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  /external/tensorflow/tensorflow/examples/learn/
text_classification_cnn.py 37 n_words = 0 variable
45 # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
49 features[WORDS_FEATURE], vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
105 global n_words
119 n_words = len(vocab_processor.vocabulary_)
120 print('Total words: %d' % n_words)
text_classification.py 32 n_words = 0 variable
65 WORDS_FEATURE, num_buckets=n_words)
79 # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
83 features[WORDS_FEATURE], vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
105 global n_words
126 n_words = len(vocab_processor.vocabulary_)
127 print('Total words: %d' % n_words)
  /external/tensorflow/tensorflow/examples/tutorials/word2vec/
word2vec_basic.py 92 def build_dataset(words, n_words):
95 count.extend(collections.Counter(words).most_common(n_words - 1))
  /prebuilts/gdb/darwin-x86/share/pretty-printers/stlport/stlport/
printers.py 129 n_words = words.type.sizeof / word_size
132 n_words = 1
137 while word < n_words:
  /prebuilts/gdb/linux-x86/share/pretty-printers/stlport/stlport/
printers.py 129 n_words = words.type.sizeof / word_size
132 n_words = 1
137 while word < n_words:
  /prebuilts/python/darwin-x86/2.7.5/share/pretty-printers/stlport/stlport/
printers.py 128 n_words = words.type.sizeof / word_size
131 n_words = 1
136 while word < n_words:
  /prebuilts/python/linux-x86/2.7.5/share/pretty-printers/stlport/stlport/
printers.py 129 n_words = words.type.sizeof / word_size
132 n_words = 1
137 while word < n_words:

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