├── README.md
├── images
    ├── decoding.gif
    ├── mem_N2N.png
    ├── overview.png
    ├── results.png
    ├── seq2seq.png
    ├── results_1.png
    ├── cnn_encoder.png
    ├── weight_init_2.png
    ├── weight_init_3.png
    ├── weight_norm.png
    ├── luong_attention.jpg
    ├── residual_connection.png
    └── upper_bound_sigmoid.png
├── bpe_utils.py
└── viz_utils.py


/README.md:
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1 | # conv_s2s
2 | 


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/images/decoding.gif:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/decoding.gif


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/images/mem_N2N.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/mem_N2N.png


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/images/overview.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/overview.png


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/images/results.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/results.png


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/images/seq2seq.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/seq2seq.png


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/images/results_1.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/results_1.png


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/images/cnn_encoder.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/cnn_encoder.png


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/images/weight_init_2.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/weight_init_2.png


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/images/weight_init_3.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/weight_init_3.png


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/images/weight_norm.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/weight_norm.png


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/images/luong_attention.jpg:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/luong_attention.jpg


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/images/residual_connection.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/residual_connection.png


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/images/upper_bound_sigmoid.png:
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https://raw.githubusercontent.com/j-min/conv_s2s/HEAD/images/upper_bound_sigmoid.png


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/bpe_utils.py:
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 1 | import re
 2 | import collections
 3 | from pprint import pprint
 4 | 
 5 | 
 6 | def get_stats(vocab):
 7 |     """
 8 |     word frequencies => bigram frequencies
 9 |     
10 |     Arg:
11 |         Dict
12 |             Keys: word
13 |             values: frequencies
14 |     
15 |     Return:
16 |         collections.defaultdict
17 |             keys: bigram
18 |             values: frequencies
19 |     """
20 |     pairs = collections.defaultdict(int)
21 |     for word, freq in vocab.items():
22 |         symbols = word.split()
23 |         for i in range(len(symbols) - 1):
24 |             pairs[(symbols[i], symbols[i+1])] += freq
25 |     return pairs
26 | 
27 | def merge_vocab(pair, v_in):
28 |     """
29 |     Apply single step of BPE to vocabulary
30 |     """
31 |     v_out = {}
32 |     bigram = re.escape(' '.join(pair)) # esacpe non-ascii characters
33 |     p = re.compile(r'(?<!\S)' + bigram + r'(?!\S)')
34 |     for word in v_in:
35 |         w_out = p.sub(''.join(pair), word)
36 |         v_out[w_out] = v_in[word]
37 |     return v_out
38 | 
39 | 


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/viz_utils.py:
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 1 | from IPython.display import clear_output, Image, display, HTML
 2 | import tensorflow as tf
 3 | import numpy as np
 4 | 
 5 | def strip_consts(graph_def, max_const_size=32):
 6 |     """Strip large constant values from graph_def."""
 7 |     strip_def = tf.GraphDef()
 8 |     for n0 in graph_def.node:
 9 |         n = strip_def.node.add() 
10 |         n.MergeFrom(n0)
11 |         if n.op == 'Const':
12 |             tensor = n.attr['value'].tensor
13 |             size = len(tensor.tensor_content)
14 |             if size > max_const_size:
15 |                 tensor.tensor_content = "<stripped %d bytes>"%size
16 |     return strip_def
17 | 
18 | def show_graph(graph_def, max_const_size=32):
19 |     """Visualize TensorFlow graph."""
20 |     if hasattr(graph_def, 'as_graph_def'):
21 |         graph_def = graph_def.as_graph_def()
22 |     strip_def = strip_consts(graph_def, max_const_size=max_const_size)
23 |     code = """
24 |         <script>
25 |           function load() {{
26 |             document.getElementById("{id}").pbtxt = {data};
27 |           }}
28 |         </script>
29 |         <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>
30 |         <div style="height:600px">
31 |           <tf-graph-basic id="{id}"></tf-graph-basic>
32 |         </div>
33 |     """.format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))
34 | 
35 |     iframe = """
36 |         <iframe seamless style="width:1200px;height:620px;border:0" srcdoc="{}"></iframe>
37 |     """.format(code.replace('"', '&quot;'))
38 |     display(HTML(iframe))


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