Use mxnet to classify images of birds (second episode)

Using one convolutional-layer and two fully-connected-layers cost too much memory and also have bad performance for training, therefore I modify the model to two convolutional-layers and two narrow fully-connected-layers:

and training it by using learning rate “0.1” instead of “0.01” which may cause “overfit” in neural network.
Finally, the model occupied only 6MB disk space (It was more than 200MB before).

Now I could build a web site on a virtual machine of AliCloud (which is sponsored by Allen Mei, my old colleague) to let users uploading birds’ image and classifying it freely. To thank my sponsor, I named the web site “Allen’s bird” 🙂

In this web, I use angularjs and ngImgCrop plugin from “Alex Kaul”. They are powerful and convenient.

The append() operation of np.array() is very slow.
After replacing np.array() by normal python array, the training script could run much faster now.

Use mxnet to classify images of birds (first episode)

Recently, I was trying to classify images of birds by using machine learning technology. The most familiar deep learning library for me is the mxnet, so I use its python interface to build my Birds-Classification-System.
For having not sufficient number of images for all kinds of bird, I just collect three types of them: “Loggerhead Shrike”, “Anhinga”, and “Eastern Meadowlark”.

Loggerhead Shrike Anhinga Eastern Meadowlark

After collecting more than 800 images of the three kinds of bird, I started to write my python code by learning the “Handwritten Digital Sample” of mxnet step by step.
Firstly, using PIL (Python Image Library) to preprocess these images – chop them from rectangle to square with 100 pixels length of edge:

Then put all images into a numpy array and label them:

Now I can build the Convolutional Neural Network model easily by using the powerful mxnet. The CNN will slice all pictures to 8×8 pixels small chunk with 2 pixels step, therefore enhance the small features of these birds, such as black-eye-mask of Loggerhead-Shrike, yellow neck of Eastern-Meadowlark, etc.

Training the data:

Using GPU for training is extremely fast – it only cost me 5 minutes to train all 800 images, although adjusting the parameters of CNN cost me more than 3 days 🙂

Firstly I use Fully Connected Neural Network, it costs a lot of time for training but prone to overfit. After using the CNN with BatchNorm() in mxnet, the speed of training and affect of classification advanced significantly.
CNN(Convolutional Neural Network) is really a ace in deep learning area for images!

A CUDA program to test performance of GPU

For testing performance of our Nvidia GPU, I have to write my first CUDA program to mutiply two Vectors with each size of 2GB:

Luckily, it works 🙂
The cudaMemcpy() cost about 1 second, but the multiplication of two Vectors cost only 80 micro seconds (even with 10 LOOP as default). Therefore I reckon GPU is perfect for training of Machine Learning, but not promising for predicting when Model has been built.

Note: Use cudaMalloc()/cudaMemcpy() instead of malloc()/memcpy() in Standard C Library, or else the program will not run VecMul<<<>>>