DNN Architectures

Modern deep neural network architectures.

Convolutional Neural Networks

import 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

class 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)

class 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

| Architecture | Best For | Params | Inference | |--------------|----------|--------|-----------| | ResNet | Image classification | 25M | Fast | | EfficientNet | Efficient vision | 5-66M | Efficient | | ViT | Vision + scale | 86-632M | GPU optimized | | BERT | NLP understanding | 110-340M | Moderate | | GPT | Text generation | 117M-175B | Heavy | | T5 | Seq2seq tasks | 60M-11B | Heavy |

Modern Architectures

# 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