Generating Pytorch Skeleton Code for Binary and Multi-class Classification to Boost SEO and Increase User Appeal
Introduction:
Welcome to this post, where I will be sharing my GitHub repository that contains an implementation for training, testing, and interpretation models. Whether you’re a beginner or an advanced developer, this repository is a valuable resource to refer to when building your own model.
The repository contains sections covering various aspects, including the setup process, training, and testing procedures. It also includes file details for better understanding and output generation information.
To help you navigate and understand the repository, each section is explained in detail. The repository also provides instructions on how to view the tensorboard logs and includes a simulation using the COVID-19 CXR dataset on Kaggle.
Additionally, the post includes visualizations of CXR samples, histograms, predictions generated by the model, and GradCam visualizations.
By exploring this repository, you will be equipped with valuable tools and insights to enhance your own model development. Happy exploring!
Full Article: Generating Pytorch Skeleton Code for Binary and Multi-class Classification to Boost SEO and Increase User Appeal
GitHub Repository for Model Development
I have created a GitHub repository that contains the implementation for training, testing, and interpretation of models. You can refer to this repository to develop your own model. In this post, I will cover the different sections of the repository and provide a brief overview of each.
Repository Link
You can access the source code repository by clicking here.
Setup
To get started, you need to install the required packages listed in the requirements.txt file. This will ensure that you have all the necessary dependencies to run the code successfully.
Training
To train your model, you can use the train_caller.py script. This script loads the train configuration file and retrieves the train parameters, model parameters, and visualization parameters. It then calls the train method in the utils.train.py file.
The train.py script is responsible for loading the training, visualization, transformation, and model parameters. It forms the model by calling the models.models.py file. It also configures the data loader by calling the data.databuilder.py script, which uses predefined transformations defined in the utils.transformer.py script. Additionally, it sets the class weights, visualizes the training and validation samples, defines the directories and model version, creates/checks paths, defines the optimizer and scheduler, and specifies the loss function using utils.loss.py. The training epochs are initiated, and the training and validation evaluation are recorded in the form of a dictionary obtained using utils.eval.py. Furthermore, a Tensorboard summary writer is created to record the train and valid loss over each epoch. The weights of the model that achieved the best results over the epochs are saved.
To start the training process, you can run the following command:
python train_caller.py
Testing
To test your trained model, you can use the test_caller.py script. This script loads the test configuration file and retrieves the test parameters, model parameters, and visualization parameters. It then calls the test method in the utils.test.py file.
Similar to the training process, the test.py script is responsible for loading the testing, visualization, transformation, and model parameters. It forms the model by calling the models.models.py file. It configures the data loader by calling the data.databuilder.py script and uses transformations defined in the utils.transformer.py script. It also visualizes the test samples, records the test evaluation in the form of a dictionary obtained using utils.eval.py, and visualizes the predictions on random samples. Additionally, it provides the functionality to visualize predictions with GradCams on random samples using utils.interpret.py.
To start the testing process, you can run the following command:
python test_caller.py
File Details
There are two configuration files, configs.cfg_test.yaml and configs.cfg_train.yaml, which contain the necessary variables to configure data, model, directory to save results, and other environment setup.
The data.databuilder.py script defines the preprocessing pipeline using utils.transformer.py and provides the functionality to return a data loader.
The data.dataset.py script is called by data.databuilder.py and reads a CSV file containing columns for file paths and labels.
The data.transformer.py script can be used to add additional transformations for train, validation, and test datasets.
The models.models.py script allows you to define your own model. By default, there are multiple state-of-the-art classification models available.
The utils.eval.py script accepts the true labels and predicted probabilities to compute performance metrics. For binary classification, a single neuron is used to generate a threshold for classification. This threshold is obtained by maximizing the harmonic mean of two vectors using the PR curve or the ROC curve.
The utils.interpret.py script provides functionality to interpret the results generated by the model.
The utils.logger.py script is used to create separate logs if needed.
The utils.loss.py script contains functions for different loss functions, including weighted/non-weighted and binary/multi-class options.
The utils.optimizers.py script defines the optimizer and scheduler for training.
The utils.test.py script contains the main logic for testing the trained model.
The utils.train.py script contains the main logic for training the model.
The utils.visualize.py script provides functionality to visualize random samples and histograms from the cohort.
Output
The results are generated in the ‘results’ directory in the following format: ‘results/
You can view the tensorboard logs using the following command:
tensorboard –logdir=
Simulation
For demonstration purposes, this framework was simulated on the COVID-19 CXR dataset on Kaggle. The dataset was transformed and standardized, and random samples were selected for visualization. Fig.1 shows random CXR samples without preprocessing, where label0 represents normal, label1 represents pneumonia, and label2 represents COVID-19. The values after the labels indicate the index of the data sample.
After resize transformation and standardization, the preprocessed version of the samples in Fig.1 is shown in Fig. 3. The histograms of intensity values for these samples are displayed in Fig. 4.
Predictions generated by the model are shown in Fig. 5, with wrong predictions indicated in red font. GradCam visualizations of the predictions are shown in Fig. 6.
To conclude, this GitHub repository provides a comprehensive framework for model development, training, testing, and interpretation. You can refer to this repository and follow the provided instructions to develop your own models and analyze the results.
Summary: Generating Pytorch Skeleton Code for Binary and Multi-class Classification to Boost SEO and Increase User Appeal
In this post, I will share my GitHub repository that contains code for training, testing, and interpretation of models. The repository includes detailed instructions and explanations for each section, such as setting up the required packages, training the model, and testing the model. It also provides information about the file details, including the configurations, data processing pipeline, model definition, evaluation metrics, and visualization functions. The results generated by the code are stored in a specific directory format, which includes tensorboard logs, model weights, GradCam images, prediction images, and CSV files. The post also includes a simulation of the framework on a COVID-19 CXR dataset, showcasing sample images before and after preprocessing, histograms of intensity values, predictions made by the model, and GradCam visualizations.
Frequently Asked Questions:
1. What is deep learning and how does it differ from traditional machine learning?
Deep learning is a subset of machine learning that emulates the human brain’s neural networks to learn and make decisions on its own. Unlike traditional machine learning, which relies on handcrafted features and algorithms, deep learning utilizes multiple layers of artificial neural networks to automatically extract relevant features from data. This enables deep learning models to learn complex patterns and relationships, making them more powerful and accurate in solving complex problems.
2. What are the main applications of deep learning?
Deep learning has found applications in various domains, including computer vision, natural language processing, speech recognition, and bioinformatics. In computer vision, deep learning models excel at tasks like image recognition, object detection, and image generation. In natural language processing, they enhance language translation, sentiment analysis, and text generation. Deep learning is also utilized in voice assistants, self-driving cars, medical diagnostics, and many other fields.
3. How does deep learning model training work?
Deep learning models are trained using large amounts of labeled data. During the training process, the model adjusts its internal parameters (weights and biases) to minimize the difference between predicted outputs and the true labels. This is accomplished through a technique called backpropagation, where errors are propagated backward through the network to update the weights. Training deep learning models often requires powerful hardware like GPUs and large datasets to achieve optimal performance.
4. What are some challenges and limitations of deep learning?
Despite its remarkable success, deep learning has a few limitations. One major challenge is the need for a considerable amount of labeled data for effective training. In domains where labelling data is expensive or time-consuming, it can be challenging to acquire enough labeled examples. Additionally, deep learning models tend to be computationally intensive and require significant computational resources for training and inference. Interpretability and explainability of deep learning models can also be a challenge, as their internal workings are often considered as black boxes.
5. How can deep learning be applied in business and industries?
Deep learning has immense potential in various industries and businesses. It can be applied to analyze large volumes of data, make accurate predictions, automate processes, improve customer experience, and enhance decision-making. For example, in finance, deep learning models can be used for fraud detection, risk assessment, and stock market forecasting. In retail, they can help in personalized marketing, demand forecasting, and inventory management. Deep learning can bring efficiency, cost savings, and innovation in numerous sectors, making businesses more competitive in the era of artificial intelligence.
