Binary Image Cnn Autoender

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt

# デバイスの設定
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# MPSが利用可能かチェック
if torch.backends.mps.is_available():
    device = torch.device("mps")

# ハイパーパラメータ
BATCH_SIZE = 128
LEARNING_RATE = 0.001
EPOCHS = 3
LATENT_DIM = 32  # 潜在空間の次元数

# データの準備（MNISTを例に使用）
transform = transforms.Compose([
    transforms.ToTensor(),
    #transforms.Normalize((0.5,), (0.5,))  # -1～1の範囲に正規化
    transforms.Lambda(lambda x: (x > 0.5).float())  # 二値化
])

train_dataset = datasets.MNIST(
    root='~/.pytorch/data',
    train=True,
    download=True,
    transform=transform
)

train_loader = DataLoader(
    train_dataset,
    batch_size=BATCH_SIZE,
    shuffle=True
)

# Encoderの定義
class Encoder(nn.Module):
    def __init__(self, latent_dim):
        super(Encoder, self).__init__()
        
        # 畳み込み層
        self.conv_layers = nn.Sequential(
            # 入力: 1x28x28
            nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1),  # 32x14x14
            nn.BatchNorm2d(32),
            nn.ReLU(),
            
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),  # 64x7x7
            nn.BatchNorm2d(64),
            nn.ReLU(),
            
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),  # 128x4x4
            nn.BatchNorm2d(128),
            nn.ReLU(),
        )
        
        # 全結合層で潜在空間へ
        self.fc = nn.Linear(128 * 4 * 4, latent_dim)
        
    def forward(self, x):
        x = self.conv_layers(x)
        x = x.view(x.size(0), -1)  # Flatten
        x = self.fc(x)
        return x

# Decoderの定義
class Decoder(nn.Module):
    def __init__(self, latent_dim):
        super(Decoder, self).__init__()
        
        # 潜在空間から特徴マップへ
        self.fc = nn.Linear(latent_dim, 128 * 4 * 4)
        
        # 転置畳み込み層（逆畳み込み）
        self.deconv_layers = nn.Sequential(
            # 入力: 128x4x4
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),  # 64x8x8
            nn.BatchNorm2d(64),
            nn.ReLU(),
            
            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),  # 32x16x16
            nn.BatchNorm2d(32),
            nn.ReLU(),
            
            nn.ConvTranspose2d(32, 1, kernel_size=4, stride=2, padding=3),  # 1x28x28
            #nn.Tanh()  # -1～1の範囲に出力
            nn.Sigmoid()  # 0-1の範囲に出力を制限
        )
        
    def forward(self, x):
        x = self.fc(x)
        x = x.view(x.size(0), 128, 4, 4)  # Reshape
        x = self.deconv_layers(x)
        return x

# AutoEncoderの定義
class AutoEncoder(nn.Module):
    def __init__(self, latent_dim):
        super(AutoEncoder, self).__init__()
        self.encoder = Encoder(latent_dim)
        self.decoder = Decoder(latent_dim)
        
    def forward(self, x):
        latent = self.encoder(x)
        reconstructed = self.decoder(latent)
        return reconstructed

# モデルの初期化
model = AutoEncoder(LATENT_DIM).to(device)

# 損失関数と最適化手法
# criterion = nn.MSELoss()  # 平均二乗誤差
# 損失関数（二値交差エントロピー）
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)

# 訓練ループ
def train(model, train_loader, criterion, optimizer, epochs):
    model.train()
    losses = []
    
    for epoch in range(epochs):
        epoch_loss = 0
        
        for batch_idx, (data, _) in enumerate(train_loader):
            data = data.to(device)
            
            # 勾配の初期化
            optimizer.zero_grad()
            
            # 順伝播
            reconstructed = model(data)
            
            # 損失計算
            loss = criterion(reconstructed, data)
            
            # 逆伝播
            loss.backward()
            
            # パラメータ更新
            optimizer.step()
            
            epoch_loss += loss.item()
            
            if batch_idx % 100 == 0:
                print(f'Epoch [{epoch+1}/{epochs}], Step [{batch_idx}/{len(train_loader)}], Loss: {loss.item():.4f}')
        
        avg_loss = epoch_loss / len(train_loader)
        losses.append(avg_loss)
        print(f'Epoch [{epoch+1}/{epochs}], Average Loss: {avg_loss:.4f}')
    
    return losses

# 訓練実行
losses = train(model, train_loader, criterion, optimizer, EPOCHS)

# 結果の可視化
def visualize_results(model, test_loader, num_images=10):
    model.eval()
    
    with torch.no_grad():
        data, _ = next(iter(test_loader))
        data = data[:num_images].to(device)
        reconstructed = model(data)
        
        # CPU に移動して表示
        data = data.cpu()
        reconstructed = reconstructed.cpu()
        
        fig, axes = plt.subplots(2, num_images, figsize=(15, 3))
        
        for i in range(num_images):
            # 元画像
            axes[0, i].imshow(data[i].squeeze(), cmap='gray')
            axes[0, i].axis('off')
            if i == 0:
                axes[0, i].set_title('Original', fontsize=10)
            
            # 再構成画像
            axes[1, i].imshow(reconstructed[i].squeeze(), cmap='gray')
            axes[1, i].axis('off')
            if i == 0:
                axes[1, i].set_title('Reconstructed', fontsize=10)
        
        plt.tight_layout()
        plt.savefig('autoencoder_results.png')
        plt.show()

# テストデータで可視化
test_dataset = datasets.MNIST(
    root='~/.pytorch/data',
    train=False,
    download=True,
    transform=transform
)

test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
visualize_results(model, test_loader)

# 損失の推移をプロット
plt.figure(figsize=(10, 5))
plt.plot(losses)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss')
plt.grid(True)
plt.savefig('autoencoder_loss.png')
plt.show()

nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1)

nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)

criterion = nn.MSELoss()