Multi-Class Image Classification Deep Learning Model for Flower Photos Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Flower Photos dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The Flower Photos dataset is a collection of 3,670 flower photos in five different species. This dataset is part of the TensorFlow standard dataset collection.

In this Take1 iteration, we will construct and tune a machine learning model using a simple three-layer MLP network. We will also observe the best result that we can obtain using the validation dataset.

ANALYSIS: In this Take1 iteration, the baseline model’s performance achieved an accuracy score of 80.24% after 25 epochs using the training dataset. After tuning the model, the best model processed the validation dataset with an accuracy score of 74.69%.

CONCLUSION: In this iteration, the TensorFlow CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Flower Photos Dataset

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://www.tensorflow.org/datasets/catalog/tf_flowers

One potential source of performance benchmarks: https://www.tensorflow.org/tutorials/images/classification

The HTML formatted report can be found here on GitHub.

Binary Image Classification Deep Learning Model for Yosemite Summer vs. Winter Using TensorFlow Take 4

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Yosemite Summer vs. Winter dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains datasets that consist of images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in the area of General Adversarial Networks.

From iteration Take1, we constructed and tuned a machine learning model for this dataset using TensorFlow with a simple VGG-3 network. We also observed the best result that we could obtain using the test dataset. The final model from this iteration became our baseline model for future iterations.

From iteration Take2, we constructed and tuned a machine learning model for this dataset using TensorFlow with a VGG-16 network. We also observed the best result that we could obtain using the test dataset. The final model from this iteration became our baseline model for future iterations.

From iteration Take3, we constructed and tuned a machine learning model for this dataset using TensorFlow with a Inception V3 network. We also observed the best result that we could obtain using the test dataset. The final model from this iteration became our baseline model for future iterations.

In this Take4 iteration, we will construct and tune a machine learning model for this dataset using TensorFlow with a ResNet50 V2 network. We will also observe the best result that we can obtain using the test dataset and compare the performance with the baseline model.

ANALYSIS: From iteration Take1, the baseline model’s performance achieved an accuracy score of 99.00% after 20 epochs using the training dataset. The final model also processed the test dataset with 74.95% accuracy.

From iteration Take2, the VGG-16 model’s performance achieved an accuracy score of 74.44% after 20 epochs using the training dataset. The final model also processed the test dataset with 70.02% accuracy.

From iteration Take3, the Inception V3 model’s performance achieved an accuracy score of 83.36% after 20 epochs using the training dataset. The final model also processed the test dataset with 52.83% accuracy.

In this Take4 iteration, the ResNet50 V2 model’s performance achieved an accuracy score of 57.48% after 20 epochs using the training dataset. The final model also processed the test dataset with 46.80% accuracy.

CONCLUSION: In this iteration, the ResNet50 V2 CNN model did not appear to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Yosemite Summer vs. Winter Dataset

Dataset ML Model: Binary image classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Image Classification Deep Learning Model for Yosemite Summer vs. Winter Using TensorFlow Take 3

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Yosemite Summer vs. Winter dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains datasets that consist of images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in the area of General Adversarial Networks.

From iteration Take1, we constructed and tuned a machine learning model for this dataset using TensorFlow with a simple VGG-3 network. We also observed the best result that we could obtain using the test dataset. The final model from this iteration became our baseline model for future iterations.

From iteration Take2, we constructed and tuned a machine learning model for this dataset using TensorFlow with a VGG-16 network. We also observed the best result that we could obtain using the test dataset. The final model from this iteration became our baseline model for future iterations.

In this Take3 iteration, we will construct and tune a machine learning model for this dataset using TensorFlow with a Inception V3 network. We will also observe the best result that we can obtain using the test dataset and compare the performance with the baseline model.

ANALYSIS: From iteration Take1, the baseline model’s performance achieved an accuracy score of 99.00% after 20 epochs using the training dataset. The final model also processed the test dataset with 74.95% accuracy.

From iteration Take2, the VGG-16 model’s performance achieved an accuracy score of 74.44% after 20 epochs using the training dataset. The final model also processed the test dataset with 70.02% accuracy.

In this Take3 iteration, the Inception V3 model’s performance achieved an accuracy score of 83.36% after 20 epochs using the training dataset. The final model also processed the test dataset with 52.83% accuracy.

CONCLUSION: In this iteration, the Inception V3 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Yosemite Summer vs. Winter Dataset

Dataset ML Model: Binary image classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

NLP Model for Disaster Tweets Classification Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a text classification model using a neural network and document the end-to-end steps using a template. The Disaster Tweets Classification dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: Twitter has become an important communication channel in times of emergency. The ubiquitous nature of smartphones enables people to announce an emergency they are observing in real-time. Because of this, more agencies are interested in programmatically monitoring Twitter. In this practice Kaggle competition, we want to build a machine learning model that predicts which Tweets are about real disasters and which ones are not. This dataset was created by Figure-Eight and shared initially on their ‘Data for Everyone’ website.

In this Take1 iteration, we will deploy a bag-of-words model to classify the Tweets. We will also submit the test predictions to Kaggle and obtain the performance level of the model.

ANALYSIS: In this Take1 iteration, the bag-of-words model’s performance achieved an average accuracy score of 75.49% after 20 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 75.02%.

CONCLUSION: In this modeling iteration, the bag-of-words TensorFlow model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Sentiment Labelled Sentences

Dataset ML Model: Binary class text classification with text-oriented features

Dataset Reference: https://www.kaggle.com/c/nlp-getting-started/

The HTML formatted report can be found here on GitHub.

Binary Image Classification Deep Learning Model for Yosemite Summer vs. Winter Using TensorFlow Take 2

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Yosemite Summer vs. Winter dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains datasets that consist of images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in the area of General Adversarial Networks.

From iteration Take1, we constructed and tuned a machine learning model for this dataset using TensorFlow with a simple VGG-3 network. We also observed the best result that we could obtain using the test dataset. The final model from this iteration became our baseline model for future iterations.

In this Take2 iteration, we will construct and tune a machine learning model for this dataset using TensorFlow with a VGG-16 network. We will also observe the best result that we can obtain using the test dataset and compare the performance with the baseline model.

ANALYSIS: From iteration Take1, the baseline model’s performance achieved an accuracy score of 99.00% after 20 epochs using the training dataset. The final model also processed the test dataset with 74.95% accuracy.

In this Take2 iteration, the VGG-16 model’s performance achieved an accuracy score of 74.44% after 20 epochs using the training dataset. The final model also processed the test dataset with 70.02% accuracy.

CONCLUSION: In this iteration, the VGG-16 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Yosemite Summer vs. Winter Dataset

Dataset ML Model: Binary image classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Image Classification Deep Learning Model for Yosemite Summer vs. Winter Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Yosemite Summer vs. Winter dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains datasets that consist of images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in the area of General Adversarial Networks.

This Take1 iteration will construct and tune a machine learning model for this dataset using TensorFlow with a simple VGG-3 network. We will also observe the best result that we can obtain using the test dataset. The final model from this iteration will become our baseline model for future iterations.

ANALYSIS: The baseline model’s performance achieved an accuracy score of 99.00% after 20 epochs using the training dataset. The final model also processed the test dataset with 74.95% accuracy.

CONCLUSION: In this iteration, the TensorFlow CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Yosemite Summer vs. Winter Dataset

Dataset ML Model: Binary image classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Image Classification Deep Learning Model for Chest X-Ray Pneumonia Images Using TensorFlow Take 6

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Chest X-Ray Pneumonia dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The dataset contains chest X-ray images selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. The image collection is organized into three folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images with various display resolutions in this collection.

From iteration Take1, we trained a simple three-layer CNN model and used the model’s performance as the baseline.

From iteration Take2, we used the same three-layer CNN model from Take1 and applied it to the same photos with a higher resolution (from 640×480 to 1024×768).

From iteration Take3, we constructed a VGG-16 CNN model and compared it with the baseline model.

From iteration Take4, we constructed an Inception V3 CNN model and compared it with the baseline model.

From iteration Take5, we constructed a MobileNetV3Large CNN model and compared it with the baseline model.

In this Take6 iteration, we will construct a VGG-19 CNN model and compare it with the baseline model.

ANALYSIS: From iteration Take1, the baseline model’s performance achieved an accuracy score of 100% after 20 epochs using the validation dataset. However, the final model processed the test dataset with an accuracy measurement of only 72.91%.

From iteration Take2, the model’s performance achieved an accuracy score of 62.50% after ten epochs using the validation dataset. However, the final model processed the test dataset with an encouraging accuracy measurement of 83.33%.

From iteration Take3, the VGG-16 model’s performance achieved an accuracy score of 87.50% after ten epochs using the validation dataset. However, the final model processed the test dataset with an encouraging accuracy measurement of 76.44%.

From iteration Take4, the Inception v3 model’s performance achieved an accuracy score of 68.75% after 20 epochs using the validation dataset. Moreover, the final model processed the test dataset with an encouraging accuracy measurement of 67.95%.

From iteration Take5, the MobileNetV3Large model’s performance achieved an accuracy score of 100% after ten epochs using the validation dataset. Moreover, the final model processed the test dataset with an encouraging accuracy measurement of 77.72%.

In this Take6 iteration, the VGG-19 model’s performance achieved an accuracy score of 75.00% after ten epochs using the validation dataset. Moreover, the final model processed the test dataset with an encouraging accuracy measurement of 76.12%.

CONCLUSION: In this iteration, the TensorFlow CNN models appeared to be suitable for modeling this dataset, but we need to experiment with the TensorFlow model to improve its accuracy.

Dataset Used: Chest X-Ray Images (Pneumonia) Dataset

Dataset ML Model: Binary image classification with numerical attributes

Dataset References: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia, https://data.mendeley.com/datasets/rscbjbr9sj/2, and http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5.

One potential source of performance benchmarks: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

The HTML formatted report can be found here on GitHub.

NLP Model for Sentiment Labelled Sentences Using TensorFlow Take 8

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a text classification model using a neural network and document the end-to-end steps using a template. The Sentiment Labelled Sentences dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: This dataset was created for the research paper ‘From Group to Individual Labels using Deep Features,’ Kotzias et al., KDD 2015. The paper researchers randomly selected 500 positive and 500 negative sentences from a larger dataset of reviews for each website. The researcher also attempted to choose sentences with a positive or negative connotation as the goal was to avoid selecting neutral sentences.

From iteration Take1, we deployed a bag-of-words model to classify the Amazon dataset’s review comments. We also applied various sequence-to-matrix modes to evaluate the model’s performance.

From iteration Take2, we deployed a word-embedding model to classify the Amazon dataset’s review comments. We also compared the result with the bag-of-word model from the previous iteration.

From iteration Take3, we deployed a bag-of-words model to classify the IMDB dataset’s review comments. We also applied various sequence-to-matrix modes to evaluate the model’s performance.

From iteration Take4, we deployed a word-embedding model to classify the IMDB dataset’s review comments. We also compared the result with the bag-of-word model from the previous iteration.

From iteration Take5, we deployed a bag-of-words model to classify the Yelp dataset’s review comments. We also applied various sequence-to-matrix modes to evaluate the model’s performance.

From iteration Take6, we deployed a word-embedding model to classify the Yelp dataset’s review comments. We also compared the result with the bag-of-word model from the previous iteration.

From iteration Take7, we deployed a bag-of-words model to classify the combined dataset from all three sets of review comments. We also compared the result with the bag-of-word model from the previous iteration.

In this Take8 iteration, we will deploy a word-embedding model to classify the combined dataset from all three sets of review comments. We will also compare the result with the models from previous iterations.

ANALYSIS: From iteration Take1, the bag-of-words model’s performance achieved an average accuracy score of 77.31% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 71.00%.

From iteration Take2, the word-embedding model’s performance achieved an average accuracy score of 73.25% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 67.00%.

From iteration Take3, the bag-of-words model’s performance achieved an average accuracy score of 77.26% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 68.66%.

From iteration Take4, the word-embedding model’s performance achieved an average accuracy score of 72.84% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 66.00%.

From iteration Take5, the bag-of-words model’s performance achieved an average accuracy score of 75.19% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 72.00%.

From iteration Take6, the word-embedding model’s performance achieved an average accuracy score of 73.44% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 72.50%.

From iteration Take7, the bag-of-words model’s performance achieved an average accuracy score of 76.84% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 78.72%.

In this Take8 iteration, the word-embedding model’s performance achieved an average accuracy score of 60.89% after 25 epochs with ten iterations of cross-validation. Furthermore, the final model processed the test dataset with an accuracy measurement of 75.63%.

CONCLUSION: In this modeling iteration, the word-embedding TensorFlow model did as well as the bag-of-words model. Furthermore, we should continue to experiment with both natural language processing techniques for further modeling.

Dataset Used: Sentiment Labelled Sentences

Dataset ML Model: Binary class text classification with text-oriented features

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences

The HTML formatted report can be found here on GitHub.