Static Features Classifier - A static features classifier for Point-Could clusters using an Attention-RNN model

Last update: Jan 25, 2022

Overview

Static Features Classifier

This is a static features classifier for Point-Could clusters using an Attention-RNN model

Installation

$ git clone https://github.com/Mohtasib/Static_Features_Classifier.git
$ cd Static_Features_Classifier
$ conda create -n sfc_env python==3.6.10
$ conda activate sfc_env
$ pip install -e .

Example Code

You can simple run the following command to run a sample code

$ python examples/train_and_evaluate.py

Usage

1. Import Libraries:

import numpy as np
from sfc.util import create_dataset
from sfc.models.Attention_RNN import Attention_RNN

2. Define the constants:

NUM_FEATURES = ...

3. Define the directories:

DATA_PATH = ...
My_Model_Logs_DIR = ...
My_Model_Weights = My_Model_Logs_DIR + 'Best_Attention_RNN_ckpt.h5'

4. Create the dataset:

features, labels = create_dataset(DATA_PATH, NUM_FEATURES)

5. Create the model:

My_Model = Attention_RNN(Logs_DIR=My_Model_Logs_DIR)

6. Load the data into the model:

My_Model.x_train = features
My_Model.y_train = labels
My_Model.x_test = ...
My_Model.y_test = ...

7. Train the model:

My_Model.Fit()

8. Evaluate the model:

My_Model.Evaluate()

9. Predict using the model:

predictions = My_Model.Predict(data)

Static Features Classifier - A static features classifier for Point-Could clusters using an Attention-RNN model

Related tags

Overview

Static Features Classifier

Installation

Example Code

Usage

1. Import Libraries:

2. Define the constants:

3. Define the directories:

4. Create the dataset:

5. Create the model:

6. Load the data into the model:

7. Train the model:

8. Evaluate the model:

9. Predict using the model:

NOTE: More details will be available soon!!!

Owner

ABDALKARIM MOHTASIB

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Static Features Classifier - A static features classifier for Point-Could clusters using an Attention-RNN model

Related tags

Overview

Static Features Classifier

Installation

Example Code

Usage

1. Import Libraries:

2. Define the constants:

3. Define the directories:

4. Create the dataset:

5. Create the model:

6. Load the data into the model:

7. Train the model:

8. Evaluate the model:

9. Predict using the model:

NOTE: More details will be available soon!!!

Owner

ABDALKARIM MOHTASIB

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

This is the code for the paper "Motion-Focused Contrastive Learning of Video Representations" (ICCV'21).

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TensorFlow implementation of Deep Reinforcement Learning papers

[CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search

Face Mask Detection System built with OpenCV, TensorFlow using Computer Vision concepts

SWA Object Detection

A package, and script, to perform imaging transcriptomics on a neuroimaging scan.

The Official PyTorch Implementation of DiscoBox.

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

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​TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

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