Binary Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the DenseNet121 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet121 model’s performance achieved an accuracy score of 99.80% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.90%. Finally, the final model processed the test dataset with an accuracy score of 99.87%.

CONCLUSION: In this iteration, the DenseNet121-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary 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 Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the ResNet50V2 architecture to make predictions.

ANALYSIS: In this iteration, the ResNet50V2 model’s performance achieved an accuracy score of 99.49% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.53%. Finally, the final model processed the test dataset with an accuracy score of 98.51%.

CONCLUSION: In this iteration, the ResNet50V2-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary 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 Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the InceptionV3 architecture to make predictions.

ANALYSIS: In this iteration, the InceptionV3 model’s performance achieved an accuracy score of 99.65% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.24%. Finally, the final model processed the test dataset with an accuracy score of 99.75%.

CONCLUSION: In this iteration, the InceptionV3-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary 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 Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo 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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the VGG16 architecture to make predictions.

ANALYSIS: In this iteration, the VGG16 model’s performance achieved an accuracy score of 96.81% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 94.49%. Finally, the final model processed the test dataset with an accuracy score of 96.56%.

CONCLUSION: In this iteration, the VGG16-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary 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 Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo 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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the DenseNet121 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet121 model’s performance achieved an accuracy score of 99.30% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 96.53%. Finally, the final model processed the test dataset with an accuracy score of 95.64%.

CONCLUSION: In this iteration, the DenseNet121-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary 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 Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo 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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the ResNet50V2 architecture to make predictions.

ANALYSIS: In this iteration, the ResNet50V2 model’s performance achieved an accuracy score of 99.08% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 97.96%. Finally, the final model processed the test dataset with an accuracy score of 95.87%.

CONCLUSION: In this iteration, the ResNet50V2-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary 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 Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo 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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the InceptionV3 architecture to make predictions.

ANALYSIS: In this iteration, the InceptionV3 model’s performance achieved an accuracy score of 99.54% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 97.89%. Finally, the final model processed the test dataset with an accuracy score of 98.62%.

CONCLUSION: In this iteration, the InceptionV3-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary 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.

Multi-Class Image Classification Deep Learning Model for Chinese MNIST Characters Using TensorFlow

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 Chinese MNIST Characters dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The Chinese MNIST dataset uses data collected by Dr. K Nazarpour and Dr. M Chen for a project at Newcastle University. One hundred Chinese nationals took part in data collection. Each participant wrote with a standard black ink pen all 15 numbers in a table with 15 designated regions drawn on a white A4 paper. Each participant repeated this process ten times, with each sheet scanned at the 300×300 pixels resolution. It resulted in a dataset of 15000 images, each representing one character from a set of 15 characters.

In this iteration, we will construct a few simple CNN models to predict the shoe category based on the available images.

ANALYSIS: The one-layer CNN model’s performance achieved an average accuracy score of 92.79% on the test dataset after 15 epochs. The three-layer CNN model processed the same test dataset with an average accuracy measurement of 97.92%.

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

Dataset Used: Chinese MNIST Digit Recognizer

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

Dataset Reference: https://www.kaggle.com/fedesoriano/chinese-mnist-digit-recognizer

One potential source of performance benchmarks: https://data.ncl.ac.uk/articles/Handwritten_Chinese_Numbers/10280831/1

The HTML formatted report can be found here on GitHub.