I have implemented the attention (Eq. 1) ofhttps://arxiv.org/pdf/1710.10903.pdfbut it's clearly not memory efficient and can run only a single model on my GPU (it takes 7-10GB). Currently, I have classMyModule(nn.Module):def__init__(self, in_features, out_features):super(My...
self).__init__() self.conv_11 = nn.Conv3d(kernel_size=3, in_channels=1, out_channels=32, padding='same') self.bn_11 = nn.BatchNorm3d(32) self.pool_11 = nn.MaxPool3d(kernel_size=2,
n_hidden *2,1)# hidden : [batch_size, n_hidden * num_directions(=2), 1(=n_layer)]attn_weights = torch.bmm(lstm_output, hidden).squeeze(2)# attn_weights : [batch_size, n_step]soft_attn_weights =
value_layer = value(input) attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))#(b, s, s)attention_scores = attention_scores / math.sqrt(hidden_size)ifattention_maskisnotNone:# Apply the attention mask is (precomputed for all layers in BertModel forward() function)...
self.pinyin_embeddings.weight.requires_grad=True#attention layerself.attention_layer =nn.Sequential( nn.Linear(self.hidden_dims, self.hidden_dims), nn.ReLU(inplace=True) )#self.attention_weights = self.attention_weights.view(self.hidden_dims, 1)#双层lstmself.lstm_net =nn.LSTM(self.char_embed...
self.pinyin_embeddings.weight.requires_grad=True#attention layerself.attention_layer =nn.Sequential( nn.Linear(self.hidden_dims, self.hidden_dims), nn.ReLU(inplace=True) )#self.attention_weights = self.attention_weights.view(self.hidden_dims, 1)#双层lstmself.lstm_net =nn.LSTM(self.char_embed...
代码实现 class AttnBlock(nn.Module): def __init__(self, in_c): super().__init__() # batch_norm layer_norm Instance_norm GroupNorm self.Group = nn.GroupNorm(32, in_c) self.proj_q = nn.Conv2d(in_c, in…
output_layer = nn.Linear(hidden_dim, output_dim) def forward(self, src, tgt): # Encoder encoder_output, (hidden, cell) = self.encoder(src) # Decoder with Attention output = [] for i in range(tgt.size(0)): # 计算注意力权重 attention_weights = torch.tanh(self.attention(torch.cat(...
hidden = final_state.view(-1, n_hidden * 2, 1) # hidden : [batch_size, n_hidden * num_directions(=2), 1(=n_layer)] attn_weights = torch.bmm(lstm_output, hidden).squeeze(2) # attn_weights : [batch_size, n_step] soft_attn_weights = F.softmax(attn_weights, 1) ...
解码注意力Attention机制:从技术解析到PyTorch实战 在本文中,我们深入探讨了注意力机制的理论基础和实际应用。从其历史发展和基础定义,到具体的数学模型,再到其在自然语言处理和计算机视觉等多个人工智能子领域的应用实例,本文为您提供了一个全面且深入的视角。通过Python和PyTorch代码示例,我们还展示了如何实现这一先进...