Phil 4.22.20

  • Amsterdam, 24 April 2020​
  • This workshop aims to bring together researchers and practitioners from the emerging fields of Graph Representation Learning and Geometric Deep Learning. The workshop will feature invited talks and a poster session. There will be ample opportunity for discussion and networking.​
  • Invited talks will be live-streamed on YouTube: https://www.youtube.com/watch?v=Zf_nLR4kMo4
  • Looking for an online seminar that presents the latest advances in reinforcement learning theory? You just found it! We aim to bring you a virtual seminar (approximately) every Tuesday at 5pm UTC featuring the latest work in theoretical reinforcement learning.

D20

  • Added P-threshold to json file. I’m concerned that everyone is too busy to participate any more. Aaron hasn’t even asked about the project since he got better and is complaining about how overworked he is. Zach seems to be equally busy. If no one steps up by the end of the week, I think it’s time to either take over the project entirely or shut it down.

ACSOS

  • Started working on Antonio’s changes
  • Changed the MappApp so that the trajectory lines are blue

GOES

  • Finish CNN chapter
  • Enable Tensorflow profiling
    • Installed the plugin: pip install tensorboard_plugin_profile
    • Updated setup_tensorboard():
      def setup_tensorboard(dir_str: str, windows_slashes:bool = True) -> List:
          if windows_slashes:
              dir_str = dir_str.replace("/", "\\")
          try:
              shutil.rmtree(dir_str)
          except:
              print("no file {} at {}".format(dir_str, os.getcwd()))
      
          # use TensorBoard, princess Aurora!
          callbacks = [tf.keras.callbacks.TensorBoard(log_dir=dir_str, profile_batch = '500,510')]
          return callbacks
  • Huh. Looks like scipy.misc.imresize() and scipy.misc.imread() are both deprecated and out of the library. Trying opencv
    • pip install opencv-python
    • Here’s how I did it, with some debugging to varify that everything was working correctly thrown in:
      img_names = ['cat.jpg', 'steam-locomotive.jpg']
      img_list = []
      for name in img_names:
          img = cv2.imread(name)
          res = np.array(cv2.resize(img, dsize=(32, 32), interpolation=cv2.INTER_CUBIC))
          cv2.imwrite(name.replace(".jpg","_32x32.jpg"), res)
          img_list.append(res)
      
      imgs = np.transpose(img_list, (0, 2, 1, 3))
      imgs = np.array(img_list) / 255
  • This forced me to go down a transpose() in multiple dimensions rabbit hole that’s worth documenting. First, here’s code that takes some tiny images in an array and transposes them:
    import numpy as np
    
    img_list = [
        # image 1
        [[[10, 20, 30],
          [11, 21, 31],
          [12, 22, 32],
          [13, 23, 33]],
    
         [[255, 255, 255],
          [48, 45, 58],
          [101, 150, 205],
          [255, 255, 255]],
    
         [[255, 255, 255],
          [43, 56, 75],
          [77, 110, 157],
          [255, 255, 255]],
    
         [[255, 255, 255],
          [236, 236, 238],
          [76, 104, 139],
          [255, 255, 255]]],
        # image 2
        [[[100, 200, 300],
          [101, 201, 301],
          [102, 202, 302],
          [103, 203, 303]],
    
         [[159, 146, 145],
          [89, 74, 76],
          [207, 207, 210],
          [212, 203, 203]],
    
         [[145, 155, 164],
          [52, 40, 36],
          [166, 160, 163],
          [136, 132, 134]],
    
         [[61, 56, 60],
          [36, 32, 35],
          [202, 195, 195],
          [172, 165, 177]]]]
    
    np_imgs = np.array(img_list)
    print("np_imgs shape = {}".format(np_imgs.shape))
    
    imgs = np.transpose(img_list, (0, 2, 1, 3))
    print("imgs shape = {}".format(np_imgs.shape))
    #imgs = np.array(imgs) / 255
    
    print("pix 0: \n{}".format(np_imgs[0]))
    print("transposed pix 0: \n{}".format(imgs[0]))
    print("\n------------------------\n")
    print("pix 1: \n{}".format(np_imgs[1]))
    print("transposed pix 1: \n{}".format(imgs[1]))
  • So, this is a complex matrix, with a shape of (2, 4, 4, 3). What we want to do is rotate the images (the inner 4, 4) by 90 degrees by transposing them. The way to understand Numpy’s transpose is that it interchanges two axis. The trick is understanding how.
  • For this matrix, applying a transpose that does nothing means writing this:
    imgs = np.transpose(img_list, (0, 1, 2, 3))
  • Think of it as an identity transpose. What we want to do is reverse the order of the inner 4, 4, which we do like this:
    imgs = np.transpose(img_list, (0, 2, 1, 3))
  • That’s it! Now the second “4” will be transposed with the first “4”. You can do this with any of the elements. So
    imgs = np.transpose(img_list, (3, 2, 1, 0))
  • Reverses everything!
  • Ok, so things are working, but the results are crap. Not really worrying about it for now because it’s CFAR and I always have this problem:
    ./images\airplane.jpg = [8] ship
    ./images\automobile.jpg = [0] airplane 
    ./images\bird.jpg = [4] deer
    ./images\cat.jpg = [0] airplane 
    ./images\cat2.jpg = [6] frog
    ./images\cat3.jpg = [8] ship
    ./images\deer.jpg = [8] ship
    ./images\dog.jpg = [2] bird
    ./images\horse.jpg = [8] ship
    ./images\ship.jpg = [0] airplane 
    ./images\steam-locomotive.jpg = [2] bird
    ./images\truck.jpg = [3] cat
    [8 0 4 0 6 8 8 2 8 0 2 3]

     

  • Meeting

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.