diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index 21862089944e..d53647cc684c 100644
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -266,11 +266,12 @@ def _convert_dense(
     # In case of RNN dense, input shape will be (1, 1, n)
     if input_dim > 2:
         input_shape = tuple(dim if dim else 1 for dim in _as_list(input_shape)[0])
-        if input_dim != 3 or input_shape[0] != 1 or input_shape[1] != 1:
-            raise tvm.error.OpAttributeInvalid(
-                f"Input shape {input_shape} is not valid for operator Dense."
-            )
-        inexpr = _op.squeeze(inexpr, axis=[0])
+        # Keras has no limitations on the shape of the input tensor. But our
+        # dense op expects 2D input. All inputs with number of dimensions > 2
+        # are reshaped all "batch" axes into one.
+        # For example: (N, d1, d2, d3) -> (N * d1 * d2, d3)
+        new_batch_size = np.prod(input_shape[:-1])
+        inexpr = _op.reshape(inexpr, newshape=(new_batch_size, input_shape[-1]))
     out = _op.nn.dense(data=inexpr, **params)
     if keras_layer.use_bias:
         bias = etab.new_const(weightList[1])
@@ -283,7 +284,8 @@ def _convert_dense(
     if act_type != "linear":
         out = _convert_activation(out, act_type, etab, data_layout)
     if input_dim > 2:
-        out = _op.expand_dims(out, axis=0)
+        out_shape = (*input_shape[:-1], units)
+        out = _op.reshape(out, newshape=out_shape)
     return out
 
 
diff --git a/tests/python/frontend/keras/test_forward.py b/tests/python/frontend/keras/test_forward.py
index aef137e634a7..0d05e34a155b 100644
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -285,6 +285,16 @@ def test_forward_dense(self, keras_mod):
         keras_model = keras_mod.models.Model(data, x)
         verify_keras_frontend(keras_model, need_transpose=False)
 
+        data = keras_mod.layers.Input(shape=(120, 2560), name="image_set")
+        x = keras_mod.layers.Dense(1, activation="linear", name="e")(data)
+        keras_model = keras_mod.models.Model(data, x)
+        verify_keras_frontend(keras_model, need_transpose=False)
+
+        data = keras_mod.layers.Input(shape=(10, 12, 2560), name="image_set")
+        x = keras_mod.layers.Dense(32, activation="linear", name="e")(data)
+        keras_model = keras_mod.models.Model(data, x)
+        verify_keras_frontend(keras_model, need_transpose=False)
+
     def test_forward_permute(self, keras_mod):
         data = keras_mod.layers.Input(shape=(2, 3, 4))
         x = keras_mod.layers.Permute([2, 3, 1])(data)