diff --git a/tests/st/networks/models/resnet50/test_resnet50_imagenet.py b/tests/st/networks/models/resnet50/test_resnet50_imagenet.py
index 64f4adda99..c88af6bcf7 100644
--- a/tests/st/networks/models/resnet50/test_resnet50_imagenet.py
+++ b/tests/st/networks/models/resnet50/test_resnet50_imagenet.py
@@ -178,20 +178,18 @@ def train_process(q, device_id, epoch_size, device_num, enable_hccl):
                                  net.trainable_params()))
     no_decayed_params = [param for param in net.trainable_params() if param not in decayed_params]
     group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
-                    {'params': no_decayed_params},
+                    {'params': no_decayed_params, 'weight_decay': 0.0},
                     {'order_params': net.trainable_params()}]
 
     if config.use_lars:
         momentum = nn.Momentum(group_params, lr, config.momentum,
-                               weight_decay=config.weight_decay, loss_scale=config.loss_scale,
-                               use_nesterov=config.use_nesterov)
+                               loss_scale=config.loss_scale, use_nesterov=config.use_nesterov)
         opt = nn.LARS(momentum, epsilon=config.lars_epsilon, coefficient=config.lars_coefficient,
                       lars_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'bias' not in x.name)
 
     else:
         opt = nn.Momentum(group_params, lr, config.momentum,
-                          weight_decay=config.weight_decay, loss_scale=config.loss_scale,
-                          use_nesterov=config.use_nesterov)
+                          loss_scale=config.loss_scale, use_nesterov=config.use_nesterov)
 
     # model
     model = Model(net, loss_fn=loss, optimizer=opt,