out, (hidden, cell) = self.lstm(x, (self.hidden0, self.cell0))
时间: 2024-05-19 10:11:34 浏览: 18
这段代码是基于 PyTorch 框架的 LSTM 模型中的一行代码,用于对输入进行序列化处理。具体来说,该代码将输入 x 作为 LSTM 模型的输入,并将上一时间步的隐藏状态和细胞状态 hidden 和 cell 作为初始状态传递给 LSTM 模型。该代码执行后,LSTM 模型将返回输出序列 out 和最后一个时间步的隐藏状态和细胞状态 hidden 和 cell。
相关问题
请在这个DeepCFD的网络添加attention机制,并给出示例代码:import paddle import paddle.nn as nn class Attention(nn.Layer): def __init__(self, input_size, hidden_size): super(Attention, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.attn = nn.Linear(self.input_size + self.hidden_size, 1) self.softmax = nn.Softmax(axis=1) def forward(self, input, hidden): max_len = input.shape[0] attn_energies = paddle.zeros([max_len, 1]) for i in range(max_len): attn_energies[i] = self.score(input[i], hidden) attn_weights = self.softmax(attn_energies) context = paddle.sum(attn_weights * input, axis=0) return context.unsqueeze(0) def score(self, input, hidden): energy = self.attn(paddle.concat([input, hidden], axis=1)) return energy class DeepCFD(nn.Layer): def __init__(self, input_size, hidden_size, output_size): super(DeepCFD, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True) self.attention = Attention(input_size, hidden_size) self.fc = nn.Linear(hidden_size, output_size) def forward(self, input): output, (hidden, cell) = self.lstm(input) context = self.attention(output, hidden[-1]) output = self.fc(context) return output
好的,下面是添加了Attention机制的DeepCFD网络的示例代码:
import paddle
import paddle.nn as nn
class Attention(nn.Layer):
def __init__(self, input_size, hidden_size):
super(Attention, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.attn = nn.Linear(self.input_size + self.hidden_size, 1)
self.softmax = nn.Softmax(axis=1)
def forward(self, input, hidden):
max_len = input.shape[1]
attn_energies = paddle.zeros([input.shape[0], max_len, 1])
for i in range(max_len):
attn_energies[:, i] = self.score(input[:, i, :], hidden)
attn_weights = self.softmax(attn_energies)
context = paddle.sum(attn_weights * input, axis=1)
return context
def score(self, input, hidden):
energy = self.attn(paddle.concat([input, hidden], axis=1))
return energy
class DeepCFD(nn.Layer):
def __init__(self, input_size, hidden_size, output_size):
super(DeepCFD, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True)
self.attention = Attention(input_size, hidden_size)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, input):
output, (hidden, cell) = self.lstm(input)
context = self.attention(output, hidden[-1])
output = self.fc(context)
return output
在这个示例代码中,我们将Attention机制应用到了LSTM的输出上。在Attention中,我们计算了每个时间步的注意力能量,然后使用softmax函数计算注意力权重。然后,我们将这些权重与LSTM输出相乘并求和,得到上下文向量作为Attention机制的输出。
在DeepCFD中,我们使用了两层LSTM,然后将LSTM输出和最后一个时刻的隐藏状态作为Attention机制的输入。最后,我们将Attention机制的输出传递到一个全连接层中,得到最终的输出。
解释这段代码class LSTM(nn.Module): def __init__(self,p,input_size, output_size, hidden_layer_size,past_history_size): """ :param input_size: 输入数据的维度 :param hidden_layer_size:隐层的数目 :param output_size: 输出的个数 """ super().__init__() # self.hidden_layer_size1 = hidden_layer_size*past_history_size self.hidden_layer_size1 = hidden_layer_size self.lstm = nn.LSTM(input_size, hidden_layer_size, batch_first=True) self.linear1 = nn.Linear(self.hidden_layer_size1, output_size) self.linear2 = nn.Linear(self.hidden_layer_size1, output_size) self.linear3 = nn.Linear(self.hidden_layer_size1, output_size) self.linear4 = nn.Linear(self.hidden_layer_size1, output_size) self.linear5 = nn.Linear(self.hidden_layer_size1, output_size) self.dropout = nn.Dropout(p=p) def forward(self, input_x): lstm_out, self.hidden_cell = self.lstm(input_x) lstm_out = self.dropout(lstm_out) # lstm_out = lstm_out.reshape(len(input_x),-1) linear_out1, linear_out2,linear_out3, linear_out4, linear_out5 = self.linear1(lstm_out), self.linear2(lstm_out),self.linear3(lstm_out), self.linear4(lstm_out),self.linear5(lstm_out) # print(linear_out1.shape) linear_out1, linear_out2,linear_out3, linear_out4, linear_out5 = linear_out1[:, -1, :], linear_out2[:, -1, :],linear_out3[:, -1, :], linear_out4[:, -1, :],linear_out5[:, -1, :] linear_out = torch.stack([linear_out1, linear_out2,linear_out3, linear_out4,linear_out5], dim=-1) linear_out = torch.squeeze(linear_out) return linear_out
这段代码定义了一个名为LSTM的神经网络类,继承自PyTorch中的nn.Module类。该类包含了一个LSTM层和5个全连接层,用于将LSTM层的输出映射到5个不同的输出。其中,输入数据的维度为input_size,隐层的数目为hidden_layer_size,输出的个数为output_size。在前向传播时,输入数据input_x首先被传入LSTM层中,得到LSTM层的输出lstm_out和隐藏状态self.hidden_cell。然后,lstm_out经过一个dropout层进行正则化处理,再分别经过5个全连接层进行线性变换,得到5个输出。最后,这5个输出通过torch.stack函数沿着最后一维进行拼接,得到一个形状为[batch_size, output_size, 5]的张量,再通过torch.squeeze函数将其压缩为形状为[batch_size, output_size]的张量,并作为前向传播的输出返回。
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