| /external/llvm/include/llvm/IR/ |
| H A D | Metadata.h | 1034 static MDNode *concatenate(MDNode *A, MDNode *B);
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| /external/swiftshader/third_party/llvm-subzero/include/llvm/IR/ |
| H A D | Metadata.h | 1058 static MDNode *concatenate(MDNode *A, MDNode *B);
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| H A D | hlo_evaluator.cc | 2231 Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) {
2233 concatenate->operands());
2234 // The result concatenate dimension is going to be the sum of all
2235 // concatenate dimensions of the operands taking part of the operation.
2239 const int64 concat_dim = concatenate->dimensions()[0];
2269 evaluated_[concatenate] = std::move(result_literal);
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| /external/tensorflow/tensorflow/contrib/data/python/kernel_tests/ |
| H A D | batch_dataset_op_test.py | 251 dataset = dataset.concatenate(dataset_ops.Dataset.from_tensors(el))
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| H A D | shape_test.py | 58 y = np.reshape(x, np.concatenate([[-1], x_shape[n:]], 0))
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| H A D | math_utils.py | 114 blocked.set_shape(batch_shape.concatenate((blocked_rows, blocked_cols)))
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| /external/tensorflow/tensorflow/python/keras/_impl/keras/engine/ |
| H A D | training_eager_test.py | 301 model.fit(x_train, np.concatenate([y_train, y_train], axis=-1))
|
| H A D | training_test.py | 328 model.fit(x_train, np.concatenate([y_train, y_train], axis=-1))
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| /external/tensorflow/tensorflow/python/ops/ |
| H A D | tensor_array_ops.py | 371 tensor_shape.TensorShape([clengths[0]]).concatenate(
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| H A D | image_ops_impl.py | 1300 rgb.set_shape(images.get_shape()[:-1].concatenate([3]))
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| H A D | rnn_cell_impl.py | 114 shape = tensor_shape.as_shape(p_static).concatenate(s_static)
156 matching structure of Tensors having shape `[batch_size].concatenate(s)`
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| H A D | nn_ops.py | 598 return np.concatenate(parts)
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| /external/llvm/lib/IR/ |
| H A D | Metadata.cpp | 842 /// MDNode::concatenate() and \a MDNode::intersect() to maintain behaviour from
859 MDNode *MDNode::concatenate(MDNode *A, MDNode *B) {
|
| /external/nist-sip/java/gov/nist/javax/sip/message/ |
| H A D | SIPMessage.java | 691 hdrlist.concatenate((SIPHeaderList) h, top);
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| /external/toolchain-utils/cros_utils/ |
| H A D | stats.py | 3104 all = N.concatenate([x, y], 0)
3570 ranked = rankdata(N.concatenate((x, y)))
3620 alldata = N.concatenate((x, y))
4089 alldata = N.concatenate(args)
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| /external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
| H A D | tpu_estimator.py | 2262 dataset = user_provided_dataset.concatenate(final_batch_dataset).prefetch(2)
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| /external/tensorflow/tensorflow/python/feature_column/ |
| H A D | feature_column.py | 2877 (original_rank_dim - 1).value).concatenate(result.get_shape()[1:]))
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| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| H A D | backend_test.py | 422 y = keras.backend.concatenate([a, b], axis=-1)
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| /external/tensorflow/tensorflow/contrib/rnn/python/kernel_tests/ |
| H A D | core_rnn_test.py | 1622 outputs_static_array_double = np.concatenate(
1745 outputs_static_array_double = np.concatenate(
|
| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| H A D | metric_ops_test.py | 2087 labels = np.concatenate([labels, new_labels])
2088 predictions = np.concatenate([predictions, new_predictions])
2291 labels = np.concatenate([labels, new_labels])
2292 predictions = np.concatenate([predictions, new_predictions])
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| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| H A D | recurrent.py | 1797 return K.concatenate([
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| H A D | metrics_test.py | 3120 return (np.concatenate([[p] * int(w) for p, w in zip(predictions, weights)]),
3121 np.concatenate([[l] * int(w) for l, w in zip(labels, weights)]))
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| H A D | feature_column_ops_test.py | 1246 self.assertAllClose(output_2_eval, np.concatenate(
|
| H A D | layers_test.py | 3535 [np.concatenate([x_lin, y_lin], axis=1) for i in range(batch_size)])
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| /external/libphonenumber/demo/war/WEB-INF/lib/ |
| H A D | commons-lang-2.6.jar | META-INF/ META-INF/MANIFEST.MF org/ org/apache/ org/apache/commons/ org/apache/commons/lang/ ... |