import torch

import torch.nn as nn

import torch.nn.functional as F

class AlphaNet(nn.Module):

def __init__(self, board_size, action_size):

super(AlphaNet, self).__init__()

self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1)

self.bn1 = nn.BatchNorm2d(64)

# Residual Blocks for "Deep" reasoning

self.res_block = nn.Sequential(

nn.Conv2d(64, 64, kernel_size=3, padding=1),

nn.BatchNorm2d(64),

nn.ReLU(),

nn.Conv2d(64, 64, kernel_size=3, padding=1),

nn.BatchNorm2d(64)

)

# Policy Head

self.policy_head = nn.Linear(64 * board_size, action_size)

# Value Head

self.value_head = nn.Linear(64 * board_size, 1)

def forward(self, x):

x = F.relu(self.bn1(self.conv1(x)))

x = F.relu(x + self.res_block(x)) # Residual connection

x = x.view(x.size(0), -1)

policy = F.softmax(self.policy_head(x), dim=1)

value = torch.tanh(self.value_head(x))

return policy, value

def train_alphazero(model, game_env, iterations=100):

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

memory = [] # Buffer for self-play data

for i in range(iterations):

# 1. Self-Play (Data Generation)

state = game_env.reset()

game_history = []

while not game_env.done:

# Use MCTS guided by 'model' to pick move

action, probs = mcts_search(state, model)

game_history.append((state, probs))

state = game_env.step(action)

# 2. Update Memory with Game Outcome (Z)

reward = game_env.reward

for s, p in game_history:

memory.append((s, p, reward))

# 3. Kaizen Step: Incremental Learning

if len(memory) > 500:

batch = random.sample(memory, 64)

loss = update_model(model, optimizer, batch)

print(f"Iteration {i}: Loss {loss:.4f} - System Optimized.")

import torch

import torch.nn as nn

class SEBlock(nn.Module):

"""Squeeze-and-Excitation for channel-wise attention."""

def __init__(self, channels, reduction=16):

super().__init__()

self.fc = nn.Sequential(

nn.Linear(channels, channels // reduction, bias=False),

nn.ReLU(inplace=True),

nn.Linear(channels // reduction, channels, bias=False),

nn.Sigmoid()

)

def forward(self, x):

b, c, _, _ = x.size()

y = x.view(b, c, -1).mean(dim=2)

y = self.fc(y).view(b, c, 1, 1)

return x * y.expand_as(x)

class ResBlock(nn.Module):

def __init__(self, channels):

super().__init__()

self.conv1 = nn.Conv2d(channels, channels, 3, padding=1, bias=False)

self.bn1 = nn.BatchNorm2d(channels)

self.conv2 = nn.Conv2d(channels, channels, 3, padding=1, bias=False)

self.bn2 = nn.BatchNorm2d(channels)

self.se = SEBlock(channels)

def forward(self, x):

residual = x

out = torch.relu(self.bn1(self.conv1(x)))

out = self.bn2(self.conv2(out))

out = self.se(out)

return torch.relu(out + residual)

import torch.multiprocessing as mp

class ActiveAlphaZero:

def __init__(self, model):

self.model = model.share_memory() # Share across processes

self.buffer = mp.Queue(maxsize=10000)

def actor_process(self, process_id):

"""Continuous Self-Play: The Kaizen Engine."""

game = GameEnv()

while True:

# Generate high-quality trajectory using MCTS + Current Model

trajectory = self.run_self_play(game)

self.buffer.put(trajectory)

def learner_process(self):

"""Continuous Optimization."""

optimizer = torch.optim.AdamW(self.model.parameters(), lr=1e-3, weight_decay=1e-4)

while True:

batch = self.sample_from_buffer()

# Multi-head loss: Policy (CrossEntropy) + Value (MSE)

loss = self.compute_alphazero_loss(batch)

loss.backward()

optimizer.step()

# HUD Update: Output 1080p-ready diagnostic stats

self.update_dev_hud(loss.item())