1 // Generated file (from: rnn.mod.py). Do not edit 2 void CreateModel(Model *model) { 3 OperandType type5(Type::INT32, {}); 4 OperandType type2(Type::TENSOR_FLOAT32, {16, 16}); 5 OperandType type1(Type::TENSOR_FLOAT32, {16, 8}); 6 OperandType type3(Type::TENSOR_FLOAT32, {16}); 7 OperandType type4(Type::TENSOR_FLOAT32, {2, 16}); 8 OperandType type0(Type::TENSOR_FLOAT32, {2, 8}); 9 // Phase 1, operands 10 auto input = model->addOperand(&type0); 11 auto weights = model->addOperand(&type1); 12 auto recurrent_weights = model->addOperand(&type2); 13 auto bias = model->addOperand(&type3); 14 auto hidden_state_in = model->addOperand(&type4); 15 auto activation_param = model->addOperand(&type5); 16 auto hidden_state_out = model->addOperand(&type4); 17 auto output = model->addOperand(&type4); 18 // Phase 2, operations 19 static int32_t activation_param_init[] = {1}; 20 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output}); 22 // Phase 3, inputs and outputs 23 model->identifyInputsAndOutputs( 24 {input, weights, recurrent_weights, bias, hidden_state_in}, 25 {hidden_state_out, output}); 26 assert(model->isValid()); 27 } 28 29 bool is_ignored(int i) { 30 static std::set<int> ignore = {0}; 31 return ignore.find(i) != ignore.end(); 32 } 33
