1 // Generated file (from: lstm.mod.py). Do not edit 2 void CreateModel(Model *model) { 3 OperandType type8(Type::FLOAT32, {}); 4 OperandType type7(Type::INT32, {}); 5 OperandType type5(Type::TENSOR_FLOAT32, {0,0}); 6 OperandType type3(Type::TENSOR_FLOAT32, {0}); 7 OperandType type9(Type::TENSOR_FLOAT32, {1, 16}); 8 OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); 9 OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); 10 OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); 11 OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); 12 OperandType type4(Type::TENSOR_FLOAT32, {4}); 13 // Phase 1, operands 14 auto input = model->addOperand(&type0); 15 auto input_to_input_weights = model->addOperand(&type1); 16 auto input_to_forget_weights = model->addOperand(&type1); 17 auto input_to_cell_weights = model->addOperand(&type1); 18 auto input_to_output_weights = model->addOperand(&type1); 19 auto recurrent_to_intput_weights = model->addOperand(&type2); 20 auto recurrent_to_forget_weights = model->addOperand(&type2); 21 auto recurrent_to_cell_weights = model->addOperand(&type2); 22 auto recurrent_to_output_weights = model->addOperand(&type2); 23 auto cell_to_input_weights = model->addOperand(&type3); 24 auto cell_to_forget_weights = model->addOperand(&type3); 25 auto cell_to_output_weights = model->addOperand(&type3); 26 auto input_gate_bias = model->addOperand(&type4); 27 auto forget_gate_bias = model->addOperand(&type4); 28 auto cell_gate_bias = model->addOperand(&type4); 29 auto output_gate_bias = model->addOperand(&type4); 30 auto projection_weights = model->addOperand(&type5); 31 auto projection_bias = model->addOperand(&type3); 32 auto output_state_in = model->addOperand(&type6); 33 auto cell_state_in = model->addOperand(&type6); 34 auto activation_param = model->addOperand(&type7); 35 auto cell_clip_param = model->addOperand(&type8); 36 auto proj_clip_param = model->addOperand(&type8); 37 auto scratch_buffer = model->addOperand(&type9); 38 auto output_state_out = model->addOperand(&type6); 39 auto cell_state_out = model->addOperand(&type6); 40 auto output = model->addOperand(&type6); 41 // Phase 2, operations 42 static int32_t activation_param_init[] = {4}; 43 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 44 static float cell_clip_param_init[] = {0.0f}; 45 model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); 46 static float proj_clip_param_init[] = {0.0f}; 47 model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); 48 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}); 49 // Phase 3, inputs and outputs 50 model->identifyInputsAndOutputs( 51 {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}, 52 {scratch_buffer, output_state_out, cell_state_out, output}); 53 assert(model->isValid()); 54 } 55 56 bool is_ignored(int i) { 57 static std::set<int> ignore = {0}; 58 return ignore.find(i) != ignore.end(); 59 } 60
