step1代码

MindSpore/src/step1/net.py

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+
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+
+def weight_variable():
+    """
+    weight initial
+    """
+    return TruncatedNormal(0.02)
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """
+    conv layer weight initial
+    """
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+def fc_with_initialize(input_channels, out_channels):
+    """
+    fc layer weight initial
+    """
+    # 请在此添加代码完成本关任务
+    # **********Begin*********#
+    ##提示：完成初始化代码
+
+    # **********End**********#
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network structure
+    """
+    #define the operator required
+    def __init__(self):
+        super(LeNet5, self).__init__()
+        self.conv1 = conv(1, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, 10)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    #use the preceding operators to construct networks
+    def construct(self, x):
+        # 请在此添加代码完成本关任务
+        # **********Begin*********#
+        ##提示：根据教程内容完成网络定义即可
+
+        # **********End**********#
+        return x