self.convs = nn.ModuleList( [nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])

这段代码是用来定义一个卷积神经网络(Convolutional Neural Network)的层，其中`config.num_filters`表示卷积核的数量，`config.filter_sizes`是一个数组，表示卷积核的大小。`nn.ModuleList`是PyTorch中的一个数据结构，用于存储多个子模块，这里将多个卷积层存储在`self.convs`中。`nn.Conv2d`则是一个卷积层，它的输入参数有三个，分别是输入的通道数、输出的通道数和卷积核的大小。在这里，输入的通道数为1，表示输入的是一维的词向量；输出的通道数为`config.num_filters`，表示卷积核的个数；卷积核的大小为`(k, config.embed)`，其中`k`是从`config.filter_sizes`中取出的卷积核大小，`config.embed`是词向量的维度。所以这段代码定义了多个卷积层，每个卷积层的卷积核大小不同，但输入和输出通道数相同。

相关问题

self.convs = nn.ModuleList(convs)是什么意思

self.convs = nn.ModuleList(convs)是一个Python代码，它的意思是将一个列表中的卷积层实例化为一个 nn.ModuleList 对象，并将其赋值给 self.convs 变量。nn.ModuleList 是一个 PyTorch 的模型组件，它可以把模块列表转换为一个模块。在这个例子中，self.convs 是一个包含多个卷积层的模块列表，可以用于神经网络的前向传递过程中。

class GraphSAGE(nn.Module): def __init__(self, in_feats, hidden_feats, out_feats, num_layers, activation): super(GraphSAGE, self).__init__() self.num_layers = num_layers self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean') self.convs = nn.ModuleList() for i in range(num_layers - 2): self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')) self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean') self.activation = activation def forward(self, blocks, x): h = x for i, block in enumerate(blocks): h_dst = h[:block.number_of_dst_nodes()] h = self.convs[i](block, (h, h_dst)) if i != self.num_layers - 2: h = self.activation(h) h = self.conv_last(blocks[-1], (h, h_dst)) return h改写一下，让它适用于异质图

class GraphSAGE(nn.Module):
def __init__(self, in_feats, hidden_feats, out_feats, num_layers, activation):
super(GraphSAGE, self).__init__()
self.num_layers = num_layers
self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean')
self.convs = nn.ModuleList()
for i in range(num_layers - 2):
self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean'))
self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean')
self.activation = activation

def forward(self, blocks, x_dict):
h = {k: v for k, v in x_dict.items()}
for i, block in enumerate(blocks):
edge_type = block.edata['type']
h_dst = h[str(edge_type)][block.dstdata[dgl.NID]]
h = self.convs[i](block, (h, h_dst))
if i != self.num_layers - 2:
h = self.activation(h)
h_dst = h[str(edge_type)][blocks[-1].dstdata[dgl.NID]]
h = self.conv_last(blocks[-1], (h, h_dst))
return h