| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| H A D | 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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| H A D | 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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| H A D | 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/ |
| H A D | 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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| H A D | 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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| H A D | 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/ |
| H A D | 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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| H A D | 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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| H A D | 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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| H A D | 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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| H A D | 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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| H A D | 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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| /frameworks/ml/nn/common/operations/ |
| H A D | 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).
|