diff --git a/tfjs-layers/src/engine/container.ts b/tfjs-layers/src/engine/container.ts
index 088c774dc83..f6d1d74508a 100644
--- a/tfjs-layers/src/engine/container.ts
+++ b/tfjs-layers/src/engine/container.ts
@@ -594,12 +594,22 @@ export abstract class Container extends Layer {
   loadWeights(weights: NamedTensorMap, strict = true) {
     const nameToWeight: {[name: string]: LayerVariable} = {};
     let totalWeightsCount = 0;
+    // get weights key from tensor map in order to check if it is from keras v3.
+    // e.g. dense/0
+    const key = Object.keys(weights)[0].split('/');
+    const isKerasSavedModelFormat = !isNaN(parseInt(key[key.length - 1], 10));
+    // Check if weights from keras v3.
     for (const layer of this.layers) {
-      for (const weight of layer.weights) {
-        if (nameToWeight[weight.originalName] != null) {
-          throw new ValueError(`Duplicate weight name: ${weight.originalName}`);
+      for (const [index, weight] of layer.weights.entries()) {
+        // Parse the name to layerName/index.
+        // e.g. dense/0, dense/1, dense_1/0, dense_1/1
+        const parsedName = isKerasSavedModelFormat ?
+            `${weight.name.split('/').slice(0, -1).join('/') + '/'}${index}` :
+            weight.originalName;
+        if (nameToWeight[parsedName] != null) {
+          throw new ValueError(`Duplicate weight name: ${parsedName}`);
         }
-        nameToWeight[weight.originalName] = weight;
+        nameToWeight[parsedName] = weight;
         totalWeightsCount++;
       }
     }
diff --git a/tfjs-layers/src/engine/topology_test.ts b/tfjs-layers/src/engine/topology_test.ts
index 0b7260ddb43..eaa93449eaf 100644
--- a/tfjs-layers/src/engine/topology_test.ts
+++ b/tfjs-layers/src/engine/topology_test.ts
@@ -1087,6 +1087,23 @@ describeMathCPUAndGPU('loadWeightsFromNamedTensorMap', () => {
     expectTensorsClose(denseLayer.weights[1].read(), tensor1d([10, 20]));
   });
 
+  it('load keras weights', () => {
+    const denseLayer =
+        tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});
+    const output = denseLayer.apply(inputTensor) as SymbolicTensor;
+    const model = tfl.model({inputs: inputTensor, outputs: output});
+
+    const namedWeightsMap: NamedTensorMap = {};
+    namedWeightsMap[denseLayer.weights[0].originalName.split('/')[0] + '/0'] =
+        tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+    namedWeightsMap[denseLayer.weights[1].originalName.split('/')[0] + '/1'] =
+        tensor1d([10, 20]);
+    model.loadWeights(namedWeightsMap);
+    expectTensorsClose(
+        denseLayer.weights[0].read(), tensor2d([1, 2, 3, 4, 5, 6], [3, 2]));
+    expectTensorsClose(denseLayer.weights[1].read(), tensor1d([10, 20]));
+  });
+
   it('Mismatching shape throws an error even in non-strict mode', () => {
     const denseLayer =
         tfl.layers.dense({units: 2, useBias: true, name: 'dense_layer'});