class LeNet5(nn.Cell): def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() 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, 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() # use the preceding operators to construct networks def construct(self, x): x = self.max_pool2d(self.relu(self.conv1(x))) x = self.max_pool2d(self.relu(self.conv2(x))) x = self.flatten(x) x = self.relu(self.fc1(x)) x = self.relu(self.fc2(x)) x = self.fc3(x) return x改变上述函数的通道数与卷积核大小,但不影响网络的功能
时间: 2023-11-17 09:07:10 浏览: 171
为了改变通道数和卷积核大小,我们需要修改 `__init__` 函数中的 `self.conv1` 和 `self.conv2` 的参数,并且保证修改后的参数能够使得网络保持原有的功能。具体地,我们可以按照如下步骤进行修改:
1. 将 `self.conv1` 的通道数修改为 8,卷积核大小修改为 3;
2. 将 `self.conv2` 的通道数修改为 32,卷积核大小修改为 3。
修改后的代码如下所示:
```
class LeNet5(nn.Cell):
def __init__(self, num_class=10, num_channel=1):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(num_channel, 8, 3, pad_mode='valid')
self.conv2 = nn.Conv2d(8, 32, 3, pad_mode='valid')
self.fc1 = nn.Dense(32 * 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()
def construct(self, x):
x = self.max_pool2d(self.relu(self.conv1(x)))
x = self.max_pool2d(self.relu(self.conv2(x)))
x = self.flatten(x)
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
```
需要注意的是,我们修改卷积核大小和通道数时需要考虑到网络的层数和输入输出的大小,否则可能会导致网络无法正常运行。在本例中,我们保留了原有的网络层数和输入输出大小,因此可以保证网络的功能不受影响。
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