def forward(self, x): # x形状大小为[batch_size, input_len, feature_size] # output形状大小为[batch_size, input_len, hidden_size] # hidden形状大小为[num_layers, batch_size, hidden_size] output, (hidden, cell) = self.lstm_layer(x) # output: [batch_size, input_len, hidden_size] -> [batch_size, input_len*pred_len] output = paddle.reshape(output, [len(output), -1]) output1 = self.linear1_1(output) output1 = self.dropout(output1) output1 = self.linear1_2(output1) # output1 = self.dropout(output1) # output1 = self.linear1_3(output1) output2 = self.linear2_1(output) output2 = self.dropout(output2) output2 = self.linear2_2(output2) # output2 = self.dropout(output2) # output2 = self.linear2_3(output2) # outputs: ([batch_size, pre_len, 1], [batch_size, pre_len, 1]) return [output1, output2]
时间: 2023-11-22 17:04:15 浏览: 37
这段代码是一个模型的前向传播函数,输入x是一个形状为[batch_size, input_len, feature_size]的tensor,经过一个LSTM层后得到形状为[batch_size, input_len, hidden_size]的output和形状为[num_layers, batch_size, hidden_size]的hidden和cell。然后将output经过两个全连接层和dropout层,得到两个形状为[batch_size, pred_len]的输出output1和output2。最后返回output1和output2。
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class SelfAttention(nn.Module): def init(self, input_size=1, num_heads=1): super(SelfAttention, self).init() self.num_heads = 1 self.head_size = 1 self.query = nn.Linear(1, 1) self.key = nn.Linear(1, 1) self.value = nn.Linear(1, 1) self.out = nn.Linear(1, 1) def forward(self, inputs): batch_size, seq_len, input_size = inputs.size() # 128 706 1 # Split inputs into num_heads inputs = inputs.view(batch_size, seq_len, self.num_heads, self.head_size) inputs = inputs.permute(0, 2, 1, 3).contiguous() queries = self.query(inputs).view(batch_size, self.num_heads, seq_len, self.head_size) keys = self.key(inputs).view(batch_size, self.num_heads, seq_len, self.head_size) values = self.value(inputs).view(batch_size, self.num_heads, seq_len, self.head_size) # Compute attention scores scores = torch.matmul(queries, keys.permute(0, 1, 3, 2)) scores = scores / (self.head_size ** 0.5) attention = F.softmax(scores, dim=-1) # Apply attention weights to values attention_output = torch.matmul(attention, values) attention_output = attention_output.view(batch_size, seq_len, input_size) # Apply output linear layer output = self.out(attention_output) return output class DenseAttentionLayer(nn.Module): def init(self, input_size, return_alphas=True, name=None, num_heads=1): super(DenseAttentionLayer, self).init() self.return_alphas = return_alphas self.name = name self.num_heads = num_heads # If input comes with a hidden dimension (e.g. 5 features per gene) # print("len(input_size): ",len(input_size)) # 2 if len(input_size) == 3: self.feature_collapse = nn.Linear(input_size[-1], 1) input_size = (input_size[0], input_size[1]) self.attention = SelfAttention(input_size=1, num_heads=1) def forward(self, inputs): print("inputs.shape: ",inputs.shape) # torch.Size([128, 706]) output = self.attention(inputs) if self.return_alphas: alphas = F.softmax(output, dim=1) return torch.mul(inputs, alphas), alphas else: return output 对于上述代码其中numheads=1 headsize=1
这段代码实现了一个自注意力层(Self-Attention Layer)和一个稠密注意力层(Dense Attention Layer)。
在自注意力层中,输入被划分为多个头(num_heads),每个头的大小为head_size。然后,通过三个线性层(query、key、value)将输入映射到查询(queries)、键(keys)和值(values)空间,并计算注意力分数(scores),再通过softmax函数计算权重(attention),最后将权重与值相乘得到输出(attention_output)。
在稠密注意力层中,首先将输入进行特征折叠(feature collapse),将第三个维度(如果存在)折叠为1。然后,将折叠后的输入送入自注意力层,得到输出。如果return_alphas为True,则也返回注意力权重。
需要注意的是,这里的num_heads和head_size都被设置为1,因此实际上并没有使用多头自注意力机制。
def forward(self, x, state=None, flow=None, mode='bilinear'): # pylint: disable=unused-argument, arguments-differ # Check size assert len(x.size()) == 5, 'Input tensor must be BxTxCxHxW.' b, timesteps, c, h, w = x.size() assert c == self.input_size, f'feature sizes must match, got input {c} for layer with size {self.input_size}' # recurrent layers rnn_output = [] rnn_state = torch.zeros(b, self.hidden_size, h, w, device=x.device) if state is None else state for t in range(timesteps): x_t = x[:, t] if flow is not None: rnn_state = warp_features(rnn_state, flow[:, t], mode=mode) # propagate rnn state rnn_state = self.gru_cell(x_t, rnn_state) rnn_output.append(rnn_state) # reshape rnn output to batch tensor return torch.stack(rnn_output, dim=1)这段代码的计算过程
这段代码实现了一个带有 GRU 循环神经网络的前向传播过程。输入张量 x 的维度为 BxTxCxHxW,其中 B 是批大小,T 是时间步数,C 是通道数,H 和 W 分别是张量的高度和宽度。该函数首先会检查输入张量的维度是否正确。
接下来,函数会初始化 GRU 循环神经网络的状态 rnn_state。如果输入参数 state 不为空,则将其作为初始状态;否则,将状态初始化为全零张量。
接着,函数会遍历时间步,每次取出输入张量 x 中的一个时间步 x_t。如果输入参数 flow 不为空,则使用 warp_features 函数对 rnn_state 进行变形,变形的方式由参数 mode 决定。
然后,函数将 x_t 和 rnn_state 作为 GRU 循环神经网络的输入,计算得到 rnn_state。将 rnn_state 添加到 rnn_output 列表中。
最后,函数将 rnn_output 列表中的张量沿着时间步维度进行堆叠,得到输出张量。输出张量的维度为 BxTxCxHxW。