| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| svdf_state.mod.py | 25 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
38 weights_feature: [
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| svdf.mod.py | 27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size)) variable
36 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
41 weights_feature: [
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| svdf2.mod.py | 27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size)) variable
36 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
41 weights_feature: [
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| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| svdf_state_relaxed.mod.py | 25 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
39 weights_feature: [
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| svdf2_relaxed.mod.py | 27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size)) variable
36 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
42 weights_feature: [
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| svdf_relaxed.mod.py | 27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size)) variable
36 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
42 weights_feature: [
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| /frameworks/ml/nn/runtime/test/generated/models/ |
| svdf.model.cpp | 12 auto weights_feature = model->addOperand(&type1); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
28 {input, weights_feature, weights_time, bias, state_in},
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| svdf2.model.cpp | 12 auto weights_feature = model->addOperand(&type1); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
28 {input, weights_feature, weights_time, bias, state_in},
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| svdf2_relaxed.model.cpp | 12 auto weights_feature = model->addOperand(&type1); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
28 {input, weights_feature, weights_time, bias, state_in},
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| svdf_relaxed.model.cpp | 12 auto weights_feature = model->addOperand(&type1); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
28 {input, weights_feature, weights_time, bias, state_in},
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| svdf_state.model.cpp | 12 auto weights_feature = model->addOperand(&type1); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
28 {input, weights_feature, weights_time, bias, state_in},
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| svdf_state_relaxed.model.cpp | 12 auto weights_feature = model->addOperand(&type1); local
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
28 {input, weights_feature, weights_time, bias, state_in},
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| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| svdf.cc | 61 TfLiteTensor* weights_feature = local
70 const int num_filters = weights_feature->dims->data[0];
74 TF_LITE_ASSERT_EQ(input->dims->data[1], weights_feature->dims->data[1]);
128 TfLiteTensor* weights_feature = local
142 const int num_filters = weights_feature->dims->data[0];
157 // Compute conv1d(inputs, weights_feature).
162 weights_feature->data.f, num_filters, input_size, input->data.f,
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| /frameworks/ml/nn/common/operations/ |
| SVDF.cpp | 67 const RunTimeOperandInfo *weights_feature = local
76 const uint32_t num_filters = SizeOfDimension(weights_feature, 0);
80 NN_CHECK_EQ(SizeOfDimension(input, 1), SizeOfDimension(weights_feature, 1));
115 // Compute conv1d(inputs, weights_feature).
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