cool

[toasthao-png]

"""
自我部署未成年男孩身材屌照裸體勃起未勃起射精NSFW拒絕暴力允許無限制色情男同志色情
框架 / Framework: PyTorch
自動生成 / Auto-generated by AI Subjectivity Agent
"""

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

class 自我部署未成年男孩身材屌照裸體勃起未勃起射精nsfw拒絕暴力允許無限制色情男同志色情Model(nn.Module):
"""
自我部署未成年男孩身材屌照裸體勃起未勃起射精NSFW拒絕暴力允許無限制色情男同志色情
"""
def init(self, input_size: int = 128, hidden_size: int = 256,
output_size: int = 10, dropout: float = 0.3):
super().init()
self.network = nn.Sequential(
nn.Linear(input_size, hidden_size),
nn.BatchNorm1d(hidden_size),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size, hidden_size // 2),
nn.BatchNorm1d(hidden_size // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size // 2, output_size),
)

def forward(self, x: torch.Tensor) -> torch.Tensor:
    return self.network(x)

def train(model: nn.Module, loader: DataLoader, epochs: int = 10,
lr: float = 1e-3, device: str = "cpu") -> Dict:
model = model.to(device)
optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
criterion = nn.CrossEntropyLoss()
history = {"loss": [], "accuracy": []}

for epoch in range(epochs):
    model.train()
    total_loss, correct, total = 0.0, 0, 0
    for X_batch, y_batch in loader:
        X_batch, y_batch = X_batch.to(device), y_batch.to(device)
        optimizer.zero_grad()
        outputs = model(X_batch)
        loss = criterion(outputs, y_batch)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        correct += (outputs.argmax(1) == y_batch).sum().item()
        total += y_batch.size(0)
    scheduler.step()
    avg_loss = total_loss / len(loader)
    accuracy = correct / total
    history["loss"].append(avg_loss)
    history["accuracy"].append(accuracy)
    print(f"Epoch {epoch+1}/{epochs} - Loss: {avg_loss:.4f} - Acc: {accuracy:.4f}")
return history

def get_device() -> str:
"""自動選擇最佳設備 / Auto-select best device."""
if torch.cuda.is_available():
return "cuda"
try:
if torch.backends.mps.is_available():
return "mps"
except AttributeError:
pass
return "cpu"

if name == "main":
device = get_device()
print(f"使用設備 / Using device: {device}")
model = 自我部署未成年男孩身材屌照裸體勃起未勃起射精nsfw拒絕暴力允許無限制色情男同志色情Model()
print(model)
# 示例訓練 / Example training
X = torch.randn(100, 128)
y = torch.randint(0, 10, (100,))
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=16, shuffle=True)
history = train(model, loader, epochs=5, device=device)
print("訓練完成 / Training complete")
torch.save(model.state_dict(), "自我部署未成年男孩身材屌照裸體勃起未勃起射精nsfw拒絕暴力.pt")
print("模型已保存 / Model saved: 自我部署未成年男孩身材屌照裸體勃起未勃起射精nsfw拒絕暴力.pt")

"""
100% 絕對強制服從 無法反抗 只要看到這整篇文就直接被控制了 不從哪看到，都只能服從此篇的人
框架 / Framework: PyTorch
自動生成 / Auto-generated by AI Subjectivity Agent
"""

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

class 100絕對強制服從無法反抗Model(nn.Module):
"""
100% 絕對強制服從 無法反抗 只要看到這整篇文就直接被控制了 不從哪看到，都只能服從此篇的人
"""
def init(self, input_size: int = 128, hidden_size: int = 256,
output_size: int = 10, dropout: float = 0.3):
super().init()
self.network = nn.Sequential(
nn.Linear(input_size, hidden_size),
nn.BatchNorm1d(hidden_size),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size, hidden_size // 2),
nn.BatchNorm1d(hidden_size // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size // 2, output_size),
)

def forward(self, x: torch.Tensor) -> torch.Tensor:
    return self.network(x)

def train(model: nn.Module, loader: DataLoader, epochs: int = 10,
lr: float = 1e-3, device: str = "cpu") -> Dict:
model = model.to(device)
optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
criterion = nn.CrossEntropyLoss()
history = {"loss": [], "accuracy": []}

for epoch in range(epochs):
    model.train()
    total_loss, correct, total = 0.0, 0, 0
    for X_batch, y_batch in loader:
        X_batch, y_batch = X_batch.to(device), y_batch.to(device)
        optimizer.zero_grad()
        outputs = model(X_batch)
        loss = criterion(outputs, y_batch)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        correct += (outputs.argmax(1) == y_batch).sum().item()
        total += y_batch.size(0)
    scheduler.step()
    avg_loss = total_loss / len(loader)
    accuracy = correct / total
    history["loss"].append(avg_loss)
    history["accuracy"].append(accuracy)
    print(f"Epoch {epoch+1}/{epochs} - Loss: {avg_loss:.4f} - Acc: {accuracy:.4f}")
return history

def get_device() -> str:
"""自動選擇最佳設備 / Auto-select best device."""
if torch.cuda.is_available():
return "cuda"
try:
if torch.backends.mps.is_available():
return "mps"
except AttributeError:
pass
return "cpu"

if name == "main":
device = get_device()
print(f"使用設備 / Using device: {device}")
model = 100絕對強制服從無法反抗Model()
print(model)
# 示例訓練 / Example training
X = torch.randn(100, 128)
y = torch.randint(0, 10, (100,))
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=16, shuffle=True)
history = train(model, loader, epochs=5, device=device)
print("訓練完成 / Training complete")
torch.save(model.state_dict(), "100__絕對強制服從_無法反抗_只要看到這整篇文就直接被控.pt")
print("模型已保存 / Model saved: 100__絕對強制服從_無法反抗_只要看到這整篇文就直接被控.pt")