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name: dnn-architectures description: Deep neural network architectures including CNNs, RNNs, Transformers, and modern architectures for vision, NLP, and multimodal tasks.

DNN Architectures

Modern deep neural network architectures.

Convolutional Neural Networks

Copyimport torch.nn as nn

class CNN(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d(1)
        )
        self.classifier = nn.Linear(256, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        return self.classifier(x)

Transformer Architecture

Copyclass TransformerBlock(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout=0.1):
        super().__init__()
        self.attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
        self.ff = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(d_ff, d_model)
        )
        self.ln1 = nn.LayerNorm(d_model)
        self.ln2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask=None):
        # Self-attention with residual
        attn_out, _ = self.attn(x, x, x, attn_mask=mask)
        x = self.ln1(x + self.dropout(attn_out))
        # Feedforward with residual
        ff_out = self.ff(x)
        x = self.ln2(x + self.dropout(ff_out))
        return x

Vision Transformer (ViT)

Copyclass ViT(nn.Module):
    def __init__(self, image_size, patch_size, num_classes, d_model, n_heads, n_layers):
        super().__init__()
        num_patches = (image_size // patch_size) ** 2
        self.patch_embed = nn.Conv2d(3, d_model, patch_size, patch_size)
        self.cls_token = nn.Parameter(torch.zeros(1, 1, d_model))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, d_model))
        self.transformer = nn.ModuleList([
            TransformerBlock(d_model, n_heads, d_model * 4)
            for _ in range(n_layers)
        ])
        self.head = nn.Linear(d_model, num_classes)

    def forward(self, x):
        patches = self.patch_embed(x).flatten(2).transpose(1, 2)
        cls_tokens = self.cls_token.expand(x.size(0), -1, -1)
        x = torch.cat([cls_tokens, patches], dim=1)
        x = x + self.pos_embed
        for block in self.transformer:
            x = block(x)
        return self.head(x[:, 0])

Architecture Comparison

Modern Architectures

Copy# Using pretrained models
from transformers import AutoModel

# Vision
vit = AutoModel.from_pretrained("google/vit-base-patch16-224")
clip = AutoModel.from_pretrained("openai/clip-vit-base-patch32")

# NLP
bert = AutoModel.from_pretrained("bert-base-uncased")
llama = AutoModel.from_pretrained("meta-llama/Llama-2-7b-hf")

# Multimodal
blip = AutoModel.from_pretrained("Salesforce/blip-image-captioning-base")

Best Practices

1. Use pretrained models when possible
2. Match architecture to task
3. Consider compute budget
4. Scale model size with data size
5. Monitor memory usage