/external/tensorflow/tensorflow/python/training/ |
D | input_test.py | 478 batched = inp.batch( 485 batched_fetch = [batched["c"], batched["s"], batched["S"]] 487 batched = inp.batch( 489 batched_fetch = batched 527 batched = inp.batch([values], batch_size=2) 531 self.evaluate(batched) 539 batched = inp.batch([values], batch_size=2) 543 self.evaluate(batched) 561 batched = inp.batch( 566 results = self.evaluate(batched) [all …]
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/external/tensorflow/tensorflow/contrib/batching/python/ops/ |
D | batch_ops_test.py | 48 batched, index, _ = batch_ops.batch( 56 sess.run([batched, index], feed_dict={inp: [1]})) 60 main_results = sess.run([batched, index], feed_dict={inp: [2]}) 88 batched, index, _ = batch_ops.batch( 97 sess.run([batched, index], feed_dict={inp: [1, 3]})) 101 main_results = sess.run([batched, index], feed_dict={inp: [2, 4]}) 119 batched, _, _ = batch_ops.batch( 130 sess.run([batched], feed_dict={inp0: [1], 135 main_results = sess.run([batched], feed_dict={inp0: [2], inp1: [3]}) 158 batched, index, _ = batch_ops.batch( [all …]
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/external/tensorflow/tensorflow/contrib/training/python/training/ |
D | sampling_ops.py | 104 batched = input_ops.batch( 110 tensor_list = [array_ops.squeeze(x, [0]) for x in batched] 241 batched = input_ops.batch( 247 val_list = [array_ops.squeeze(x, [0]) for x in batched[:-1]] 248 label = array_ops.squeeze(batched[-1], [0]) 253 batched = input_ops.maybe_batch( 258 return batched[:-1], batched[-1]
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_MatrixDiagPart.pbtxt | 16 summary: "Returns the batched diagonal part of a batched tensor." 19 of the batched `input`. The `diagonal` part is computed as follows:
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D | api_def_BatchFunction.pbtxt | 6 The tensors to be batched. 13 to be batched. 74 the types of tensors to be batched. 117 Assumes that all arguments of the function are Tensors which will be batched 120 Arguments that are captured, are not batched. The session.run call which does
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D | api_def_Batch.pbtxt | 9 All Tensors in in_tensors are batched together (so, for example, labels and 10 features should be batched with a single instance of this operation. 22 in_tensors: The tensors to be batched. 40 T: the types of tensors to be batched.
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D | api_def_MatrixDiag.pbtxt | 15 summary: "Returns a batched diagonal tensor with a given batched diagonal values."
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D | api_def_MatrixSetDiag.pbtxt | 21 summary: "Returns a batched matrix tensor with new batched diagonal values."
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D | api_def_Rpc.pbtxt | 95 from regular batched tensors using the `encode_proto` op, and convert 96 the response `MyResponseProto` serialized protos to batched tensors
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D | api_def_TryRpc.pbtxt | 108 from regular batched tensors using the `encode_proto` op, and convert 109 the response `MyResponseProto` serialized protos to batched tensors
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D | api_def_UnbatchGrad.pbtxt | 14 batched_grad: The return value, either an empty tensor or the batched gradient.
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D | api_def_Unbatch.pbtxt | 18 batched input tensor associated with a given invocation of the op.
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D | api_def_SparseSoftmax.pbtxt | 28 summary: "Applies softmax to a batched N-D `SparseTensor`."
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/external/libaom/libaom/common/ |
D | rawenc.c | 18 static const uint8_t batched[BATCH_SIZE] = { 128, 128, 128, 128, variable 43 const uint8_t *b = batched; in write_greyscale() 57 writer_func(file_or_md5, batched, sizeof(uint8_t), 1); in write_greyscale()
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/external/deqp/modules/glshared/ |
D | glsStateChangePerfTestCases.cpp | 544 ResultStats batched = calculateStats(m_batchedResults); in logAndSetTestResult() local 552 log << TestLog::Message << "Batched mean: " << batched.mean << TestLog::EndMessage; in logAndSetTestResult() 553 log << TestLog::Message << "Batched median: " << batched.median << TestLog::EndMessage; in logAndSetTestResult() 554 …log << TestLog::Message << "Batched variance: " << batched.variance << TestLog::EndMessag… in logAndSetTestResult() 555 log << TestLog::Message << "Batched min: " << batched.min << TestLog::EndMessage; in logAndSetTestResult() 556 log << TestLog::Message << "Batched max: " << batched.max << TestLog::EndMessage; in logAndSetTestResult() 558 …log << TestLog::Message << "Batched/Interleaved mean ratio: " << (interleaved.mean/batched.mean) … in logAndSetTestResult() 559 …log << TestLog::Message << "Batched/Interleaved median ratio: " << (interleaved.median/batched.med… in logAndSetTestResult() 561 …ST_RESULT_PASS, de::floatToString((float)(((double)interleaved.median) / batched.median), 2).c_str… in logAndSetTestResult()
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/external/tensorflow/tensorflow/python/data/experimental/kernel_tests/ |
D | get_single_element_test.py | 68 batched = ds.batch(2) 69 element = get_single_element.get_single_element(batched)
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/external/tensorflow/tensorflow/python/kernel_tests/random/ |
D | multinomial_op_test.py | 147 batched = (len(vec.shape) == 2) 148 return vec / vec.sum(axis=1, keepdims=True) if batched else vec / vec.sum()
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/external/swiftshader/third_party/LLVM/docs/HistoricalNotes/ |
D | 2002-06-25-MegaPatchInfo.txt | 47 include every .h file that it used. Now things are batched a little bit more
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/external/llvm/docs/HistoricalNotes/ |
D | 2002-06-25-MegaPatchInfo.txt | 47 include every .h file that it used. Now things are batched a little bit more
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/external/swiftshader/third_party/llvm-7.0/llvm/docs/HistoricalNotes/ |
D | 2002-06-25-MegaPatchInfo.txt | 47 include every .h file that it used. Now things are batched a little bit more
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/external/libjpeg-turbo/ |
D | ChangeLog.md | 1181 crop operations to be batched together, so the source coefficients only need to
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/external/tensorflow/ |
D | RELEASE.md | 1182 Direct batched convolution
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/external/jline/src/src/test/resources/jline/example/ |
D | english.gz |
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