def attention_net(self, lstm_output, final_state): 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)
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,
self.attention = nn.Linear(hidden_dim * 2, 1) self.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 ...
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 =
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...
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(...
lstm_layer(input_sequence) return output_states,final_h 注意力机制 这里的k是可以理解为下面代码的encoder_states,这个encoder_states是encoder中所有的隐藏层的状态,还有就是k==v,也就是v也是encoder_states,Q可以理解为这里的decoder_state class Seq2SeqAttentionMechanism(nn.Module): def __init__(self)...
# dropout: If non-zero, introduces a Dropout layer on the outputs of each RNN layer except the last layer, with dropout probability equal to dropout. Default: 0 # bidirectional: If True, becomes a bidirectional RNN. Default: False
Module): r""" Self attention Layer. Source paper: https://arxiv.org/abs/1805.08318 """ def __init__(self, in_dim, activation=F.relu): super(SelfAttention, self).__init__() self.chanel_in = in_dim self.activation = activation self.f = nn.Conv2d(in_channels=in_dim, out_...
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...