diff --git a/chapter03/lenet/lenet.py b/chapter03/lenet/lenet.py
index 068719da4ee151a94c8df218bbcefe5c0a3b3aeb..1b11096d10380db771fe344d9d257a45b2fd7979 100644
--- a/chapter03/lenet/lenet.py
+++ b/chapter03/lenet/lenet.py
@@ -14,6 +14,7 @@
 # ============================================================================
 """LeNet."""
 import mindspore.nn as nn
+from mindspore.common.initializer import Normal
 
 
 class LeNet5(nn.Cell):
@@ -22,22 +23,21 @@ class LeNet5(nn.Cell):
 
     Args:
         num_class (int): Num classes. Default: 10.
-        channel (int): Num classes. Default: 1.
+        num_channel (int): Num channels. Default: 1.
 
     Returns:
         Tensor, output tensor
     Examples:
-        >>> LeNet(num_class=10, channel=1)
+        >>> LeNet(num_class=10, num_channel=1)
 
     """
-    def __init__(self, num_class=10, channel=1):
+    def __init__(self, num_class=10, num_channel=1):
         super(LeNet5, self).__init__()
-        self.num_class = num_class
-        self.conv1 = nn.Conv2d(channel, 6, 5, pad_mode='valid')
+        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
         self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
-        self.fc1 = nn.Dense(16 * 5 * 5, 120)
-        self.fc2 = nn.Dense(120, 84)
-        self.fc3 = nn.Dense(84, self.num_class)
+        self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
+        self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
+        self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = nn.Flatten()
diff --git a/chapter03/lenet/main.py b/chapter03/lenet/main.py
index 1dc82379a886bdf968e9641bc0e106ee5d1dbac6..efc8cf5a739bb7c982b66209d5912267e961782a 100644
--- a/chapter03/lenet/main.py
+++ b/chapter03/lenet/main.py
@@ -91,7 +91,7 @@ if __name__ == "__main__":
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     network = LeNet5(cfg.num_classes)
-    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     repeat_size = 1
     net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})