写一个胶囊神经网络的代码
时间: 2023-11-04 07:05:53 浏览: 63
以下是一个使用PyTorch实现的简单胶囊神经网络(Capsule Neural Network)的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class CapsuleLayer(nn.Module):
def __init__(self, input_dim, output_dim, num_capsules, capsule_dim):
super(CapsuleLayer, self).__init__()
self.num_capsules = num_capsules
self.capsule_dim = capsule_dim
self.W = nn.Parameter(torch.randn(num_capsules, input_dim, output_dim, capsule_dim))
def forward(self, x):
batch_size = x.size(0)
x = x.unsqueeze(2).unsqueeze(3).unsqueeze(4)
u_hat = torch.matmul(self.W, x)
b_ij = torch.zeros(batch_size, self.num_capsules, 1, 1).to(x.device)
num_iterations = 3 # 胶囊网络迭代次数
for iteration in range(num_iterations):
c_ij = F.softmax(b_ij, dim=1)
s_j = (c_ij * u_hat).sum(dim=1, keepdim=True)
v_j = self.squash(s_j)
if iteration < num_iterations - 1:
a_ij = torch.matmul(u_hat.transpose(2, 3), v_j)
b_ij = b_ij + a_ij.squeeze()
return v_j.squeeze()
def squash(self, input_tensor):
squared_norm = (input_tensor ** 2).sum(dim=-1, keepdim=True)
scale = squared_norm / (1 + squared_norm)
output_tensor = scale * input_tensor / torch.sqrt(squared_norm)
return output_tensor
class CapsuleNet(nn.Module):
def __init__(self):
super(CapsuleNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=256, kernel_size=9)
self.primary_caps = CapsuleLayer(input_dim=256, output_dim=32, num_capsules=8, capsule_dim=8)
self.digit_caps = CapsuleLayer(input_dim=32, output_dim=16, num_capsules=10, capsule_dim=16)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.primary_caps(x)
x = self.digit_caps(x)
return x
# 创建一个胶囊网络实例
model = CapsuleNet()
# 随机生成输入数据
input_data = torch.randn(10, 1, 28, 28)
# 前向传播
output = model(input_data)
print(output.shape)
```
这个代码定义了一个简单的胶囊神经网络模型,包括一个卷积层(用于提取特征),一个主胶囊层和一个数字胶囊层。在`forward`方法中,输入数据首先经过卷积层,然后传递给主胶囊层和数字胶囊层进行处理。最后,输出是一个形状为(batch_size, num_capsules, capsule_dim)的张量。在这个示例中,输入数据的大小为(10, 1, 28, 28),表示批次大小为10的灰度图像。您可以根据自己的需求和数据集进行修改和扩展这个简单的胶囊神经网络模型。
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