/external/tensorflow/tensorflow/contrib/cudnn_rnn/python/kernel_tests/ |
D | cudnn_rnn_ops_benchmark.py | 68 seq_length = config["seq_length"] 70 return "y%d_u%d_b%d_q%d" % (num_layers, num_units, batch_size, seq_length) 94 seq_length = config["seq_length"] 100 array_ops.ones([seq_length, batch_size, num_units])) 126 seq_length = config["seq_length"] 129 inputs = array_ops.zeros([batch_size, seq_length, num_units], 150 seq_length = config["seq_length"] 153 inputs = array_ops.zeros([batch_size, seq_length, num_units],
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D | cudnn_rnn_test.py | 164 def SynthesizeInput(self, seq_length, batch_size, seed=1234): argument 173 inputs = np.random.randn(seq_length, batch_size, 629 seq_length = 8 654 inputs, initial_state = model.SynthesizeInput(seq_length, batch_size) 910 def _TestCudnnCompatibleRnnCells(self, num_layers, seq_length, num_units, argument 938 inputs, _ = model.SynthesizeInput(seq_length, batch_size, seed) 967 inference_input = np.random.rand(seq_length, batch_size, 980 dtype, shape=[seq_length, batch_size, input_size]) 1204 batch_size, seq_length, dir_count, dropout, dtype, argument 1235 random_ops.random_uniform([seq_length, batch_size, input_size],
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/external/tensorflow/tensorflow/python/feature_column/ |
D | utils.py | 38 seq_length = math_ops.segment_max(column_ids, segment_ids=row_ids) 45 seq_length = math_ops.cast( 46 math_ops.ceil(seq_length / num_elements), dtypes.int64) 50 n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1] 51 padding = array_ops.zeros(n_pad, dtype=seq_length.dtype) 52 return array_ops.concat([seq_length, padding], axis=0, name=name_scope)
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D | sequence_feature_column.py | 568 seq_length = fc_utils.sequence_length_from_sparse_tensor( 572 dense_tensor=dense_tensor, sequence_length=seq_length)
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/external/tensorflow/tensorflow/core/ops/ |
D | cudnn_rnn_ops_test.cc | 42 int seq_length = 2; in TEST() local 47 std::vector<int> input_shape = {seq_length, batch_size, num_units}; in TEST() 50 std::vector<int> output_shape = {seq_length, batch_size, in TEST() 74 int seq_length = 2; in TEST() local 79 std::vector<int> input_shape = {seq_length, batch_size, num_units}; in TEST() 82 std::vector<int> output_shape = {seq_length, batch_size, in TEST()
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D | cudnn_rnn_ops.cc | 85 auto seq_length = c->Dim(input_shape, 0); in __anone1d60d010302() local 95 auto output_shape = c->MakeShape({seq_length, batch_size, output_size}); in __anone1d60d010302() 128 auto seq_length = c->Dim(input_shape, 0); in __anone1d60d010402() local 138 auto output_shape = c->MakeShape({seq_length, batch_size, output_size}); in __anone1d60d010402()
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/external/v8/src/heap/ |
D | marking.cc | 152 CellPrinter() : seq_start(0), seq_type(0), seq_length(0) {} in CellPrinter() 156 seq_length++; in Print() 164 seq_length = 0; in Print() 175 if (seq_length > 0) { in Flush() 177 seq_length * Bitmap::kBitsPerCell); in Flush() 178 seq_length = 0; in Flush() 187 uint32_t seq_length; member in v8::internal::__anon522e25580111::CellPrinter
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/external/icu/icu4c/source/i18n/ |
D | csr2022.cpp | 49 int32_t seq_length = (int32_t)uprv_strlen((const char *) seq); in match_2022() local 51 if (textLen-i >= seq_length) { in match_2022() 53 while(j < seq_length) { in match_2022() 62 i += seq_length-1; in match_2022()
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/external/tensorflow/tensorflow/compiler/tests/ |
D | lstm.py | 124 def RandomInputs(batch_size, seq_length, num_inputs): argument 129 for seq in range(seq_length): 137 def BuildLSTMLayer(batch_size, seq_length, num_inputs, num_nodes): argument 155 x_seq, pad_seq = RandomInputs(batch_size, seq_length, num_inputs)
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D | lstm_test.py | 142 seq_length = 3 147 x_seq = [constant_op.constant(self._inputs)] * seq_length 162 seq_length = 3 167 x_seq = [constant_op.constant(self._inputs)] * seq_length 169 ] * seq_length 242 out_seq, weights = lstm.BuildLSTMLayer(FLAGS.batch_size, FLAGS.seq_length, 252 '%s_%d_%d_%d_%d' % (name, FLAGS.batch_size, FLAGS.seq_length,
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_CudnnRNNV3.pbtxt | 20 [seq_length, batch_size, input_size]. If time_major is false, the shape is 21 [batch_size, seq_length, input_size]. 33 [seq_length, batch_size, dir * num_units]. If time_major is false, the 34 shape is [batch_size, seq_length, dir * num_units].
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D | api_def_CudnnRNNBackpropV3.pbtxt | 20 [seq_length, batch_size, input_size]. If time_major is false, the shape is 21 [batch_size, seq_length, input_size]. 33 [seq_length, batch_size, dir * num_units]. If time_major is false, the 34 shape is [batch_size, seq_length, dir * num_units].
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D | api_def_CudnnRNN.pbtxt | 18 input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. 27 output: A 3-D tensor with the shape of [seq_length, batch_size,
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D | api_def_CudnnRNNV2.pbtxt | 19 input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. 28 output: A 3-D tensor with the shape of [seq_length, batch_size,
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D | api_def_CudnnRNNBackprop.pbtxt | 17 input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. 26 output: A 3-D tensor with the shape of [seq_length, batch_size,
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D | api_def_CudnnRNNBackpropV2.pbtxt | 20 input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. 29 output: A 3-D tensor with the shape of [seq_length, batch_size,
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/external/tensorflow/tensorflow/python/ops/ |
D | ctc_ops.py | 788 def collapse_repeated(labels, seq_length, name=None): argument 806 [labels, seq_length]): 808 seq_length = ops.convert_to_tensor(seq_length, name="seq_length") 818 seq_mask = array_ops.sequence_mask(seq_length, maxlen=maxlen) 846 math_ops.cast(new_seq_len, seq_length.dtype))
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/external/tensorflow/tensorflow/python/data/experimental/ops/ |
D | grouping.py | 185 seq_length = element_length_func(*args) 191 math_ops.less_equal(buckets_min, seq_length), 192 math_ops.less(seq_length, buckets_max))
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/external/tensorflow/tensorflow/python/kernel_tests/ |
D | ctc_loss_op_test.py | 572 seq_length=[4, 5, 5]) 587 seq_length=constant_op.constant([4, 5, 5], dtype=dtypes.int64)) 602 seq_length=[4, 5, 5]) 615 seq_length=[5, 4, 3]) 628 seq_length=[4, 5, 1])
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/external/tensorflow/tensorflow/contrib/feature_column/python/feature_column/ |
D | sequence_feature_column.py | 510 seq_length = fc_utils.sequence_length_from_sparse_tensor( 514 dense_tensor=dense_tensor, sequence_length=seq_length)
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/external/tensorflow/tensorflow/stream_executor/rocm/ |
D | rocm_dnn.cc | 1783 MIOpenRnnSequenceTensorDescriptor(int seq_length, int batch_size, in MIOpenRnnSequenceTensorDescriptor() argument 1785 : seq_length_(seq_length), in MIOpenRnnSequenceTensorDescriptor() 1790 if (seq_length <= 0) { in MIOpenRnnSequenceTensorDescriptor() 1792 absl::StrCat("sequence length must be positive: ", seq_length); in MIOpenRnnSequenceTensorDescriptor() 1805 handles_.assign(seq_length, handle); in MIOpenRnnSequenceTensorDescriptor() 1821 int seq_length() const { return seq_length_; } in seq_length() function in stream_executor::gpu::MIOpenRnnSequenceTensorDescriptor 1884 int seq_length = 0; member 1908 model_dims->seq_length = input_desc.seq_length(); in ExtractAndCheckRnnForward() 1928 if (!(output_desc.seq_length() == model_dims->seq_length && in ExtractAndCheckRnnForward() 1976 input_desc.seq_length() /*seqLength*/, input_desc.handles() /*xDesc*/, in CreateRnnWorkspace() [all …]
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D | rocm_dnn.h | 56 createRnnSequenceTensorDescriptor(int seq_length, int batch_size,
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/external/tensorflow/tensorflow/tools/api/golden/v2/ |
D | tensorflow.nn.pbtxt | 61 …argspec: "args=[\'labels\', \'seq_length\', \'name\'], varargs=None, keywords=None, defaults=[\'No…
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/external/tensorflow/tensorflow/tools/api/golden/v1/ |
D | tensorflow.nn.pbtxt | 61 …argspec: "args=[\'labels\', \'seq_length\', \'name\'], varargs=None, keywords=None, defaults=[\'No…
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