Question

我正在使用Pytorch文档（https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html），但我并不真正理解他们为什么要制作仿射层（16 * 6 * 6，120）。我知道卷积层的最后输出是16，这里的输出是120，但是即使带有注释，我也不知道6 * 6的来源。有人可以解释吗？

import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
print(net)

Answer 1

6x6来自x的高度和宽度，经过它们的卷积和maxpool之后。

这里是简化的版本，您可以在其中查看形状在每个点上的变化。在示例中打印形状可能会有所帮助，这样您就可以准确了解所有变化。

import torch
import torch.nn as nn
import torch.nn.functional as F

conv1 = nn.Conv2d(1, 6, 3)
conv2 = nn.Conv2d(6, 16, 3)

# Making a pretend input similar to theirs.
# We define an input with 1 batch, 1 channel, height 32, width 32
x = torch.ones((1,1,32,32))


# Simulating forward()

x = F.max_pool2d(F.relu(conv1(x)), (2, 2))
print(x.shape)  # torch.Size([1, 6, 15, 15])  1 batch, 6 channels, height 15, width 15

x = F.max_pool2d(F.relu(conv2(x)), 2)
print(x.shape)  # torch.Size([1, 16, 6, 6])  1 batch, 16 channels, height 6, width 6

接下来，他们将x展平并通过fc1接受16*6*6并产生120的输出。

pytorch线性/仿射层参数令人困惑

1 个答案: