| // Generated file (from: rnn.mod.py). Do not edit |
| void CreateModel(Model *model) { |
| OperandType type5(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_FLOAT32, {16, 16}); |
| OperandType type1(Type::TENSOR_FLOAT32, {16, 8}); |
| OperandType type3(Type::TENSOR_FLOAT32, {16}); |
| OperandType type4(Type::TENSOR_FLOAT32, {2, 16}); |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 8}); |
| // Phase 1, operands |
| auto input = model->addOperand(&type0); |
| auto weights = model->addOperand(&type1); |
| auto recurrent_weights = model->addOperand(&type2); |
| auto bias = model->addOperand(&type3); |
| auto hidden_state_in = model->addOperand(&type4); |
| auto activation_param = model->addOperand(&type5); |
| auto hidden_state_out = model->addOperand(&type4); |
| auto output = model->addOperand(&type4); |
| // Phase 2, operations |
| static int32_t activation_param_init[] = {1}; |
| model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input, weights, recurrent_weights, bias, hidden_state_in}, |
| {hidden_state_out, output}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored(int i) { |
| static std::set<int> ignore = {0}; |
| return ignore.find(i) != ignore.end(); |
| } |