diff --git a/example/vgg16_cifar10/eval.py b/example/vgg16_cifar10/eval.py
index b034183373..ca2bbd12eb 100644
--- a/example/vgg16_cifar10/eval.py
+++ b/example/vgg16_cifar10/eval.py
@@ -39,7 +39,7 @@ if __name__ == '__main__':
     context.set_context(device_id=args_opt.device_id)
     context.set_context(enable_mem_reuse=True, enable_hccl=False)
 
-    net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes)
+    net = vgg16(num_classes=cfg.num_classes)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
                    weight_decay=cfg.weight_decay)
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
diff --git a/example/vgg16_cifar10/train.py b/example/vgg16_cifar10/train.py
index 32cd344d50..a4aa587c3d 100644
--- a/example/vgg16_cifar10/train.py
+++ b/example/vgg16_cifar10/train.py
@@ -64,7 +64,7 @@ if __name__ == '__main__':
     context.set_context(device_id=args_opt.device_id)
     context.set_context(enable_mem_reuse=True, enable_hccl=False)
 
-    net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes)
+    net = vgg16(num_classes=cfg.num_classes)
     lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=50000 // cfg.batch_size)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay)
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
diff --git a/mindspore/model_zoo/vgg.py b/mindspore/model_zoo/vgg.py
index 6fcd075cc8..f3532fab13 100644
--- a/mindspore/model_zoo/vgg.py
+++ b/mindspore/model_zoo/vgg.py
@@ -14,7 +14,6 @@
 # ============================================================================
 """VGG."""
 import mindspore.nn as nn
-from mindspore.ops import operations as P
 from mindspore.common.initializer import initializer
 import mindspore.common.dtype as mstype
 
@@ -63,8 +62,7 @@ class Vgg(nn.Cell):
     def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1):
         super(Vgg, self).__init__()
         self.layers = _make_layer(base, batch_norm=batch_norm)
-        self.reshape = P.Reshape()
-        self.shp = (batch_size, -1)
+        self.flatten = nn.Flatten()
         self.classifier = nn.SequentialCell([
             nn.Dense(512 * 7 * 7, 4096),
             nn.ReLU(),
@@ -74,7 +72,7 @@ class Vgg(nn.Cell):
 
     def construct(self, x):
         x = self.layers(x)
-        x = self.reshape(x, self.shp)
+        x = self.flatten(x)
         x = self.classifier(x)
         return x
 
@@ -87,20 +85,19 @@ cfg = {
 }
 
 
-def vgg16(batch_size=1, num_classes=1000):
+def vgg16(num_classes=1000):
     """
     Get Vgg16 neural network with batch normalization.
 
     Args:
-        batch_size (int): Batch size. Default: 1.
         num_classes (int): Class numbers. Default: 1000.
 
     Returns:
         Cell, cell instance of Vgg16 neural network with batch normalization.
 
     Examples:
-        >>> vgg16(batch_size=1, num_classes=1000)
+        >>> vgg16(num_classes=1000)
     """
 
-    net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True, batch_size=batch_size)
+    net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True)
     return net