RNN
?代码实现：
# 一般循环神经网络RNN
class ConvRNN(nn.Module):
def __init__(self, inp_dim, oup_dim, kernel, dilation):
super().__init__()
pad_x = int(dilation * (kernel - 1) / 2)
self.conv_x = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
pad_h = int((kernel - 1) / 2)
self.conv_h = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.relu = nn.LeakyReLU(0.2)
def forward(self, x, h=None):
if h is None:
h = F.tanh(self.conv_x(x))
else:
h = F.tanh(self.conv_x(x) + self.conv_h(h))
h = self.relu(h)
return h, h?
LSTM??
参考：图解LSTM 结构逻辑_BruceJust的博客-CSDN博客_lstm结构图
?
前向传播公式：
?
代码实现：
?
# 参考LSTM结构图
# https://blog.csdn.net/weixin_42175217/article/details/106183682
class ConvLSTM(nn.Module):
def __init__(self, inp_dim, oup_dim, kernel, dilation):
super().__init__()
pad_x = int(dilation * (kernel - 1) / 2)
self.conv_xf = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
self.conv_xi = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
self.conv_xo = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
self.conv_xj = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
pad_h = int((kernel - 1) / 2)
self.conv_hf = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.conv_hi = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.conv_ho = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.conv_hj = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.relu = nn.LeakyReLU(0.2)
def forward(self, x, pair=None):
if pair is None:
i = F.sigmoid(self.conv_xi(x))
o = F.sigmoid(self.conv_xo(x))
j = F.tanh(self.conv_xj(x))
c = i * j
h = o * c
else:
h, c = pair
f = F.sigmoid(self.conv_xf(x) + self.conv_hf(h))
i = F.sigmoid(self.conv_xi(x) + self.conv_hi(h))
o = F.sigmoid(self.conv_xo(x) + self.conv_ho(h))
j = F.tanh(self.conv_xj(x) + self.conv_hj(h))
c = f * c + i * j
h = o * F.tanh(c)
h = self.relu(h)
return h, [h, c]?GRU
?
前向传播公式：
?代码实现：
class ConvGRU(nn.Module):
def __init__(self, inp_dim, oup_dim, kernel, dilation):
super().__init__()
pad_x = int(dilation * (kernel - 1) / 2)
self.conv_xz = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
self.conv_xr = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
self.conv_xn = nn.Conv2d(inp_dim, oup_dim, kernel, padding=pad_x, dilation=dilation)
pad_h = int((kernel - 1) / 2)
self.conv_hz = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.conv_hr = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.conv_hn = nn.Conv2d(oup_dim, oup_dim, kernel, padding=pad_h)
self.relu = nn.LeakyReLU(0.2)
def forward(self, x, h=None):
if h is None:
z = F.sigmoid(self.conv_xz(x))
f = F.tanh(self.conv_xn(x))
h = z * f
else:
z = F.sigmoid(self.conv_xz(x) + self.conv_hz(h))
r = F.sigmoid(self.conv_xr(x) + self.conv_hr(h))
n = F.tanh(self.conv_xn(x) + self.conv_hn(r * h))
h = (1 - z) * h + z * n
h = self.relu(h)
return h, h?