Keras-tutorial-on-CNNs

We're going to build 3 image CNNs using just Tensorflow and Keras. The first is a simple classifier for images that will show you the basics of the keras api and how to build a simple CNN. The second is a classifier that uses pre-trained VGG16 convolutional layers and fine tunes them for a different classification task. The third model is a CNN regressor taht also uses pre-trained VGG16 layers but fine tunes them for the task of face detection. The goal for this is to fully understand how a Convolutional Neural Network works.

Overview

This is the code for the workshop "Implementing CNNs with keras and tensorflow".

Includes 3 different CNN architectures:

1. A simple CNN for image classification

2. A CNN that uses the pretrained layers from VGG16 for image classification

3. A CNN regressor that uses the pretrained layers from VGG16 for the task of face detection

Requirements

• Python
• NumPy
• OpenCV
• scikit-learn
• Tensorflow
• Keras

Run pip install -r requirements.txt to install the requirements.

Install pip here.

Usage

Training

Step 1. Clone this repository with git.

$ git clone https://github.com/AlexGidiotis/Keras-tutorial-on-CNNs.git
$ cd Keras-tutorial-on-CNNs

Step 2. Download celebA data

$ mkdir data/

Step 3. Move the data you downloaded to the data directory you just created.

Step 4. Created a directory to save the trained models.

$ mkdir model/

Step 5. Try training your own models.

Results

• The simple CNN achieves approximately 80% accuracy on the cifar10 data after 50 epochs.

• The CNN with the VGG16 pre-trained layers achieves approximately 91% accuracy on the cifar10 data.

• The CNN regressor achieves approximately 187 Mean Squared Error on the celebA data after 28 epochs.