| /prebuilts/go/darwin-x86/test/fixedbugs/ |
| bug424.go | 9 // at which embedding level it is and in which order
10 // embedding is done.
|
| bug414.go | 7 // Issue 1743: test embedding of imported types with private methods.
|
| /prebuilts/go/linux-x86/test/fixedbugs/ |
| bug424.go | 9 // at which embedding level it is and in which order
10 // embedding is done.
|
| bug414.go | 7 // Issue 1743: test embedding of imported types with private methods.
|
| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| conditioning_utils_impl.py | 77 """Get a dense embedding vector from a one-hot encoding."""
80 embedding = variable_scope.get_variable(
81 'embedding', [num_tokens, embedding_size])
83 embedding, label_id, name='token_to_embedding')
100 embedding_size: The size of the class embedding.
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| embedding_ops_test.py | 251 embedding = embedding_ops.embedding_lookup(p, ids)
253 tf_result = embedding.eval(feed_dict=feed_dict)
256 self.assertShapeEqual(np_result, embedding)
263 embedding = embedding_ops.embedding_lookup(
266 self.assertAllEqual(embedding.eval(), [[1.0]])
273 embedding = embedding_ops.embedding_lookup(
279 self.assertAllEqual(embedding.eval(), 2 * normalized.eval())
291 embedding = embedding_ops.embedding_lookup(p_variable, ids)
297 tf_result = embedding.eval(feed_dict=feed_dict)
301 self.assertShapeEqual(np_result, embedding)
[all...] |
| /prebuilts/go/darwin-x86/test/fixedbugs/bug424.dir/ |
| main.go | 7 // at which embedding level it is and in which order
8 // embedding is done.
|
| /prebuilts/go/linux-x86/test/fixedbugs/bug424.dir/ |
| main.go | 7 // at which embedding level it is and in which order
8 // embedding is done.
|
| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| metric_loss_ops_test.py | 99 embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
108 pdist_matrix = pairwise_distance_np(embedding, squared=True)
140 embeddings=ops.convert_to_tensor(embedding),
155 embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
163 pdist_matrix = pairwise_distance_np(embedding)
193 embeddings=ops.convert_to_tensor(embedding),
522 embedding, labels = blobs
523 embedding = (embedding - embedding.mean(axis=0)) / embedding.std(axis=0
[all...] |
| /prebuilts/go/darwin-x86/src/vendor/golang_org/x/text/unicode/bidi/ |
| bracket.go | 73 // takes the direction type for the start-of-sentence and the embedding level.
196 // b If any strong type (either L or R) matching the embedding direction is
197 // found, set the type for both brackets in the pair to match the embedding
206 // c Otherwise, if a strong type (opposite the embedding direction) is
210 // the embedding direction, then set the type for both brackets in the pair
212 // the embedding direction.
248 // assuming the given embedding direction.
251 // it returns this this type. Otherwise it returns the embedding direction.
262 return dir // type matching embedding direction found
299 // no strong opposite type found before - use embedding (c.2
[all...] |
| core.go | 52 // level is the embedding level of a character. Even embedding levels indicate
100 // close brackets, after normalization). The embedding levels are optional, but
101 // may be supplied to encode embedding levels of styled text.
133 // If no externally supplied paragraph embedding level, use default.
138 // Initialize result levels to paragraph embedding level.
167 // 5) resolving implicit embedding levels
250 // default embedding level when no strong types found is 0.
261 // This stack will store the embedding levels and override and isolated
332 // Push new embedding level, override status, and isolate
[all...] |
| bidi.go | 24 // This API tries to avoid dealing with embedding levels for now. Under the hood
27 // embedding hierarchy, though.
57 // ICU allows the user to define embedding levels. This may be used, for example,
|
| /prebuilts/go/linux-x86/src/vendor/golang_org/x/text/unicode/bidi/ |
| bracket.go | 73 // takes the direction type for the start-of-sentence and the embedding level.
196 // b If any strong type (either L or R) matching the embedding direction is
197 // found, set the type for both brackets in the pair to match the embedding
206 // c Otherwise, if a strong type (opposite the embedding direction) is
210 // the embedding direction, then set the type for both brackets in the pair
212 // the embedding direction.
248 // assuming the given embedding direction.
251 // it returns this this type. Otherwise it returns the embedding direction.
262 return dir // type matching embedding direction found
299 // no strong opposite type found before - use embedding (c.2
[all...] |
| core.go | 52 // level is the embedding level of a character. Even embedding levels indicate
100 // close brackets, after normalization). The embedding levels are optional, but
101 // may be supplied to encode embedding levels of styled text.
133 // If no externally supplied paragraph embedding level, use default.
138 // Initialize result levels to paragraph embedding level.
167 // 5) resolving implicit embedding levels
250 // default embedding level when no strong types found is 0.
261 // This stack will store the embedding levels and override and isolated
332 // Push new embedding level, override status, and isolate
[all...] |
| bidi.go | 24 // This API tries to avoid dealing with embedding levels for now. Under the hood
27 // embedding hierarchy, though.
57 // ICU allows the user to define embedding levels. This may be used, for example,
|
| /external/tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/ |
| seq2seq.py | 34 - embedding_rnn_seq2seq: The basic model with input embedding.
35 - embedding_tied_rnn_seq2seq: The tied model with input embedding.
36 - embedding_attention_seq2seq: Advanced model with input embedding and
40 - one2many_rnn_seq2seq: The embedding model with multiple decoders.
82 def _extract_argmax_and_embed(embedding,
88 embedding: embedding tensor for symbols.
104 emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
242 """RNN decoder with embedding and a pure-decoding option.
248 num_symbols: Integer, how many symbols come into the embedding
[all...] |
| /external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
| helper.py | 261 def __init__(self, inputs, sequence_length, embedding, sampling_probability,
268 embedding: A callable that takes a vector tensor of `ids` (argmax ids),
283 [embedding, sampling_probability]):
284 if callable(embedding):
285 self._embedding_fn = embedding
288 lambda ids: embedding_ops.embedding_lookup(embedding, ids))
495 result through an embedding layer to get the next input.
498 def __init__(self, embedding, start_tokens, end_token):
502 embedding: A callable that takes a vector tensor of `ids` (argmax ids),
512 if callable(embedding)
[all...] |
| beam_search_decoder.py | 170 embedding,
181 embedding: A callable that takes a vector tensor of `ids` (argmax ids),
207 if callable(embedding):
208 self._embedding_fn = embedding
211 lambda ids: embedding_ops.embedding_lookup(embedding, ids))
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_ptb/ |
| rnn_ptb.py | 70 class Embedding(tf.layers.Layer):
71 """An Embedding layer."""
74 super(Embedding, self).__init__(**kwargs)
79 self.embedding = self.add_variable(
87 return tf.nn.embedding_lookup(self.embedding, x)
112 self.embedding = self.track_layer(Embedding(vocab_size, embedding_dim))
136 y = self.embedding(input_seq)
347 "--embedding-dim", type=int, default=200, help="Embedding dimension."
[all...] |
| /prebuilts/go/darwin-x86/test/ |
| notinheap.go | 14 // Types embedding notinheap types must be notinheap.
|
| /prebuilts/go/linux-x86/test/ |
| notinheap.go | 14 // Types embedding notinheap types must be notinheap.
|
| /external/tensorflow/tensorflow/python/ops/ |
| control_flow_ops_test.py | 206 embedding = embedding_ops.embedding_lookup(embedding_matrix + 0.0, [0])
207 cost += math_ops.reduce_sum(embedding)
231 embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
232 cost += math_ops.reduce_sum(embedding)
253 embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
256 lambda: cost + math_ops.reduce_sum(embedding))
267 embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
269 math_ops.reduce_sum(embedding) + math_ops.reduce_sum(embedding) +
270 math_ops.reduce_sum(embedding)) + math_ops.reduce_sum(embedding
[all...] |
| /external/tensorflow/tensorflow/python/eager/ |
| backprop_test.py | 126 embedding = resource_variable_ops.ResourceVariable(
127 initial_value=random_init, dtype=dtypes.float32, name='embedding')
130 tape.watch_variable(embedding)
131 embedded_x = embedding_ops.embedding_lookup(embedding, x)
153 opt.apply_gradients([(grad, embedding)])
154 self.assertAllClose(expected, embedding.read_value())
|
| /external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
| beam_search_decoder_test.py | 340 embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32)
369 embedding=embedding,
|
| /external/capstone/ |
| config.mk | 79 # enable OS X kernel embedding support. If 'CAPSTONE_USE_SYS_DYN_MEM = yes',
|
