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/external/tensorflow/tensorflow/lite/micro/examples/magic_wand/train/
Ddata_load.py39 seq_length): argument
41 self.seq_length = seq_length
66 def pad(self, data, seq_length, dim): argument
71 tmp_data = (np.random.rand(seq_length, dim) - 0.5) * noise_level + data[0]
72 tmp_data[(seq_length -
73 min(len(data), seq_length)):] = data[:min(len(data), seq_length)]
76 tmp_data = (np.random.rand(seq_length, dim) - 0.5) * noise_level + data[-1]
77 tmp_data[:min(len(data), seq_length)] = data[:min(len(data), seq_length)]
85 features = np.zeros((length, self.seq_length, self.dim))
89 padded_data = self.pad(data, self.seq_length, self.dim)
Dtrain.py51 def build_cnn(seq_length): argument
58 input_shape=(seq_length, 3, 1)), # output_shape=(batch, 128, 3, 8)
78 def build_lstm(seq_length): argument
83 input_shape=(seq_length, 3)), # output_shape=(batch, 44)
93 def load_data(train_data_path, valid_data_path, test_data_path, seq_length): argument
95 train_data_path, valid_data_path, test_data_path, seq_length=seq_length)
101 def build_net(args, seq_length): argument
103 model, model_path = build_cnn(seq_length)
105 model, model_path = build_lstm(seq_length)
185 seq_length = 128 variable
[all …]
Dtrain_test.py36 self.seq_length = 128
40 self.seq_length)
48 cnn, cnn_path = build_cnn(self.seq_length)
49 lstm, lstm_path = build_lstm(self.seq_length)
Ddata_load_test.py34 "./data/train", "./data/valid", "./data/test", seq_length=512)
57 padding_data1 = self.loader.pad(original_data1, seq_length=5, dim=2)
58 padding_data2 = self.loader.pad(original_data2, seq_length=5, dim=2)
/external/v8/src/heap/
Dmarking.cc80 CellPrinter() : seq_start(0), seq_type(0), seq_length(0) {} in CellPrinter()
84 seq_length++; in Print()
92 seq_length = 0; in Print()
103 if (seq_length > 0) { in Flush()
105 seq_length * Bitmap::kBitsPerCell); in Flush()
106 seq_length = 0; in Flush()
115 size_t seq_length; member in v8::internal::__anon769e1e5a0111::CellPrinter
/external/tensorflow/tensorflow/python/feature_column/
Dutils.py38 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)
Dsequence_feature_column_integration_test.py138 seq_input, seq_length = sfc.SequenceFeatures([shared_seq])(features)
144 [seq_input, seq_length, non_seq_input])
Dsequence_feature_column.py573 seq_length = fc_utils.sequence_length_from_sparse_tensor(
577 dense_tensor=dense_tensor, sequence_length=seq_length)
/external/tensorflow/tensorflow/core/ops/
Dcudnn_rnn_ops_test.cc42 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()
Dcudnn_rnn_ops.cc86 auto seq_length = c->Dim(input_shape, 0); in __anon4fa58c430302() local
96 auto output_shape = c->MakeShape({seq_length, batch_size, output_size}); in __anon4fa58c430302()
129 auto seq_length = c->Dim(input_shape, 0); in __anon4fa58c430402() local
139 auto output_shape = c->MakeShape({seq_length, batch_size, output_size}); in __anon4fa58c430402()
/external/icu/icu4c/source/i18n/
Dcsr2022.cpp49 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()
/external/tensorflow/tensorflow/compiler/tests/
Dlstm.py127 def RandomInputs(batch_size, seq_length, num_inputs): argument
132 for seq in range(seq_length):
140 def BuildLSTMLayer(batch_size, seq_length, num_inputs, num_nodes): argument
158 x_seq, pad_seq = RandomInputs(batch_size, seq_length, num_inputs)
Dlstm_test.py142 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,
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_CudnnRNNV3.pbtxt20 [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].
Dapi_def_CudnnRNNBackpropV3.pbtxt20 [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].
Dapi_def_CudnnRNN.pbtxt18 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,
Dapi_def_CudnnRNNV2.pbtxt19 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,
Dapi_def_CudnnRNNBackprop.pbtxt17 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,
Dapi_def_CudnnRNNBackpropV2.pbtxt20 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,
/external/tensorflow/tensorflow/python/data/experimental/ops/
Dgrouping.py186 seq_length = element_length_func(*args)
192 math_ops.less_equal(buckets_min, seq_length),
193 math_ops.less(seq_length, buckets_max))
/external/tensorflow/tensorflow/python/ops/
Dctc_ops.py1031 def collapse_repeated(labels, seq_length, name=None): argument
1049 with ops.name_scope(name, "collapse_repeated_labels", [labels, seq_length]):
1051 seq_length = ops.convert_to_tensor(seq_length, name="seq_length")
1062 seq_mask = array_ops.sequence_mask(seq_length, maxlen=maxlen)
1090 math_ops.cast(new_seq_len, seq_length.dtype))
/external/tensorflow/tensorflow/python/kernel_tests/
Dctc_loss_op_test.py582 seq_length=[4, 5, 5])
597 seq_length=constant_op.constant([4, 5, 5], dtype=dtypes.int64))
612 seq_length=[4, 5, 5])
625 seq_length=[5, 4, 3])
638 seq_length=[4, 5, 1])
/external/tensorflow/tensorflow/stream_executor/rocm/
Drocm_dnn.cc1827 MIOpenRnnSequenceTensorDescriptor(int seq_length, int batch_size, in MIOpenRnnSequenceTensorDescriptor() argument
1829 : seq_length_(seq_length), in MIOpenRnnSequenceTensorDescriptor()
1834 if (seq_length <= 0) { in MIOpenRnnSequenceTensorDescriptor()
1836 absl::StrCat("sequence length must be positive: ", seq_length); in MIOpenRnnSequenceTensorDescriptor()
1849 handles_.assign(seq_length, handle); in MIOpenRnnSequenceTensorDescriptor()
1865 int seq_length() const { return seq_length_; } in seq_length() function in stream_executor::gpu::MIOpenRnnSequenceTensorDescriptor
1928 int seq_length = 0; member
1952 model_dims->seq_length = input_desc.seq_length(); in ExtractAndCheckRnnForward()
1972 if (!(output_desc.seq_length() == model_dims->seq_length && in ExtractAndCheckRnnForward()
2020 input_desc.seq_length() /*seqLength*/, input_desc.handles() /*xDesc*/, in CreateRnnWorkspace()
[all …]
/external/tensorflow/tensorflow/python/tpu/
Dtpu_embedding.py1113 seq_length = self._feature_to_config_dict[feature].max_sequence_length
1114 if not seq_length:
1119 table_activations[:, feature_index:(feature_index+seq_length), :])
1120 feature_index = feature_index + seq_length
/external/tensorflow/tensorflow/python/distribute/
Dcustom_training_loop_test.py866 seq_length = 10
868 x_train = np.random.rand(batch_size, seq_length, 1).astype("float32")

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