但如果要防止 梯度爆炸（gradient explosion） 或 梯度消失（gradient vanishing），可以採取以下幾種策略：
1. 使用 nn.utils.clip_grad_norm_() 限制梯度範圍
- 在
Zi2ZiModel的訓練過程中，每次更新參數時，對梯度進行裁剪，防止梯度值過大導致爆炸。
2. 替換激活函數
ReLU()容易導致梯度消失（dead neurons），可以改用LeakyReLU()或SiLU()（Swish）來減少這種影響。
3. 權重初始化
- 為避免梯度消失或爆炸，可以在網路初始化時使用
nn.init.kaiming_normal_()或nn.init.xavier_uniform_()來確保合理的權重分佈。
4. 調整 gamma 參數的初始化
SelfAttention中的self.gamma = nn.Parameter(torch.zeros(1))可能會導致梯度學習變慢，建議改為nn.Parameter(torch.ones(1) * 0.1)。
5. 使用 LayerNorm 或 GroupNorm
BatchNorm2d可能會因 batch size 過小而效果不佳，可考慮改用nn.GroupNorm()或nn.LayerNorm()。
修改後的關鍵部分
1. 梯度裁剪
def optimize_parameters(self):
# 計算 Generator 損失
self.optimizer_G.zero_grad()
with torch.cuda.amp.autocast(enabled=True):
loss_G = self.compute_generator_loss()
self.scaler_G.scale(loss_G).backward()
# **新增梯度裁剪，防止梯度爆炸**
torch.nn.utils.clip_grad_norm_(self.netG.parameters(), max_norm=self.gradient_clip)
self.scaler_G.step(self.optimizer_G)
self.scaler_G.update()
2. 修改 SelfAttention
class SelfAttention(nn.Module):
def __init__(self, channels):
super(SelfAttention, self).__init__()
self.query = nn.Conv2d(channels, channels // 8, kernel_size=1)
self.key = nn.Conv2d(channels, channels // 8, kernel_size=1)
self.value = nn.Conv2d(channels, channels, kernel_size=1)
# **將 gamma 的初始值設為 0.1 而不是 0，避免梯度消失**
self.gamma = nn.Parameter(torch.ones(1) * 0.1)
self.scale = (channels // 8) ** -0.5
def forward(self, x):
B, C, H, W = x.shape
proj_query = self.query(x).view(B, -1, H * W).permute(0, 2, 1)
proj_key = self.key(x).view(B, -1, H * W)
energy = torch.bmm(proj_query, proj_key)
attention = F.softmax(energy * self.scale, dim=-1)
proj_value = self.value(x).view(B, -1, H * W)
out = torch.bmm(proj_value, attention.permute(0, 2, 1)).view(B, C, H, W)
return self.gamma * out + x # gamma 控制輸出影響力
3. 修改 ResSkip，避免梯度消失
class ResSkip(nn.Module):
def __init__(self, channels):
super(ResSkip, self).__init__()
self.conv = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.norm = nn.GroupNorm(8, channels) # **改用 GroupNorm 來減少 BatchNorm 依賴**
self.relu = nn.SiLU(inplace=True) # **用 SiLU 替換 ReLU，減少死神經元問題**
def forward(self, x):
return x + self.relu(self.norm(self.conv(x)))
這些修改應該能有效防止梯度爆炸和消失，讓模型更穩定！你可以試試看，看看效果如何 
gemini
gemini 第一次給的答案就比較弱, 按 “重做” 按鈕, 才有參考性佳的答案:
好的，根據您提供的程式碼，我將針對梯度爆炸或消失的問題進行修改，並提供解釋。以下是修改後的程式碼和說明：
1. ResSkip 層的改進：
- 初始化權重： 確保卷積層的權重初始化合理，例如使用 Kaiming 初始化。
- 使用 Layer Normalization： 在 ReLU 之後添加 Layer Normalization，有助於穩定訓練。
Python
class ResSkip(nn.Module):
def __init__(self, channels):
super(ResSkip, self).__init__()
self.conv = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
nn.init.kaiming_normal_(self.conv.weight, nonlinearity='relu') # Kaiming 初始化
self.relu = nn.ReLU(inplace=True)
self.norm = nn.LayerNorm([channels, None, None]) # 添加 Layer Normalization
def forward(self, x):
return x + self.norm(self.relu(self.conv(x)))
2. SelfAttention 層的改進：
- 使用 Layer Normalization： 在
value層之後添加 Layer Normalization。 - 初始化權重： 確保卷積層的權重初始化合理。
Python
class SelfAttention(nn.Module):
def __init__(self, channels):
super(SelfAttention, self).__init__()
self.query = nn.Conv2d(channels, channels // 8, kernel_size=1)
self.key = nn.Conv2d(channels, channels // 8, kernel_size=1)
self.value = nn.Conv2d(channels, channels, kernel_size=1)
self.gamma = nn.Parameter(torch.zeros(1))
self.scale = (channels // 8) ** -0.5 # 預先計算縮放因子
self.norm = nn.LayerNorm([channels, None, None]) # 添加 Layer Normalization
nn.init.kaiming_normal_(self.query.weight)
nn.init.kaiming_normal_(self.key.weight)
nn.init.kaiming_normal_(self.value.weight)
def forward(self, x):
B, C, H, W = x.shape
proj_query = self.query(x).view(B, -1, H * W).permute(0, 2, 1) # B, N, C'
proj_key = self.key(x).view(B, -1, H * W) # B, C', N
energy = torch.bmm(proj_query, proj_key) # B, N, N
attention = F.softmax(energy * self.scale, dim=-1) # B, N, N
proj_value = self.value(x).view(B, -1, H * W) # B, C, N
out = torch.bmm(proj_value, attention.permute(0, 2, 1)).view(B, C, H, W) # B, C, H, W
return self.gamma * self.norm(out) + x
3. UnetSkipConnectionBlock 層的改進：
- 使用 Layer Normalization： 在
upconv層之後添加 Layer Normalization。 - 初始化權重： 確保卷積層的權重初始化合理。
Python
class UnetSkipConnectionBlock(nn.Module):
# ... (其他程式碼保持不變)
def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None,
norm_layer=nn.InstanceNorm2d, layer=0, embedding_dim=128,
use_dropout=False, self_attention=False, blur=False, outermost=False, innermost=False):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
self.innermost = innermost
use_bias = norm_layer != nn.BatchNorm2d
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
nn.init.kaiming_normal_(downconv.weight, nonlinearity='leaky_relu')
downrelu = nn.LeakyReLU(0.2, True)
downnorm = norm_layer(inner_nc)
uprelu = nn.ReLU(inplace=False)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
nn.init.kaiming_normal_(upconv.weight)
self.down = nn.Sequential(downconv)
self.up = nn.Sequential(uprelu, upconv, nn.Tanh())
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc + embedding_dim, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
nn.init.kaiming_normal_(upconv.weight)
self.down = nn.Sequential(downrelu, downconv)
self.up = nn.Sequential(uprelu, upconv, upnorm)
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
nn.init.kaiming_normal_(upconv.weight)
self.down = nn.Sequential(downrelu, downconv, downnorm)
self.up = nn.Sequential(uprelu, upconv, upnorm)
if use_dropout:
self.up.add_module("dropout", nn.Dropout(0.3))
self.submodule = submodule
self.self_attn = SelfAttention(inner_nc) if self_attention and layer in [4, 6] else None
self.res_skip = ResSkip(outer_nc) if not outermost and not innermost else None
# ... (其他程式碼保持不變)
4. Discriminator 層的改進：
- 初始化權重： 確保卷積層和線性層的權重初始化合理。
Python
class Discriminator(nn.Module):
# ... (其他程式碼保持不變)
def __init__(self, input_nc=1, embedding_num=40, ndf=64, norm_layer=nn.BatchNorm2d,
final_channels=1, blur=False):
super(Discriminator, self).__init__()
use_bias = norm_layer != nn.BatchNorm2d
kw = 5
padw = 2
sequence = [
nn.utils.spectral_norm(nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw)),
nn.LeakyReLU(0.2, True)
]
nf_mult = 1
for n in range(1, 3):
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
sequence += [
nn.utils.spectral_norm(nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias)),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [
nn.utils.spectral_norm(nn.Conv2d(ndf * nf_mult, final_channels, kernel_size=kw, stride=1, padding=padw, bias=use_bias)),
norm_layer(final_channels),
nn.LeakyReLU(0.2, True)
]
self.model = nn.Sequential(*sequence)
self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # 確保最終輸出是 (batch_size, C, 1, 1)
self.blur = blur
self.gaussian_blur = T.GaussianBlur(kernel_size=1, sigma=1.0) # 設定模糊程度
self.embedding_num = embedding_num
self.binary = nn.Linear(nf_mult*ndf, 1)
self.category = nn.Linear(nf
class Zi2ZiModel:
# … (其他程式碼保持不變)
def optimize_parameters(self, use_autocast=False):
self.forward()
self.set_requires_grad(self.netD, True)
self.optimizer_D.zero_grad()
if use_autocast:
with torch.amp.autocast(device_type='cuda'):
category_loss = self.backward_D()
scaled_d_loss = self.scaler_D.scale(self.d_loss)
scaled_d_loss.backward()
self.scaler_D.unscale_(self.optimizer_D)
grad_norm_d = torch.nn.utils.clip_grad_norm_(self.netD.parameters(), self.gradient_clip)
self.scaler_D.step(self.optimizer_D)
self.scaler_D.update()
else:
category_loss = self.backward_D()
self.d_loss.backward()
grad_norm_d = torch.nn.utils.clip_grad_norm_(self.netD.parameters(), self.gradient_clip)
self.optimizer_D.step()
# 檢查判別器損失是否為 NaN
if torch.isnan(self.d_loss):
print("判別器損失為 NaN，停止訓練。")
# 可以選擇引發異常或記錄錯誤
# raise ValueError("判別器損失為 NaN。")
return # 或執行其他適當的錯誤處理
self.set_requires_grad(self.netD, False)
self.optimizer_G.zero_grad()
const_loss, l1_loss, cheat_loss, fm_loss, perceptual_loss = 0, 0, 0, 0, 0
if use_autocast:
with torch.amp.autocast(device_type='cuda'):
const_loss, l1_loss, cheat_loss, fm_loss, perceptual_loss = self.backward_G()
scaled_g_loss = self.scaler_G.scale(self.g_loss)
scaled_g_loss.backward()
self.scaler_G.unscale_(self.optimizer_G)
grad_norm_g = torch.nn.utils.clip_grad_norm_(self.netG.parameters(), self.gradient_clip)
self.scaler_G.step(self.optimizer_G)
self.scaler_G.update()
else:
const_loss, l1_loss, cheat_loss, fm_loss, perceptual_loss = self.backward_G()
self.g_loss.backward()
grad_norm_g = torch.nn.utils.clip_grad_norm_(self.netG.parameters(), self.gradient_clip)
self.optimizer_G.step()
self.forward()
self.optimizer_G.zero_grad()
if use_autocast:
with torch.amp.autocast(device_type='cuda'):
const_loss, l1_loss, cheat_loss, fm_loss, perceptual_loss = self.backward_G()
scaled_g_loss = self.scaler_G.scale(self.g_loss)
scaled_g_loss.backward()
self.scaler_G.unscale_(self.optimizer_G)
grad_norm_g = torch.nn.utils.clip_grad_norm_(self.netG.parameters(), self.gradient_clip)
self.scaler_G.step(self.optimizer_G)
self.scaler_G.update()
else:
const_loss, l1_loss, cheat_loss, fm_loss, perceptual_loss = self.backward_G()
self.g_loss.backward()
grad_norm_g = torch.nn.utils.clip_grad_norm_(self.netG.parameters(), self.gradient_clip)
self.optimizer_G.step()
# 可以選擇性地監控梯度範數
# print(f"判別器梯度範數：{grad_norm_d}")
# print(f"生成器梯度範數：{grad_norm_g}")
return const_loss, l1_loss, cheat_loss, fm_loss, perceptual_loss