B. Deep Learning

  • 0. Environment

    In this blog, we are going to use Keras(A Python Deep Learning Library) to implement our deep learning model. It is compatible with Python 2.7-3.5. Keras uses Tensorflow, Theano, or CNTK as its backend engines, so only need to install one of them.

  • 1. Neural Network

    In this blog, we are going to use Neural Network to do image classification. The following figure shows a simple example of Neural Network. If you are interested in this field, please see this review or recently this review.

  • 2. CNN

    In this blog, we are going to use Convolutional Neural Network (CNN) to do image classification. The following figure shows the comparison between a 3-layer Neural Network and a simple Convolutional Neural Network.

  • 3. Image Cpation

    In this blog, we are going to use LSTMs (Long Short Term Memory Networks) to generate a caption for a given image. LSTMs are a special kind of Recurrent Neural Networks (RNN).

  • 4. Train a classifier

    How to train an object classifier using our own images 1. Info: I prepared two python scripts (retrain.py, predict.py) for this task. retrain.py: used to train the classifier. predict.py: used to load the trained model and test on new images.

  • 5. Model on Android

    How to use a pre-trained mode on an Android device In previous chapter, we discussed how to train an object classifier using our own images. At the end, we got trained model and labels file (retrained_graph.

  • 6. Boat Classifier

    In this chapter, I list all materials related to the trained classifier which is used to classify an image into 5 different types of boats: iceboat shrimper patrol boat fishing boat weather ship 0.

Last updated on Jan 24, 2021