Tensorflow

On 10th November, I saw the news that Google open sourced Tensorflow. As a programmer that is passionate towards AI, this is a thing that I must try out.

Setup

FYI, setup instruction can be found at here.

Tensorflow with mnist

Put the file at /home/vagrant/notebook/

• Download fully_connected_feed.py
  • replace from tensorflow.g3doc.tutorials.mnist import input_data to import input_data
  • replace from tensorflow.g3doc.tutorials.mnist import input_data to import mnist
• Download input_data.py
• Download mnist.py

Execute python fully_connected_feed.py, it should run and give you the result like this

Step 1000: loss = 0.40 (0.007 sec)
Step 1100: loss = 0.52 (0.087 sec)
Step 1200: loss = 0.46 (0.005 sec)
Step 1300: loss = 0.49 (0.005 sec)
Step 1400: loss = 0.48 (0.006 sec)
Step 1500: loss = 0.37 (0.029 sec)
Step 1600: loss = 0.45 (0.005 sec)
Step 1700: loss = 0.40 (0.005 sec)
Step 1800: loss = 0.39 (0.005 sec)
Step 1900: loss = 0.44 (0.006 sec)
Training Data Eval:
  Num examples: 55000  Num correct: 49219  Precision @ 1: 0.8949
Validation Data Eval:
  Num examples: 5000  Num correct: 4508  Precision @ 1: 0.9016
Test Data Eval:
  Num examples: 10000  Num correct: 8978  Precision @ 1: 0.8978

Graph visualization

Now run tensorboard --logdir=/home/vagrant/notebook/data, open browser at localhost:6006 to view the graph. You should be able to see something like this:

My Review

Not really standout from torch/caffe/etc, I thought it has the ability to drag and drop modify code like Pentaho, however it only has the ability to view the summary graph.

• Overall - Good to use.
• Surprising me? Not.