1 files changed, 0 insertions, 60 deletions
diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm3.model.cpp
deleted file mode 100644
index 32f2d012d..000000000
--- a/runtimes/tests/neural_networks_test/generated/models/lstm3.model.cpp
+++ /dev/null
@@ -1,60 +0,0 @@
-// Generated file (from: lstm3.mod.py). Do not edit
-void CreateModel(Model *model) {
-  OperandType type9(Type::FLOAT32, {});
-  OperandType type8(Type::INT32, {});
-  OperandType type5(Type::TENSOR_FLOAT32, {0});
-  OperandType type4(Type::TENSOR_FLOAT32, {16,20});
-  OperandType type6(Type::TENSOR_FLOAT32, {2, 16});
-  OperandType type7(Type::TENSOR_FLOAT32, {2, 20});
-  OperandType type0(Type::TENSOR_FLOAT32, {2, 5});
-  OperandType type10(Type::TENSOR_FLOAT32, {2, 80});
-  OperandType type2(Type::TENSOR_FLOAT32, {20, 16});
-  OperandType type1(Type::TENSOR_FLOAT32, {20, 5});
-  OperandType type3(Type::TENSOR_FLOAT32, {20});
-  // 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(&type3);
-  auto forget_gate_bias = model->addOperand(&type3);
-  auto cell_gate_bias = model->addOperand(&type3);
-  auto output_gate_bias = model->addOperand(&type3);
-  auto projection_weights = model->addOperand(&type4);
-  auto projection_bias = model->addOperand(&type5);
-  auto output_state_in = model->addOperand(&type6);
-  auto cell_state_in = model->addOperand(&type7);
-  auto activation_param = model->addOperand(&type8);
-  auto cell_clip_param = model->addOperand(&type9);
-  auto proj_clip_param = model->addOperand(&type9);
-  auto scratch_buffer = model->addOperand(&type10);
-  auto output_state_out = model->addOperand(&type6);
-  auto cell_state_out = model->addOperand(&type7);
-  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 = {0};
-  return ignore.find(i) != ignore.end();
-}