A Spatial-compositional Feature Fusion Convolutional Autoencoder for Multivariate Geochemical Anomaly Recognition
This project is implemented using python (≥3.6 ), which can run in window 10 systems.
This project uses four Python open source libraries (Numpy, Pandas, H5py, Scikit-learn, Tensorflow and Keras). This project complies with the GPL-3.0 License.
Numpy (https://numpy.org/) is an open source numerical calculation tool developed by Travis Oliphant. Used in this project for matrix operation. This library complies with the BSD license.
Pandas (https://pandas.pydata.org/) is an open source library, providing high-performance, easy-to-use data structures and data analysis tools. This library complies with the BSD license.
H5py (http:// http://h5py.org/) is a Pythonic interface to the HDF5 binary data format s. Used in this project for saving training weights. This library complies with the BSD license.
Scikit-learn (https:// scikit-learn.org/ is an open source machine learning library that supports supervised and unsupervised learning. Used in this project for evaluating model performance. This library complies with the BSD license.
Tensorflow (https:// tensorflow.google.cn/) is an end-to-end open source platform for machine learning. Used in this project for building the deep learning model. This library complies with the Apache-2.0 License.
Keras (https:// keras.io/) is a minimalist, highly modular neural network library. Used in this project for building the deep learning model. This library complies with the Apache-2.0 License.