请问下大家tensorflow中的probability在pytorch中有没有对应的包，我非科班学了好久pytorch，结果发现probability中的convolution1dflipout在pytorch中找不到😭
pytorch里面还没有与convolution1dflipout相对应的卷积层，你要不然就结合pyro这种概率编程库自己实现这个层，要不然就要用pytorch里的自定义层的功能自己实现。
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
# 定义 Conv1dFlipout 层
class Conv1dFlipout(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
super(Conv1dFlipout, self).__init__()
# 初始化各种参数
self.in_channels = in_channels # 输入通道数
self.out_channels = out_channels # 输出通道数
self.kernel_size = kernel_size # 卷积核大小
self.stride = stride # 步幅
self.padding = padding # 填充
self.dilation = dilation # 空洞
self.groups = groups # 分组卷积
self.bias = bias # 是否使用偏置项
# 定义权重均值和对数标准差，这些参数是需要学习的
self.weight_mean = Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size))
self.weight_logstd = Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size))
# 定义偏置项的均值和对数标准差，这些参数是需要学习的
if bias:
self.bias_mean = Parameter(torch.Tensor(out_channels))
self.bias_logstd = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias_mean', None)
self.register_parameter('bias_logstd', None)
# 重置各个参数的初始值
self.reset_parameters()
# 重置各个参数的初始值
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight_mean, a=math.sqrt(5))
nn.init.constant_(self.weight_logstd, -10)
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_mean)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self.bias_mean, -bound, bound)
nn.init.constant_(self.bias_logstd, -10)
# 定义前向传播函数
def forward(self, x):
weight_epsilon = torch.randn(self.weight_mean.shape).to(x.device)
weight = self.weight_mean + weight_epsilon * torch.exp(self.weight_logstd)
if self.bias is not None:
bias_epsilon = torch.randn(self.bias_mean.shape).to(x.device)
bias = self.bias_mean + bias_epsilon * torch.exp(self.bias_logstd)
else:
bias = None
# 获取输入和权重的维度
batch_size, input_channels, input_length = x.size()
output_channels, _, kernel_size = weight.size()
# 计算输出的维度
output_length = (input_length + 2 * self.padding - dilation * (kernel_size - 1) - 1) // self.stride + 1
# 将输入和权重张量重塑为卷积运算所需的形状
x = x.view(batch_size, input_channels // self.groups, self.groups, input_length)
weight = weight.view(output_channels, input_channels // self.groups, self.groups, kernel_size)
# 使用groups进行卷积运算
output = F.conv1d(x, weight, bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups)
# 将输出张量重塑为卷积运算输出的形状
output = output.view(batch_size, output_channels, output_length)
return output
一个新的卷积结构就要重定义一个新的卷积层，这还是其中一个示例。所以说，既然这么麻烦，算了还不如直接调用tensorflow现成的
probability是概率，在pytorch里也是有的。
不知道你这个问题是否已经解决, 如果还没有解决的话: