Suyash Damle suyashdamle

## mxnet_custom_iterator_generator.py
'''
- For generator: (creates set of fake images in NCHW format ;
    2nd channel being label encodings)
    - Generate random integers between 0 and 9 (of 1 X batch_size shape).
        These would be our labels for fake images
    - Convert these into one-hot encoding : --> (batch_size, 10 )
    - Broadcast to (batch_size X 10 X 10 shape) --> (batch_size, 10, 10)
    - Add another dimension for channel: --> (batch_size, 1, 10, 10)
    - Generate a 10X10 noise matrix using mx.random.normal() --> (batch_size, 1, 10, 10)
    - Concatenate the label encodings with noise: --> (batch_size, 2, 10, 10)

## openai_gym_video.ipynb

      
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                suyashdamle
                / openai_gym_video.ipynb
            
            
              Last active
              May 19, 2019 14:39
            
              
                OpenAI_gym_video.ipynb
              
          
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## client.py
import socket

HOST = 'localhost'  # The server's hostname or IP address
PORT = 64532        # The port used by the server

use_tcp = False

if not use_tcp:

	################# UDP CLIENT ####################

## Finding data packet boundaries using struct.ipynb

      
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                suyashdamle
                / Finding data packet boundaries using struct.ipynb
            
            
              Created
              April 7, 2019 13:08
            
              
                Finding message boundaries over the TCP sockets by appending length of message
              
          
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## trim.py
import os
import numpy as np
import cv2
from scipy.misc import imsave

DIR = './'
LEFT=190
RIGHT=1246
TOP=0
BOTTOM=590
	'''
	- For generator: (creates set of fake images in NCHW format ;
	2nd channel being label encodings)
	- Generate random integers between 0 and 9 (of 1 X batch_size shape).
	These would be our labels for fake images
	- Convert these into one-hot encoding : --> (batch_size, 10 )
	- Broadcast to (batch_size X 10 X 10 shape) --> (batch_size, 10, 10)
	- Add another dimension for channel: --> (batch_size, 1, 10, 10)
	- Generate a 10X10 noise matrix using mx.random.normal() --> (batch_size, 1, 10, 10)
	- Concatenate the label encodings with noise: --> (batch_size, 2, 10, 10)
	import socket

	HOST = 'localhost' # The server's hostname or IP address
	PORT = 64532 # The port used by the server

	use_tcp = False

	if not use_tcp:

	################# UDP CLIENT ####################
	import os
	import numpy as np
	import cv2
	from scipy.misc import imsave

	DIR = './'
	LEFT=190
	RIGHT=1246
	TOP=0
	BOTTOM=590