我曾尝试使用CNN进行情感分析，但无法弄清楚张量如何在正向方法内转换。我试图实现我的模型，但它的错误应为“预期的3维张量，但得到4维张量用于论证”任何人都可以告诉我如何知道张量进出的形状是什么吗？
我在互联网上找到了此代码以供参考，但无法弄清楚张量形状的变化是如何精确的，我如何知道下一层/函数将其用作参数的尺寸格式张量。谁能解释我的用法此代码的正向方法有效吗？
class CNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim,
dropout, pad_idx):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)
self.conv_0 = nn.Conv2d(in_channels = 1,
out_channels = n_filters,
kernel_size = (filter_sizes[0], embedding_dim))
self.conv_1 = nn.Conv2d(in_channels = 1,
out_channels = n_filters,
kernel_size = (filter_sizes[1], embedding_dim))
self.conv_2 = nn.Conv2d(in_channels = 1,
out_channels = n_filters,
kernel_size = (filter_sizes[2], embedding_dim))
self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, text):
#text = [batch size, sent len]
embedded = self.embedding(text)
#embedded = [batch size, sent len, emb dim]
embedded = embedded.unsqueeze(1)
#embedded = [batch size, 1, sent len, emb dim]
conved_0 = F.relu(self.conv_0(embedded).squeeze(3))
conved_1 = F.relu(self.conv_1(embedded).squeeze(3))
conved_2 = F.relu(self.conv_2(embedded).squeeze(3))
#conved_n = [batch size, n_filters, sent len - filter_sizes[n] + 1]
pooled_0 = F.max_pool1d(conved_0, conved_0.shape[2]).squeeze(2)
pooled_1 = F.max_pool1d(conved_1, conved_1.shape[2]).squeeze(2)
pooled_2 = F.max_pool1d(conved_2, conved_2.shape[2]).squeeze(2)
#pooled_n = [batch size, n_filters]
cat = self.dropout(torch.cat((pooled_0, pooled_1, pooled_2), dim = 1))
#cat = [batch size, n_filters * len(filter_sizes)]
return self.fc(cat)
）
最简单的方法是在每个前进步骤之后打印张量的形状，
def forward(self, text):
print(text.shape)
embedded = self.embedding(text)
print(embedded.shape)
embedded = embedded.unsqueeze(1)
print(embedded.shape)
conved_0 = F.relu(self.conv_0(embedded).squeeze(3))
conved_1 = F.relu(self.conv_1(embedded).squeeze(3))
conved_2 = F.relu(self.conv_2(embedded).squeeze(3))
print(conved_2.shape)
....
顺便说一句，我认为您的代码有几个问题，我想您想使用cnv1d而不是conv2d，并且每个池cmaes在相应的卷积之后，都为conv-> pool-> conv-> pool。 ..，不是conv-> conv-> conv->池->池->池
考虑使用此代码
class CNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout, pad_idx):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)
self.conv_0 = nn.Conv1d(in_channels = 1,
out_channels = n_filters,
kernel_size = filter_sizes[0])
self.conv_1 = nn.Conv2d(in_channels = 1,
out_channels = n_filters,
kernel_size = filter_sizes[1])
self.conv_2 = nn.Conv2d(in_channels = 1,
out_channels = n_filters,
kernel_size = filter_sizes[2]
self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, text):
#text = [batch size, sent len]
embedded = self.embedding(text)
#embedded = [batch size, sent len, emb dim]
conved_0 = F.relu(self.conv_0(embedded))
conved_1 = F.relu(self.conv_1(embedded))
conved_2 = F.relu(self.conv_2(embedded))
pooled_0 = F.max_pool1d(conved_0, 5)
pooled_1 = F.max_pool1d(conved_1, 5)
pooled_2 = F.max_pool1d(conved_2, 5)
cat = self.dropout(torch.cat((pooled_0, pooled_1, pooled_2), dim = 1))
return self.fc(cat)