Md Asadul Islam mdasadul

## cmd.sh
# post_loc.txt contains the json you want to post
# -p means to POST it
# -H adds an Auth header (could be Basic or Token)
# -T sets the Content-Type
# -c is concurrent clients
# -n is the number of requests to run in the test

ab -p post_loc.txt -T application/json -H 'Authorization: Token abcd1234' -c 10 -n 2000 http://example.com/api/v1/locations/

## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## Q-Net Learning Clean.ipynb

      
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                awjuliani
                / Q-Net Learning Clean.ipynb
            
            
              Created
              August 25, 2016 20:30
            
              
                Basic Q-Learning algorithm using Tensorflow
              
          
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## tf_char_rnn.py
"""Character based language modeling with multi-layer GRUs.

To train the model:

  python3 tf_char_rnn.py --mode training \
      --logdir path/to/logdir --corpus path/to/corpus.txt

To generate text from seed words:

  python3 tf_char_rnn.py --mode sampling \

## word2vec_tftut.py
import tensorflow as tf
import numpy as np

corpus_raw = 'He is the king . The king is royal . She is the royal  queen '

# convert to lower case
corpus_raw = corpus_raw.lower()

words = []
for word in corpus_raw.split():
	# post_loc.txt contains the json you want to post
	# -p means to POST it
	# -H adds an Auth header (could be Basic or Token)
	# -T sets the Content-Type
	# -c is concurrent clients
	# -n is the number of requests to run in the test

	ab -p post_loc.txt -T application/json -H 'Authorization: Token abcd1234' -c 10 -n 2000 http://example.com/api/v1/locations/
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)
	"""Character based language modeling with multi-layer GRUs.

	To train the model:

	python3 tf_char_rnn.py --mode training \
	--logdir path/to/logdir --corpus path/to/corpus.txt

	To generate text from seed words:

	python3 tf_char_rnn.py --mode sampling \
	import tensorflow as tf
	import numpy as np

	corpus_raw = 'He is the king . The king is royal . She is the royal queen '

	# convert to lower case
	corpus_raw = corpus_raw.lower()

	words = []
	for word in corpus_raw.split():