diff --git a/tools/converter/source/optimizer/tflitextra/FullConnect.cpp b/tools/converter/source/optimizer/tflitextra/FullConnect.cpp
index da124e525..406bd8ccb 100644
--- a/tools/converter/source/optimizer/tflitextra/FullConnect.cpp
+++ b/tools/converter/source/optimizer/tflitextra/FullConnect.cpp
@@ -30,12 +30,14 @@ public:
                 }
             }
         }
-        MNN_ASSERT(inputs.size() == 3);
+        MNN_ASSERT(inputs.size() >= 2);
         auto input     = inputs[0];
         auto weight    = inputs[1];
-        auto bias      = inputs[2];
         input = _Reshape(input, {0, -1}, NHWC);
-        auto newOutput = _MatMul(input, weight, false, true) + bias;
+        auto newOutput = _MatMul(input, weight, false, true);
+        if (inputs.size() == 3) {
+            newOutput = newOutput + inputs[2];
+        }
         if (activation == tflite::ActivationFunctionType_RELU) {
             newOutput = _Relu(newOutput);
         } else if (activation == tflite::ActivationFunctionType_RELU6) {
diff --git a/tools/converter/source/tflite/liteConverter.cpp b/tools/converter/source/tflite/liteConverter.cpp
index 49d307e28..401d7426e 100644
--- a/tools/converter/source/tflite/liteConverter.cpp
+++ b/tools/converter/source/tflite/liteConverter.cpp
@@ -305,8 +305,6 @@ int tflite2MNNNet(const std::string inputModel, const std::string bizCode,
             op->type      = creator->opType(quantizedModel);
             op->main.type = creator->type(quantizedModel);
             // set default input output index
-            op->inputIndexes.resize(ops[j]->inputs.size());
-            op->outputIndexes.resize(ops[j]->outputs.size());
             auto insertQuantinfo = [&](int idx) {
                 if (quantizedModel != 2) {
                     return;
@@ -327,12 +325,19 @@ int tflite2MNNNet(const std::string inputModel, const std::string bizCode,
                 tensorDescribe->quantInfo->zero = quant->zero_point[0];
                 MNNNetT->extraTensorDescribe.emplace_back(std::move(tensorDescribe));
             };
+            op->inputIndexes.clear();
+            op->outputIndexes.clear();
+
             for (int i = 0; i < ops[j]->inputs.size(); i++) {
-                op->inputIndexes[i] = ops[j]->inputs[i];
+                if (ops[j]->inputs[i] >= 0) {
+                    op->inputIndexes.emplace_back(ops[j]->inputs[i]);
+                }
             }
             for (int i = 0; i < ops[j]->outputs.size(); i++) {
-                op->outputIndexes[i] = ops[j]->outputs[i];
-                insertQuantinfo(ops[j]->outputs[i]);
+                if (ops[j]->outputs[i] >= 0) {
+                    op->outputIndexes.emplace_back(ops[j]->outputs[i]);
+                    insertQuantinfo(ops[j]->outputs[i]);
+                }
             }
             // Run actual conversion
             creator->run(op, ops[j], tensors, tfliteModelBuffer, tfliteOpSet, quantizedModel);