Searched refs:bias (Results 1 - 25 of 43) sorted by relevance

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/frameworks/ml/nn/runtime/test/specs/
H A Dfully_connected_float_large_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_FLOAT32", "{1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
30 bias:
H A Dfully_connected_float_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_FLOAT32", "{1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 bias: [4]}
H A Dfully_connected_quant8_large_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_INT32", "{1}, 0.04, 0") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
30 bias:
H A Dfully_connected_quant8_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_INT32", "{1}, 0.25f, 0") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 bias: [4]}
H A Dfully_connected_float.mod.py20 bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [4]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dfully_connected_float_large.mod.py20 bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [900000]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dfully_connected_quant8.mod.py20 bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.25f, 0", [4]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dfully_connected_quant8_large.mod.py20 bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.04, 0", [10]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dlocal_response_norm_float_1.mod.py4 bias = Float32Scalar("bias", 9.) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
H A Dlocal_response_norm_float_2.mod.py4 bias = Float32Scalar("bias", 0.) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
H A Dlocal_response_norm_float_3.mod.py4 bias = Float32Scalar("bias", 0.) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
H A Dlocal_response_norm_float_4.mod.py4 bias = Float32Scalar("bias", 9.) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
H A Drnn_state.mod.py26 bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
80 bias: [
H A Dsvdf_state.mod.py27 bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) variable
34 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
56 bias: [],
H A Drnn.mod.py26 bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
80 bias: [
/frameworks/native/services/sensorservice/
H A DCorrectedGyroSensor.cpp59 const vec3_t bias(mSensorFusion.getGyroBias());
61 outEvent->data[0] -= bias.x;
62 outEvent->data[1] -= bias.y;
63 outEvent->data[2] -= bias.z;
/frameworks/ml/nn/common/operations/
H A DNormalization.cpp45 int32_t radius, float bias, float alpha, float beta,
49 radius, bias, alpha, beta,
44 localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius, float bias, float alpha, float beta, float* outputData, const Shape& outputShape) argument
/frameworks/ml/nn/runtime/test/generated/models/
H A Dlocal_response_norm_float_1.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Dlocal_response_norm_float_2.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Dlocal_response_norm_float_3.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Dlocal_response_norm_float_4.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Drnn.model.cpp13 auto bias = model->addOperand(&type3); local
19 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
22 {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
H A Drnn_state.model.cpp13 auto bias = model->addOperand(&type3); local
19 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
22 {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
H A Dsvdf.model.cpp14 auto bias = model->addOperand(&type3); local
21 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
24 {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param},
H A Dsvdf_state.model.cpp14 auto bias = model->addOperand(&type3); local
21 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
24 {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param},

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