If you would like to try large SET-MLP models, below are the expected running times measured on my laptop (16 GB RAM) using the original implementation of the "weightsEvolution" method. I have used exactly the model and the dataset from the file "set_mlp_sparse_data_structures.py" and I just changed the number of hidden neurons per layer: 3,000 neurons/hidden layer, 12,317 neurons in total 0.3 minutes/epoch 30,000 neurons/hidden layer, 93,317 neurons in total 3 minutes/epoch 300,000 neurons/hidden layer, 903,317 neurons in total 49 minutes/epoch 600,000 neurons/hidden layer, 1,803,317 neurons in total 112 minutes/epoch If you would like to try out SET-MLP with various activation functions, optimization methods and so on (in the detriment of scalability) please use Implementation 1. Proof of concept implementation of Sparse Evolutionary Training (SET) for Restricted Boltzmann Machine (RBM) on COIL20 dataset using Python, SciPy sparse data structures, and (optionally) Cython. This implementation can be used to create SET-RBM with hundred of thousands of neurons on a standard laptop and was developed just before the publication of Reference 1. Tutorial details "Scalable Deep Learning: from theory to practice" The code is based on Implementation 2 of SET-MLP to which Dropout is added. In the "Pretrained_results" folder there is a nice animation "fashion_mnist_connections_evolution_per_input_pixel_rand0.gif" of the input layer connectivity evolution during training. Tutorial details - "Scalable Deep Learning: from theory to practice" The code is based on Implementation 2 of SET-MLP to which Dropout is added. In the "Pretrained_results" folder there is a nice animation "fashion_mnist_connections_evolution_per_input_pixel_rand0.gif" of the input layer connectivity evolution during training. For an easy understanding of these implementations please read the following articles. Also, if you use parts of this code in your work, please cite the corresponding ones: @article{Mocanu2018SET, author = {Mocanu, Decebal Constantin and Mocanu, Elena and Stone, Peter and Nguyen, Phuong H. and Gibescu, Madeleine and Liotta, Antonio}, journal = {Nature Communications}, title = {Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science}, year = {2018}, doi = {10.1038/s41467-018-04316-3}, url = { }} @article{Mocanu2016XBM, author={Mocanu, Decebal Constantin and Mocanu, Elena and Nguyen, Phuong H. and Gibescu, Madeleine and Liotta, Antonio}, title={A topological insight into restricted Boltzmann machines}, journal= {Machine Learning}, year={2016}, volume={104}, number={2}, pages={243--270}, doi={10.1007/s10994-016-5570-z}, url={ }} @phdthesis{Mocanu2017PhDthesis, title = {Network computations in artificial intelligence}, author = {Mocanu, Decebal Constantin}, year = {2017}, isbn = {978-90-386-4305-2}, publisher = {Eindhoven University of Technology}, url={ } } @article{Liu2019onemillion, author = {Liu, Shiwei and Mocanu, Decebal Constantin and Mocanu and Ramapuram Matavalam, Amarsagar Reddy and Pei, Yulong Pei and Pechenizkiy, Mykola}, journal = {arXiv:1901.09181}, title = {Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware}, year = {2019}, url={ } } SET shows that large sparse neural networks can be built if topological sparsity is created from the design phase, before training. There are many algorithmic and implementation improvements which can be made. If you find this work interesting, please share the links to this Github page and to Reference 1. For any question, suggestion, feedback please feel free to contact me by email. Community Some time ago, I had a very pleasant unexpected surprise when I found out that Michael Klear released "Synapses". This library implements SET layers in PyTorch and as Michael says it is "truly sparse". For more details please read his article: And try out "Synapses" yourself: Many things can be improved in "Synapses". If interested, please contact and help Michael in developing further the project. Update 4 June 2020 Our paper "Topological insights into sparse neural networks" has been accepted at ECMLPKDD 2020. It proposes Neural Network Sparse Topology Distance (NNSTD) to measure the distance between different sparse neural networks. The code is here . Also, it shows in a principled manner that sparse training easily unveils a plenitude of sparse sub-networks with very different topologies which outperform the dense networks. Update 30 November 2020 For an interesting quick read about sparse training, please have a look on this blog Update 14 December 2020 To see how sparse training can be used for feature selection please check our latest paper, titled "Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders", here: and the corresponding truly sparse implementation here: Many thanks, Decebal

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