/external/tensorflow/tensorflow/contrib/feature_column/python/feature_column/ |
D | sequence_feature_column_test.py | 90 vocabulary_size = 3 105 self.assertAllEqual((vocabulary_size, embedding_dimension), shape) 112 key='aaa', num_buckets=vocabulary_size) 118 key='bbb', num_buckets=vocabulary_size) 146 vocabulary_size = 3 155 key='aaa', num_buckets=vocabulary_size) 167 vocabulary_size = 3 191 self.assertAllEqual((vocabulary_size, embedding_dimension), shape) 207 key='aaa', num_buckets=vocabulary_size) 209 key='bbb', num_buckets=vocabulary_size) [all …]
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D | sequence_feature_column.py | 272 key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0, argument 327 vocabulary_size=vocabulary_size,
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/external/tensorflow/tensorflow/python/feature_column/ |
D | sequence_feature_column_test.py | 93 vocabulary_size = 3 108 self.assertAllEqual((vocabulary_size, embedding_dimension), shape) 115 key='aaa', num_buckets=vocabulary_size) 121 key='bbb', num_buckets=vocabulary_size) 148 vocabulary_size = 3 157 key='aaa', num_buckets=vocabulary_size) 171 vocabulary_size = 3 195 self.assertAllEqual((vocabulary_size, embedding_dimension), shape) 211 key='aaa', num_buckets=vocabulary_size) 213 key='bbb', num_buckets=vocabulary_size) [all …]
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D | feature_column_test.py | 3381 key='aaa', vocabulary_file='path_to_file', vocabulary_size=3) 3393 key=('aaa',), vocabulary_file='path_to_file', vocabulary_size=3) 3400 vocabulary_size=3, 3413 vocabulary_size=3, 3426 key='aaa', vocabulary_file=None, vocabulary_size=3) 3431 key='aaa', vocabulary_file='', vocabulary_size=3) 3436 key='aaa', vocabulary_file='file_does_not_exist', vocabulary_size=10) 3451 vocabulary_size=-1) 3456 vocabulary_size=0) 3463 vocabulary_size=self._wire_vocabulary_size + 1) [all …]
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D | feature_column_v2_test.py | 4517 key='aaa', vocabulary_file='path_to_file', vocabulary_size=3) 4529 key=('aaa',), vocabulary_file='path_to_file', vocabulary_size=3) 4536 vocabulary_size=3, 4549 vocabulary_size=3, 4562 key='aaa', vocabulary_file=None, vocabulary_size=3) 4567 key='aaa', vocabulary_file='', vocabulary_size=3) 4572 key='aaa', vocabulary_file='file_does_not_exist', vocabulary_size=10) 4589 vocabulary_size=-1) 4594 vocabulary_size=0) 4601 vocabulary_size=self._wire_vocabulary_size + 1) [all …]
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D | sequence_feature_column.py | 297 key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0, argument 354 vocabulary_size=vocabulary_size,
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D | feature_column.py | 1144 vocabulary_size=None, argument 1229 if vocabulary_size is None: 1234 vocabulary_size = sum(1 for _ in f) 1237 'in the vocabulary_file %s.', vocabulary_size, key, vocabulary_file) 1240 if vocabulary_size < 1: 1255 vocabulary_size=vocabulary_size, 2737 vocab_size=self.vocabulary_size, 2745 return self.vocabulary_size + self.num_oov_buckets
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D | feature_column_v2.py | 1485 vocabulary_size=None, argument 1568 key, vocabulary_file, vocabulary_size, 1576 vocabulary_size=None, argument 1661 if vocabulary_size is None: 1666 vocabulary_size = sum(1 for _ in f) 1669 'in the vocabulary_file %s.', vocabulary_size, key, vocabulary_file) 1672 if vocabulary_size < 1: 1687 vocabulary_size=vocabulary_size, 3569 vocab_size=self.vocabulary_size, 3589 return self.vocabulary_size + self.num_oov_buckets
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/external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
D | basic_decoder_test.py | 130 vocabulary_size = 7 131 cell_depth = vocabulary_size # cell's logits must match vocabulary size 133 start_tokens = np.random.randint(0, vocabulary_size, size=batch_size) 137 embeddings = np.random.randn(vocabulary_size, 139 cell = rnn_cell.LSTMCell(vocabulary_size) 202 vocabulary_size = 7 203 cell_depth = vocabulary_size # cell's logits must match vocabulary size 206 start_tokens = np.random.randint(0, vocabulary_size, size=batch_size) 213 embeddings = np.random.randn(vocabulary_size, 215 cell = rnn_cell.LSTMCell(vocabulary_size) [all …]
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D | basic_decoder_v2_test.py | 132 vocabulary_size = 7 133 cell_depth = vocabulary_size # cell's logits must match vocabulary size 135 start_tokens = np.random.randint(0, vocabulary_size, size=batch_size) 139 embeddings = np.random.randn(vocabulary_size, 142 cell = rnn_cell.LSTMCell(vocabulary_size) 208 vocabulary_size = 7 209 cell_depth = vocabulary_size # cell's logits must match vocabulary size 212 start_tokens = np.random.randint(0, vocabulary_size, size=batch_size) 216 embeddings = np.random.randn(vocabulary_size, 219 cell = rnn_cell.LSTMCell(vocabulary_size) [all …]
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/external/tensorflow/tensorflow/python/tpu/ |
D | tpu_embedding.py | 52 vocabulary_size, argument 81 if not isinstance(vocabulary_size, int) or vocabulary_size < 1: 82 raise ValueError('Invalid vocabulary_size {}.'.format(vocabulary_size)) 96 return super(TableConfig, cls).__new__(cls, vocabulary_size, dimension, 460 table_descriptor.vocabulary_size = table_config.vocabulary_size 526 vocabulary_size=self._table_to_config_dict[table].vocabulary_size, 821 vocabulary_size=table_config.vocabulary_size, 901 vocabulary_size=table_config.vocabulary_size, 909 vocabulary_size=table_config.vocabulary_size, 1087 vocabulary_size, argument [all …]
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D | feature_column_test.py | 84 vocabulary_size = 3 103 self.assertAllEqual((vocabulary_size, embedding_dimension), shape) 122 key='aaa', num_buckets=vocabulary_size) 223 vocabulary_size = 3 244 self.assertAllEqual((vocabulary_size, embedding_dimension), shape) 265 key='aaa', num_buckets=vocabulary_size) 267 key='bbb', num_buckets=vocabulary_size)
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D | _tpu_estimator_embedding.py | 138 vocabulary_size, dimension = column.get_embedding_table_size() 140 vocabulary_size=vocabulary_size,
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/external/tensorflow/tensorflow/examples/tutorials/word2vec/ |
D | word2vec_basic.py | 78 vocabulary_size = 50000 105 vocabulary, vocabulary_size) 175 tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) 181 tf.truncated_normal([vocabulary_size, embedding_size], 184 nce_biases = tf.Variable(tf.zeros([vocabulary_size])) 199 num_classes=vocabulary_size)) 286 for i in xrange(vocabulary_size):
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/external/tensorflow/tensorflow/python/training/ |
D | warm_starting_util_test.py | 597 "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4) 633 "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4) 678 "sc_vocab", vocabulary_file=current_vocab_path, vocabulary_size=2) 701 new_vocab_size=sc_vocab.vocabulary_size, 761 "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4) 821 new_vocab_size=sc_vocab.vocabulary_size, 854 "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6) 882 new_vocab_size=sc_vocab.vocabulary_size, 922 "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6) 945 new_vocab_size=sc_vocab.vocabulary_size, [all …]
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/external/tensorflow/tensorflow/tools/api/golden/v2/ |
D | tensorflow.feature_column.pbtxt | 17 …argspec: "args=[\'key\', \'vocabulary_file\', \'vocabulary_size\', \'dtype\', \'default_value\', \… 53 …argspec: "args=[\'key\', \'vocabulary_file\', \'vocabulary_size\', \'num_oov_buckets\', \'default_…
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/external/tensorflow/tensorflow/tools/api/golden/v1/ |
D | tensorflow.feature_column.pbtxt | 17 …argspec: "args=[\'key\', \'vocabulary_file\', \'vocabulary_size\', \'num_oov_buckets\', \'default_… 61 …argspec: "args=[\'key\', \'vocabulary_file\', \'vocabulary_size\', \'num_oov_buckets\', \'default_…
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/external/tensorflow/tensorflow/examples/udacity/ |
D | 6_lstm.ipynb | 300 "vocabulary_size = len(string.ascii_lowercase) + 1 # [a-z] + ' '\n", 395 " batch = np.zeros(shape=(self._batch_size, vocabulary_size), dtype=np.float)\n", 479 " p = np.zeros(shape=[1, vocabulary_size], dtype=np.float)\n", 485 " b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n", 522 " ix = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", 526 " fx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", 530 " cx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", 534 " ox = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", 541 " w = tf.Variable(tf.truncated_normal([num_nodes, vocabulary_size], -0.1, 0.1))\n", 542 " b = tf.Variable(tf.zeros([vocabulary_size]))\n", [all …]
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D | 5_word2vec.ipynb | 249 "vocabulary_size = 50000\n", 253 " count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n", 434 " tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n", 436 " tf.truncated_normal([vocabulary_size, embedding_size],\n", 438 " softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n", 446 … labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))\n",
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/external/tensorflow/tensorflow/python/keras/preprocessing/ |
D | sequence_test.py | 87 np.arange(3), vocabulary_size=3) 94 np.arange(5), vocabulary_size=5, window_size=1, categorical=True)
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/external/tensorflow/tensorflow/core/protobuf/tpu/ |
D | tpu_embedding_configuration.proto | 14 int32 vocabulary_size = 2; field 53 // of hosts, each of the first "table_descriptor.vocabulary_size % num_hosts"
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