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Searched
full:n_words
(Results
1 - 7
of
7
) sorted by null
/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
:
Completed in 122 milliseconds