Homework 01 : Deep learning Tutorial

Transcription

1 Homework 01 : Deep learning Tutorial Introduction to TensorFlow and MLP 1. Introduction You are going to install TensorFlow as a tutorial of deep learning implementation. This instruction will provide a guide for installation and a toy example of TensorFlow. We assumed that your environment is Ubuntu TensorFlow is an open source software library for numerical computation using data flow graphs. It is widely used as a deep learning framework. If you want to know more about TensorFlow, visit the following link. In the end of this instruction, there are two screenshots. You will submit your own results compressed on etl. The file name should be DRL_[Student ID]_hw01.zip. 2. Install TensorFlow cpu version (python2.7) In this instruction, we guide for installation of TensorFlow cpu version on python2.7. If you want to install gpu version or python3 environment, please look at the following link. There are several ways to install TensorFlow on Ubuntu. You may install TensorFlow through pip, which is the simplest installation procedure. 2.1 Install pip package TensorFlow mainly uses Python and it is automatically installed on Ubuntu. You can confirm that python is already installed on your system by python -V command. You should install pip package manager before you start the installation. sudo apt-get install python-pip python-dev 2.2 Install TensorFlow Install TensorFlow by following command. pip install tensorflow

2 2.3 Validate your installation You can run a short TensorFlow program to validate your TensorFlow installation. You have to make a short code by python2.7. python After this command, you are now in the python interactive shell. Then type the following. import tensorflow as tf hello = tf.constant('hello, TensorFlow!') sess = tf.session() print(sess.run(hello)) If the system outputs the following, then you are ready to begin writing TensorFlow program. Hello, TensorFlow! 3. TensorFlow Implementation 3.1 Install Jupyter Notebook You will use Jupyter Notebook as an editor of Python and Tensorflow. You need to install some following packages. sudo pip install numpy sudo pip install matplotlib sudo pip install jupyter Jupyter notebook is run by following command. jupyter notebook Then you will see the web browser that shows your directories.

3 3.2 Run the example code As a tutorial, you will run a multi-layer perceptron (MLP) algorithm for classify images of 5 celebrities. The complete code and dataset are uploaded on the course homepage. You can access the downloaded folder through Jupyter notebook and it will look like follows. Figure 1 Jupyter Notebook directory Open mlp_celeb.ipynb file and you will see as following. Figure 2 Jupyter Notebook editor

4 You can run each block of the code by Shift + Enter. Each block gives output separately. In TensorFlow, you need to define a graph model first, then feed the real data to the graph at run time. - In DEFINE MODEL block, learnable parameters are defined as weights, and biases. You can change the capacity of the model by changing these parameters. - In DEFINE GRAPH block, the shape of the neural network, in this case MLP, is defined. It consists of 2 layers each of which have a linear transformation and a nonlinear activation function, sigmoid function. If you run the whole blocks, then you may see the result like following. Figure 3 Results of the example code The result shows current epoch, cost, training accuracy, test accuracy, and the failure examples. The following images are the failure examples of classification in that epoch.

5 4. Submission Format Try at least 2 different configurations of the given model (include the original code) and capture each of their results. You can try any kind of change in the model. Hints You can change the number of hidden variables in DEFINE MODEL block. If you want to add more layer, you should also add more tf.variable in DEFINE MODEL block. Submit your screenshots of the results and the README.txt file that explains your model. Submit your own results compressed on etl. The file name should be DRL_[Student ID]_hw01.zip. Due to: :59 KST