several model variations were implemented using high resolution data, low-resolution data, with two activation marker features or seven activation marker features, with or without a score threshold. All models were constructed and trained using Keras with the Tensorflow backend. The training was done ...
machine-learning neural-network tensorflow keras optimizer outlier-detection deeplearning standardscaler sklearn-metrics hyperparametertuning labelencoder featureselection roc-auc-curve logtransform Updated Jan 22, 2024 Jupyter Notebook Metu-O / Feature-Selection-Qiime2- Star 0 Code Issues Pull requests...
estimates of feature importance for individual decisions in a single end-to-end trained neural network model. This repository provides a reference implementation of AMEs and the Granger-causal objective using the Keras and TensorFlow frameworks. You can find the manuscript athttp://arxiv.org/abs/...
TensorFlow/Keras are Python’s deep learning libraries that supply users with application programming interfaces (APIs) for creating and training neural networks. Librosa’s Python library contributes tools for feature extraction from audio signals. PyTorch is similar to TensorFlow. It supports building...
In a neural network, weight magnitude is a metric that shows the importance of each connection (Kavzoglu and Mather 1998). This stems from the fact that weights with a small magnitude have a small effect on the performance of the model. At the beginning of training, we initialize all ...
Several months ago I wrote a tutorial onimplementing custom Keras data generators, and more specifically, yielding data from a CSV file to train a neural network with Keras. At the time, I found that readers were a bit confused on practical applications where you would use such a generator ...
VisionTool is a semantic features extraction toolbox written in Python based on tensorflow and the embedded neural network library keras (see Fig. 3 for a schematic description of VisionTool’s workflow). The toolbox offers a user-friendly interface allowing the user to easily exploit all the ...
Unlike traditional CD methods, the convolutional neural network (CNN)-based approaches do not require manual feature design and can extract multi-level abstract features in complex scenes in an automatic manner. Given the importance of feature learning, which directly affects the reconstruction of the...
We implemented neural network-based models using Keras with TensorFlow backend, Cox-EN model using scikit- survival package. For CNN-Cox model with 2D-Hybrid-CNN structure, we reshaped the screened 6407 gene inputs as a matrix with 100 rows and 65 columns by adding 93 zeros in the last ...
This is partially due to the separation of the feature aggregation step from variable selection, which fails to adapt to the importance of these features. Combining these two steps could potentially enhance variable selection performance and enable identified clusters to better adapt to their importance...