diff options
Diffstat (limited to 'runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp')
-rw-r--r-- | runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp | 59 |
1 files changed, 0 insertions, 59 deletions
diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp deleted file mode 100644 index 7952e34bd..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// Generated file (from: lstm_state2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type8(Type::FLOAT32, {}); - OperandType type7(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0,0}); - OperandType type3(Type::TENSOR_FLOAT32, {0}); - OperandType type9(Type::TENSOR_FLOAT32, {1, 16}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {4}); - // 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(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - 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()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {1, 2, 0}; - return ignore.find(i) != ignore.end(); -} |