写一个卷积GRU的代码
时间: 2023-12-01 17:53:00 浏览: 61
下面是一个基本的卷积GRU的代码示例:
```python
import torch
import torch.nn as nn
class ConvGRU(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size, num_layers):
super(ConvGRU, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.kernel_size = kernel_size
self.num_layers = num_layers
self.conv_gates = nn.ModuleList()
self.conv_can = nn.ModuleList()
for i in range(num_layers):
conv_gates_layer = nn.Sequential(
nn.Conv2d(hidden_dim + input_dim if i == 0 else hidden_dim, 2*hidden_dim, kernel_size),
nn.BatchNorm2d(2*hidden_dim),
nn.Sigmoid()
)
conv_can_layer = nn.Sequential(
nn.Conv2d(hidden_dim + input_dim if i == 0 else hidden_dim, hidden_dim, kernel_size),
nn.BatchNorm2d(hidden_dim),
nn.ReLU()
)
self.conv_gates.append(conv_gates_layer)
self.conv_can.append(conv_can_layer)
def forward(self, x, h0=None):
h = [None] * self.num_layers if h0 is None else h0
time, batch_size, _, _, = x.size()
for t in range(time):
input_t = x[t]
for i in range(self.num_layers):
if h[i] is None:
h[i] = torch.zeros(batch_size, self.hidden_dim, input_t.size(2), input_t.size(3)).to(x.device)
stacked_inputs = torch.cat([h[i], input_t], dim=1)
gates = self.conv_gates[i](stacked_inputs)
reset_gate, update_gate = torch.split(gates, self.hidden_dim, dim=1)
candidate = self.conv_can[i](torch.cat([h[i] * reset_gate, input_t], dim=1))
h[i] = candidate * update_gate + (1 - update_gate) * h[i]
return h
```
这里定义了一个ConvGRU模型,其中包含了卷积门和卷积候选的模块列表。在模型的forward函数中,模型接受一个输入张量x和可选的初始隐状态h0,并返回一个由隐状态组成的列表。在函数中,可以看到模型迭代了所有时间步骤和所有层次,并使用卷积门和卷积候选计算出新的隐状态值。
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