| // clang-format off |
| // Generated file (from: lstm2_state.mod.py). Do not edit |
| void CreateModel(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); |
| OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); |
| OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); |
| OperandType type3(Type::TENSOR_FLOAT32, {0}); |
| OperandType type4(Type::TENSOR_FLOAT32, {4}); |
| OperandType type5(Type::TENSOR_FLOAT32, {0, 0}); |
| OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); |
| OperandType type7(Type::INT32, {}); |
| OperandType type8(Type::FLOAT32, {}); |
| OperandType type9(Type::TENSOR_FLOAT32, {1, 12}); |
| // Phase 1, operands |
| auto input = model->addOperand(&type0); |
| auto input_to_input_weights = model->addOperand(&type1); |
| auto input_to_forget_weights = model->addOperand(&type1); |
| auto input_to_cell_weights = model->addOperand(&type1); |
| auto input_to_output_weights = model->addOperand(&type1); |
| auto recurrent_to_intput_weights = model->addOperand(&type2); |
| auto recurrent_to_forget_weights = model->addOperand(&type2); |
| auto recurrent_to_cell_weights = model->addOperand(&type2); |
| auto recurrent_to_output_weights = model->addOperand(&type2); |
| auto cell_to_input_weights = model->addOperand(&type3); |
| auto cell_to_forget_weights = model->addOperand(&type4); |
| auto cell_to_output_weights = model->addOperand(&type4); |
| auto input_gate_bias = model->addOperand(&type4); |
| auto forget_gate_bias = model->addOperand(&type4); |
| auto cell_gate_bias = model->addOperand(&type4); |
| auto output_gate_bias = model->addOperand(&type4); |
| auto projection_weights = model->addOperand(&type5); |
| auto projection_bias = model->addOperand(&type3); |
| auto output_state_in = model->addOperand(&type6); |
| auto cell_state_in = model->addOperand(&type6); |
| auto activation_param = model->addOperand(&type7); |
| auto cell_clip_param = model->addOperand(&type8); |
| auto proj_clip_param = model->addOperand(&type8); |
| auto scratch_buffer = model->addOperand(&type9); |
| auto output_state_out = model->addOperand(&type6); |
| auto cell_state_out = model->addOperand(&type6); |
| auto output = model->addOperand(&type6); |
| // Phase 2, operations |
| static int32_t activation_param_init[] = {4}; |
| model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); |
| static float cell_clip_param_init[] = {0.0f}; |
| model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); |
| static float proj_clip_param_init[] = {0.0f}; |
| model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); |
| model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, |
| {scratch_buffer, output_state_out, cell_state_out, output}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored(int i) { |
| static std::set<int> ignore = {0}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |