diff --git a/tfjs-layers/src/layers/normalization.ts b/tfjs-layers/src/layers/normalization.ts
index 51ae31572b5..31828e57ad1 100644
--- a/tfjs-layers/src/layers/normalization.ts
+++ b/tfjs-layers/src/layers/normalization.ts
@@ -472,7 +472,7 @@ export interface LayerNormalizationLayerArgs extends LayerArgs {
 
 export class LayerNormalization extends Layer {
   /** @nocollapse */
-  static className = 'BatchNormalization';
+  static className = 'LayerNormalization';
 
   private axis: number|number[];
   readonly epsilon: number;
diff --git a/tfjs-layers/src/layers/normalization_test.ts b/tfjs-layers/src/layers/normalization_test.ts
index a461d966294..a568c24dc30 100644
--- a/tfjs-layers/src/layers/normalization_test.ts
+++ b/tfjs-layers/src/layers/normalization_test.ts
@@ -692,6 +692,17 @@ describe('LayerNormalization Layer: Symbolic', () => {
     const layerPrime = tfl.layers.layerNormalization(tsConfig);
     expect(layerPrime.getConfig()).toEqual(layer.getConfig());
   });
+
+  it('Deserialize model with BatchNorm Layer', async () => {
+    // tslint:disable:max-line-length
+    const modelJSONString =
+        `{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "Dense", "config": {"name": "dense", "trainable": true, "batch_input_shape": [null, 5], "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.2.4-tf", "backend": "tensorflow"}`;
+    // tslint:enable:max-line-length
+    const model = await tfl.models.modelFromJSON(JSON.parse(modelJSONString));
+    const ys = model.predict(zeros([3, 5])) as Tensor;
+    expect(ys.shape).toEqual([3, 1]);
+    expect(model.layers[1].getWeights().length).toEqual(4);
+  });
 });
 
 describeMathCPUAndGPU('LayerNormalization Layer: Tensor', () => {