x = nn.Flatten()(x)后x是什么格式

在 PyTorch 中，`nn.Flatten()` 可以将输入张量展平为一维张量。因此，如果 `x` 是一个形状为 `(batch_size, C, H, W)` 的四维张量，其中 `batch_size` 是批量大小，`C` 是通道数，`H` 和 `W` 是高度和宽度，那么 `x = nn.Flatten()(x)` 将 `x` 展平为一个形状为 `(batch_size, C*H*W)` 的二维张量。该张量的第一维是批量大小，第二维是展平后的特征数。

相关问题

class LeNet5_2(nn.Cell): # 定义算子 def __init__(self, num_class=10, num_channel=3): super(LeNet5_2, self).__init__() # 卷积层 self.conv1 = nn.Conv2d(num_channel, 64, 3, pad_mode='valid', weight_init=Normal(0.06)) self.conv2 = nn.Conv2d(64, 64, 3, pad_mode='valid', weight_init=Normal(0.06)) self.conv3 = nn.Conv2d(64, 128, 3, pad_mode='valid', weight_init=Normal(0.06)) # 全连接层 self.fc1 = nn.Dense(128* 2* 2, 120, weight_init=Normal(0.06)) self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.06)) self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.06)) # 激活函数 self.relu = nn.ReLU() # 最大池化成 self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) # 网络展开 self.flatten = nn.Flatten() #? self.num_class = num_class # 构建网络 def construct(self, x): x = self.conv1(x) x = self.relu(x) x = self.max_pool2d(x) x = self.conv2(x) x = self.relu(x) x = self.max_pool2d(x) x = self.conv3(x) x = self.relu(x) x = self.max_pool2d(x) x = self.flatten(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x 添加批量归一层

可以在LeNet5_2类的__init__方法中添加BatchNorm2d层来实现批量归一化操作，如下所示：

class LeNet5_2(nn.Cell):
    def __init__(self, num_class=10, num_channel=3):
        super(LeNet5_2, self).__init__()
        # 卷积层
        self.conv1 = nn.Conv2d(num_channel, 64, 3, pad_mode='valid', weight_init=Normal(0.06))
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 64, 3, pad_mode='valid', weight_init=Normal(0.06))
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, 3, pad_mode='valid', weight_init=Normal(0.06))
        self.bn3 = nn.BatchNorm2d(128)
        
        # 全连接层
        self.fc1 = nn.Dense(128*2*2, 120, weight_init=Normal(0.06))
        self.bn4 = nn.BatchNorm1d(120)
        self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.06))
        self.bn5 = nn.BatchNorm1d(84)
        self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.06))
        
        # 激活函数
        self.relu = nn.ReLU()
        
        # 最大池化层
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # 网络展开
        self.flatten = nn.Flatten()
        
        self.num_class = num_class
    
    # 构建网络
    def construct(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.bn4(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.bn5(x)
        x = self.relu(x)
        x = self.fc3(x)
        
        return x

其中，BatchNorm2d层用于卷积层的批量归一化操作，BatchNorm1d层用于全连接层的批量归一化操作。在构建网络时，需要将批量归一化层添加到对应的卷积层或全连接层后面。

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

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。