1120 lines
39 KiB
Markdown
1120 lines
39 KiB
Markdown
# AI Enhancement Implementation: Advanced Neural Networks
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## Overview
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This document provides detailed implementation guidance for AI enhancement, focusing on advanced neural networks that leverage every available terrestrial, satellite, and auxiliary channel for seamless integration.
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## 1. Advanced Neural Network Architecture
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### 1.1 3D Transformer Implementation
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```python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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from dataclasses import dataclass
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@dataclass
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class Transformer3DConfig:
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d_model: int = 512
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n_heads: int = 8
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n_layers: int = 6
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d_ff: int = 2048
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dropout: float = 0.1
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max_seq_length: int = 1024
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spatial_dimensions: int = 3
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class Transformer3D(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.d_model = config.d_model
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self.n_heads = config.n_heads
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# 3D positional encoding
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self.pos_encoder = PositionalEncoding3D(config)
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# Multi-head attention layers
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self.attention_layers = nn.ModuleList([
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MultiHeadAttention3D(config) for _ in range(config.n_layers)
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])
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# Feed-forward layers
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self.feed_forward_layers = nn.ModuleList([
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FeedForward3D(config) for _ in range(config.n_layers)
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])
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# Layer normalization
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self.layer_norms = nn.ModuleList([
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nn.LayerNorm(config.d_model) for _ in range(config.n_layers * 2)
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])
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# Output projection
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self.output_projection = nn.Linear(config.d_model, config.d_model)
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def forward(self, x: torch.Tensor, spatial_positions: torch.Tensor) -> torch.Tensor:
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"""Forward pass through 3D transformer"""
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# Task: Implement 3D transformer forward pass
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# - 3D positional encoding
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# - Multi-head attention
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# - Spatial relationship modeling
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# - Cross-modal attention
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batch_size, seq_len, _ = x.shape
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# Apply 3D positional encoding
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x = self.pos_encoder(x, spatial_positions)
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# Process through transformer layers
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for i in range(self.config.n_layers):
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# Self-attention
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attn_output = self.attention_layers[i](x, x, x)
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x = self.layer_norms[i * 2](x + attn_output)
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# Feed-forward
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ff_output = self.feed_forward_layers[i](x)
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x = self.layer_norms[i * 2 + 1](x + ff_output)
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# Output projection
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output = self.output_projection(x)
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return output
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class PositionalEncoding3D(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.spatial_encoding = SpatialEncoding3D(config)
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self.temporal_encoding = TemporalEncoding(config)
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def forward(self, x: torch.Tensor, spatial_positions: torch.Tensor) -> torch.Tensor:
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"""Apply 3D positional encoding"""
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# Implementation for 3D positional encoding
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# - Spatial position encoding
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# - Temporal position encoding
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# - Coordinate system transformation
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# - Multi-scale encoding
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# Apply spatial encoding
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spatial_encoding = self.spatial_encoding(spatial_positions)
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# Apply temporal encoding
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temporal_encoding = self.temporal_encoding(x)
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# Combine encodings
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combined_encoding = spatial_encoding + temporal_encoding
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return x + combined_encoding
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class SpatialEncoding3D(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.spatial_embedding = nn.Linear(3, config.d_model)
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self.scale_embeddings = nn.ModuleList([
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nn.Linear(config.d_model, config.d_model)
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for _ in range(4) # 4 different scales
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])
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def forward(self, spatial_positions: torch.Tensor) -> torch.Tensor:
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"""Generate spatial encoding for 3D positions"""
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# Implementation for spatial encoding
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# - 3D coordinate embedding
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# - Multi-scale representation
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# - Spatial relationship modeling
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# - Coordinate system transformation
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batch_size, seq_len, _ = spatial_positions.shape
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# Embed 3D coordinates
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spatial_embedding = self.spatial_embedding(spatial_positions)
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# Multi-scale encoding
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multi_scale_encoding = torch.zeros_like(spatial_embedding)
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for i, scale_embedding in enumerate(self.scale_embeddings):
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scale_factor = 2 ** i
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scaled_positions = spatial_positions * scale_factor
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scale_encoding = scale_embedding(spatial_embedding)
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multi_scale_encoding += scale_encoding
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return multi_scale_encoding
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class MultiHeadAttention3D(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.d_k = config.d_model // config.n_heads
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self.d_v = config.d_model // config.n_heads
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# Linear transformations
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self.w_q = nn.Linear(config.d_model, config.d_model)
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self.w_k = nn.Linear(config.d_model, config.d_model)
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self.w_v = nn.Linear(config.d_model, config.d_model)
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self.w_o = nn.Linear(config.d_model, config.d_model)
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# Spatial attention
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self.spatial_attention = SpatialAttention3D(config)
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self.dropout = nn.Dropout(config.dropout)
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def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
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"""Multi-head attention with spatial modeling"""
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# Implementation for 3D multi-head attention
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# - Spatial relationship modeling
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# - Cross-modal attention
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# - Geometric constraints
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# - Attention visualization
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batch_size, seq_len, d_model = query.shape
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# Linear transformations
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Q = self.w_q(query).view(batch_size, seq_len, self.config.n_heads, self.d_k)
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K = self.w_k(key).view(batch_size, seq_len, self.config.n_heads, self.d_k)
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V = self.w_v(value).view(batch_size, seq_len, self.config.n_heads, self.d_v)
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# Transpose for attention computation
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Q = Q.transpose(1, 2)
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K = K.transpose(1, 2)
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V = V.transpose(1, 2)
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# Compute attention scores
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scores = torch.matmul(Q, K.transpose(-2, -1)) / np.sqrt(self.d_k)
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# Apply spatial attention
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spatial_scores = self.spatial_attention(Q, K)
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scores = scores + spatial_scores
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# Apply attention weights
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attention_weights = F.softmax(scores, dim=-1)
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attention_weights = self.dropout(attention_weights)
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# Apply to values
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context = torch.matmul(attention_weights, V)
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# Reshape and apply output projection
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context = context.transpose(1, 2).contiguous().view(
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batch_size, seq_len, d_model
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)
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output = self.w_o(context)
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return output
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class SpatialAttention3D(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.spatial_projection = nn.Linear(3, config.d_model // config.n_heads)
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self.distance_attention = DistanceAttention(config)
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def forward(self, Q: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
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"""Compute spatial attention scores"""
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# Implementation for spatial attention
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# - Distance-based attention
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# - Geometric relationships
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# - Spatial constraints
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# - Multi-scale attention
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batch_size, n_heads, seq_len, d_k = Q.shape
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# Compute spatial relationships
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spatial_scores = self.distance_attention(Q, K)
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return spatial_scores
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class DistanceAttention(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.distance_embedding = nn.Linear(1, config.d_model // config.n_heads)
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self.attention_weights = nn.Parameter(torch.randn(config.n_heads))
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def forward(self, Q: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
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"""Compute distance-based attention"""
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# Implementation for distance attention
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# - Euclidean distance computation
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# - Distance embedding
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# - Attention weight learning
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# - Geometric constraints
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batch_size, n_heads, seq_len, d_k = Q.shape
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# Compute distances (simplified - in practice would use actual spatial positions)
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distances = torch.cdist(Q.view(-1, d_k), K.view(-1, d_k))
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distances = distances.view(batch_size, n_heads, seq_len, seq_len)
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# Embed distances
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distance_embedding = self.distance_embedding(distances.unsqueeze(-1))
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# Apply learned attention weights
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attention_scores = distance_embedding * self.attention_weights.view(1, -1, 1, 1)
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return attention_scores
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```
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### 1.2 Attention Mechanism Design
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```python
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class AttentionMechanism(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.self_attention = SelfAttention3D(config)
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self.cross_attention = CrossAttention3D(config)
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self.temporal_attention = TemporalAttention(config)
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self.hierarchical_attention = HierarchicalAttention(config)
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def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None) -> torch.Tensor:
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"""Apply multiple attention mechanisms"""
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# Task: Implement advanced attention mechanisms
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# - Self-attention for spatial relationships
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# - Cross-attention for multi-modal fusion
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# - Temporal attention for sequence modeling
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# - Hierarchical attention for scale invariance
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# Self-attention
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self_attended = self.self_attention(x)
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# Cross-attention if context provided
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if context is not None:
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cross_attended = self.cross_attention(self_attended, context)
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else:
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cross_attended = self_attended
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# Temporal attention
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temporal_attended = self.temporal_attention(cross_attended)
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# Hierarchical attention
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hierarchical_attended = self.hierarchical_attention(temporal_attended)
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return hierarchical_attended
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class SelfAttention3D(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.attention = MultiHeadAttention3D(config)
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self.spatial_encoder = SpatialEncoder3D(config)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Self-attention with spatial encoding"""
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# Implementation for self-attention
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# - Spatial relationship modeling
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# - Geometric constraints
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# - Attention visualization
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# - Performance optimization
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# Encode spatial relationships
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spatial_encoded = self.spatial_encoder(x)
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# Apply self-attention
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attended = self.attention(spatial_encoded, spatial_encoded, spatial_encoded)
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return attended
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class CrossAttention3D(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.attention = MultiHeadAttention3D(config)
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self.modality_fusion = ModalityFusion(config)
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def forward(self, query: torch.Tensor, key_value: torch.Tensor) -> torch.Tensor:
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"""Cross-attention for multi-modal fusion"""
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# Implementation for cross-attention
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# - Multi-modal fusion
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# - Cross-domain attention
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# - Modality alignment
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# - Feature integration
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# Apply cross-attention
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attended = self.attention(query, key_value, key_value)
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# Fuse modalities
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fused = self.modality_fusion(attended, key_value)
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return fused
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class ModalityFusion(nn.Module):
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def __init__(self, config: Transformer3DConfig):
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super().__init__()
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self.config = config
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self.fusion_gate = nn.Linear(config.d_model * 2, config.d_model)
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self.fusion_weights = nn.Parameter(torch.randn(2))
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def forward(self, attended: torch.Tensor, context: torch.Tensor) -> torch.Tensor:
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"""Fuse attended features with context"""
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# Implementation for modality fusion
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# - Gated fusion
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# - Weighted combination
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# - Feature alignment
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# - Cross-modal integration
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# Concatenate features
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combined = torch.cat([attended, context], dim=-1)
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# Apply gated fusion
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gate = torch.sigmoid(self.fusion_gate(combined))
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# Weighted combination
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fused = gate * attended + (1 - gate) * context
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return fused
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```
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## 2. Meta-Learning Framework
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### 2.1 Model-Agnostic Meta-Learning (MAML)
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```python
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class MAML(nn.Module):
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def __init__(self, model: nn.Module, config: MAMLConfig):
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super().__init__()
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self.model = model
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self.config = config
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self.meta_optimizer = torch.optim.Adam(self.model.parameters(), lr=config.meta_lr)
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self.task_generator = TaskGenerator(config)
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def forward(self, support_data: torch.Tensor, query_data: torch.Tensor) -> torch.Tensor:
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"""MAML forward pass"""
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# Task: Implement MAML forward pass
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# - Fast adaptation to new tasks
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# - Few-shot learning
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# - Cross-domain generalization
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# - Continuous learning
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# Generate task
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task = self.task_generator.generate_task(support_data, query_data)
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# Inner loop adaptation
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adapted_model = self.inner_loop_adaptation(task.support_data, task.support_labels)
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# Outer loop evaluation
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predictions = adapted_model(task.query_data)
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return predictions
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def inner_loop_adaptation(self, support_data: torch.Tensor, support_labels: torch.Tensor) -> nn.Module:
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"""Inner loop adaptation for fast learning"""
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# Implementation for inner loop adaptation
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# - Gradient-based adaptation
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# - Parameter updates
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# - Loss computation
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# - Optimization
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# Create copy of model for adaptation
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adapted_model = copy.deepcopy(self.model)
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inner_optimizer = torch.optim.SGD(adapted_model.parameters(), lr=self.config.inner_lr)
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for _ in range(self.config.inner_steps):
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# Forward pass
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predictions = adapted_model(support_data)
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# Compute loss
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loss = F.cross_entropy(predictions, support_labels)
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# Backward pass
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inner_optimizer.zero_grad()
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loss.backward()
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inner_optimizer.step()
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return adapted_model
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def meta_update(self, tasks: List[Task]):
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"""Meta-update using multiple tasks"""
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# Implementation for meta-update
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# - Task sampling
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# - Gradient accumulation
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# - Meta-optimization
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# - Performance evaluation
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meta_loss = 0.0
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for task in tasks:
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# Inner loop adaptation
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adapted_model = self.inner_loop_adaptation(task.support_data, task.support_labels)
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# Query set evaluation
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query_predictions = adapted_model(task.query_data)
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query_loss = F.cross_entropy(query_predictions, task.query_labels)
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meta_loss += query_loss
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# Average meta loss
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meta_loss /= len(tasks)
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# Meta-update
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self.meta_optimizer.zero_grad()
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meta_loss.backward()
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self.meta_optimizer.step()
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return meta_loss
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@dataclass
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class MAMLConfig:
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meta_lr: float = 0.001
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inner_lr: float = 0.01
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inner_steps: int = 5
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n_tasks: int = 4
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n_shot: int = 5
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n_query: int = 15
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```
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### 2.2 Few-Shot Learning
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```python
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class FewShotLearner(nn.Module):
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def __init__(self, config: FewShotConfig):
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super().__init__()
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self.config = config
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self.encoder = PrototypicalEncoder(config)
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self.prototypical_net = PrototypicalNetwork(config)
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self.matching_net = MatchingNetwork(config)
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self.relation_net = RelationNetwork(config)
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def forward(self, support_data: torch.Tensor, query_data: torch.Tensor,
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support_labels: torch.Tensor) -> torch.Tensor:
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"""Few-shot learning forward pass"""
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# Task: Implement few-shot learning
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# - Prototypical networks
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# - Matching networks
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# - Relation networks
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# - Meta-learning integration
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# Encode support and query data
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support_encoded = self.encoder(support_data)
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query_encoded = self.encoder(query_data)
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# Apply few-shot learning method
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if self.config.method == "prototypical":
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predictions = self.prototypical_net(support_encoded, query_encoded, support_labels)
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elif self.config.method == "matching":
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predictions = self.matching_net(support_encoded, query_encoded, support_labels)
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elif self.config.method == "relation":
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predictions = self.relation_net(support_encoded, query_encoded, support_labels)
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else:
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raise ValueError(f"Unknown few-shot method: {self.config.method}")
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return predictions
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class PrototypicalNetwork(nn.Module):
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def __init__(self, config: FewShotConfig):
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super().__init__()
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self.config = config
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def forward(self, support_encoded: torch.Tensor, query_encoded: torch.Tensor,
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support_labels: torch.Tensor) -> torch.Tensor:
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"""Prototypical network forward pass"""
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# Implementation for prototypical networks
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# - Prototype computation
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# - Distance calculation
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# - Classification
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# - Uncertainty estimation
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# Compute prototypes
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prototypes = self.compute_prototypes(support_encoded, support_labels)
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# Compute distances
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distances = self.compute_distances(query_encoded, prototypes)
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# Convert to probabilities
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logits = -distances
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probabilities = F.softmax(logits, dim=-1)
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return probabilities
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def compute_prototypes(self, support_encoded: torch.Tensor, support_labels: torch.Tensor) -> torch.Tensor:
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"""Compute class prototypes"""
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# Implementation for prototype computation
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# - Class-wise averaging
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# - Prototype refinement
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# - Outlier handling
|
|
# - Prototype validation
|
|
|
|
unique_labels = torch.unique(support_labels)
|
|
prototypes = []
|
|
|
|
for label in unique_labels:
|
|
# Get samples for this class
|
|
class_mask = (support_labels == label)
|
|
class_samples = support_encoded[class_mask]
|
|
|
|
# Compute prototype (mean)
|
|
prototype = class_samples.mean(dim=0)
|
|
prototypes.append(prototype)
|
|
|
|
return torch.stack(prototypes)
|
|
|
|
def compute_distances(self, query_encoded: torch.Tensor, prototypes: torch.Tensor) -> torch.Tensor:
|
|
"""Compute Euclidean distances"""
|
|
# Implementation for distance computation
|
|
# - Euclidean distance
|
|
# - Distance normalization
|
|
# - Metric learning
|
|
# - Distance weighting
|
|
|
|
# Compute Euclidean distances
|
|
distances = torch.cdist(query_encoded, prototypes)
|
|
|
|
return distances
|
|
|
|
@dataclass
|
|
class FewShotConfig:
|
|
method: str = "prototypical" # "prototypical", "matching", "relation"
|
|
n_way: int = 5
|
|
n_shot: int = 5
|
|
n_query: int = 15
|
|
embedding_dim: int = 64
|
|
```
|
|
|
|
## 3. Federated Learning
|
|
|
|
### 3.1 Federated Aggregation
|
|
|
|
```python
|
|
class FederatedLearning:
|
|
def __init__(self, config: FederatedConfig):
|
|
self.config = config
|
|
self.federated_aggregator = FederatedAggregator(config)
|
|
self.privacy_preservation = PrivacyPreservation(config)
|
|
self.communication_optimizer = CommunicationOptimizer(config)
|
|
self.quality_assurance = QualityAssurance(config)
|
|
|
|
async def federated_training(self, clients: List[Client], global_model: nn.Module):
|
|
"""Federated training process"""
|
|
# Task: Implement federated learning
|
|
# - Secure aggregation
|
|
# - Differential privacy
|
|
# - Communication optimization
|
|
# - Quality assurance
|
|
|
|
for round in range(self.config.n_rounds):
|
|
# Client training
|
|
client_models = await self.train_clients(clients, global_model)
|
|
|
|
# Secure aggregation
|
|
aggregated_model = await self.federated_aggregator.aggregate(client_models)
|
|
|
|
# Privacy preservation
|
|
private_model = await self.privacy_preservation.apply_privacy(aggregated_model)
|
|
|
|
# Update global model
|
|
global_model.load_state_dict(private_model.state_dict())
|
|
|
|
# Quality assurance
|
|
quality_metrics = await self.quality_assurance.evaluate_quality(global_model)
|
|
|
|
# Communication optimization
|
|
await self.communication_optimizer.optimize_communication(clients)
|
|
|
|
class FederatedAggregator:
|
|
def __init__(self, config: FederatedConfig):
|
|
self.config = config
|
|
self.aggregation_methods = {
|
|
'fedavg': FedAvgAggregator(),
|
|
'fedprox': FedProxAggregator(),
|
|
'scaffold': ScaffoldAggregator(),
|
|
'secure': SecureAggregator()
|
|
}
|
|
|
|
async def aggregate(self, client_models: List[nn.Module]) -> nn.Module:
|
|
"""Aggregate client models securely"""
|
|
# Implementation for federated aggregation
|
|
# - FedAvg aggregation
|
|
# - Secure aggregation
|
|
# - Weighted averaging
|
|
# - Outlier detection
|
|
|
|
# Select aggregation method
|
|
aggregator = self.aggregation_methods[self.config.aggregation_method]
|
|
|
|
# Perform aggregation
|
|
aggregated_model = await aggregator.aggregate(client_models)
|
|
|
|
return aggregated_model
|
|
|
|
class FedAvgAggregator:
|
|
async def aggregate(self, client_models: List[nn.Module]) -> nn.Module:
|
|
"""Federated Averaging aggregation"""
|
|
# Implementation for FedAvg
|
|
# - Weight averaging
|
|
# - Client weighting
|
|
# - Convergence analysis
|
|
# - Performance optimization
|
|
|
|
# Get global model structure
|
|
global_model = copy.deepcopy(client_models[0])
|
|
|
|
# Initialize aggregated weights
|
|
aggregated_state = {}
|
|
|
|
# Aggregate each parameter
|
|
for param_name in global_model.state_dict().keys():
|
|
param_tensors = [model.state_dict()[param_name] for model in client_models]
|
|
|
|
# Weighted average (assuming equal weights for simplicity)
|
|
weights = torch.ones(len(client_models)) / len(client_models)
|
|
aggregated_param = sum(w * p for w, p in zip(weights, param_tensors))
|
|
|
|
aggregated_state[param_name] = aggregated_param
|
|
|
|
# Update global model
|
|
global_model.load_state_dict(aggregated_state)
|
|
|
|
return global_model
|
|
|
|
class SecureAggregator:
|
|
def __init__(self):
|
|
self.encryption = HomomorphicEncryption()
|
|
self.secure_sum = SecureSum()
|
|
|
|
async def aggregate(self, client_models: List[nn.Module]) -> nn.Module:
|
|
"""Secure aggregation with privacy preservation"""
|
|
# Implementation for secure aggregation
|
|
# - Homomorphic encryption
|
|
# - Secure multi-party computation
|
|
# - Differential privacy
|
|
# - Privacy guarantees
|
|
|
|
# Encrypt client models
|
|
encrypted_models = []
|
|
for model in client_models:
|
|
encrypted_model = await self.encryption.encrypt_model(model)
|
|
encrypted_models.append(encrypted_model)
|
|
|
|
# Secure aggregation
|
|
aggregated_encrypted = await self.secure_sum.secure_sum(encrypted_models)
|
|
|
|
# Decrypt aggregated model
|
|
aggregated_model = await self.encryption.decrypt_model(aggregated_encrypted)
|
|
|
|
return aggregated_model
|
|
|
|
@dataclass
|
|
class FederatedConfig:
|
|
n_rounds: int = 100
|
|
n_clients: int = 10
|
|
aggregation_method: str = "fedavg" # "fedavg", "fedprox", "scaffold", "secure"
|
|
privacy_budget: float = 1.0
|
|
communication_rounds: int = 5
|
|
```
|
|
|
|
### 3.2 Privacy Preservation
|
|
|
|
```python
|
|
class PrivacyPreservation:
|
|
def __init__(self, config: FederatedConfig):
|
|
self.config = config
|
|
self.differential_privacy = DifferentialPrivacy(config)
|
|
self.homomorphic_encryption = HomomorphicEncryption()
|
|
self.secure_computation = SecureComputation(config)
|
|
self.audit_logger = AuditLogger()
|
|
|
|
async def apply_privacy(self, model: nn.Module) -> nn.Module:
|
|
"""Apply privacy preservation to model"""
|
|
# Task: Implement privacy preservation
|
|
# - Differential privacy
|
|
# - Homomorphic encryption
|
|
# - Secure computation
|
|
# - Audit logging
|
|
|
|
# Apply differential privacy
|
|
private_model = await self.differential_privacy.apply_dp(model)
|
|
|
|
# Apply homomorphic encryption if needed
|
|
if self.config.use_encryption:
|
|
encrypted_model = await self.homomorphic_encryption.encrypt_model(private_model)
|
|
private_model = encrypted_model
|
|
|
|
# Log privacy actions
|
|
await self.audit_logger.log_privacy_action("model_privacy", "differential_privacy")
|
|
|
|
return private_model
|
|
|
|
class DifferentialPrivacy:
|
|
def __init__(self, config: FederatedConfig):
|
|
self.config = config
|
|
self.noise_scale = config.privacy_budget
|
|
self.sensitivity_calculator = SensitivityCalculator()
|
|
|
|
async def apply_dp(self, model: nn.Module) -> nn.Module:
|
|
"""Apply differential privacy to model"""
|
|
# Implementation for differential privacy
|
|
# - Noise addition
|
|
# - Sensitivity calculation
|
|
# - Privacy budget management
|
|
# - Privacy guarantees
|
|
|
|
# Calculate sensitivity
|
|
sensitivity = await self.sensitivity_calculator.calculate_sensitivity(model)
|
|
|
|
# Add noise
|
|
noisy_model = await self.add_noise(model, sensitivity)
|
|
|
|
return noisy_model
|
|
|
|
async def add_noise(self, model: nn.Module, sensitivity: float) -> nn.Module:
|
|
"""Add calibrated noise to model parameters"""
|
|
# Implementation for noise addition
|
|
# - Gaussian noise
|
|
# - Laplace noise
|
|
# - Noise calibration
|
|
# - Privacy analysis
|
|
|
|
noisy_model = copy.deepcopy(model)
|
|
|
|
for param_name, param in noisy_model.named_parameters():
|
|
# Calculate noise scale
|
|
noise_scale = sensitivity / self.config.privacy_budget
|
|
|
|
# Add Gaussian noise
|
|
noise = torch.randn_like(param) * noise_scale
|
|
param.data += noise
|
|
|
|
return noisy_model
|
|
|
|
class HomomorphicEncryption:
|
|
def __init__(self):
|
|
self.encryption_scheme = PaillierEncryption()
|
|
self.key_manager = KeyManager()
|
|
|
|
async def encrypt_model(self, model: nn.Module) -> EncryptedModel:
|
|
"""Encrypt model using homomorphic encryption"""
|
|
# Implementation for homomorphic encryption
|
|
# - Paillier encryption
|
|
# - Key management
|
|
# - Encrypted computation
|
|
# - Decryption
|
|
|
|
# Generate keys
|
|
public_key, private_key = await self.key_manager.generate_keys()
|
|
|
|
# Encrypt model parameters
|
|
encrypted_state = {}
|
|
for param_name, param in model.state_dict().items():
|
|
encrypted_param = await self.encryption_scheme.encrypt(param, public_key)
|
|
encrypted_state[param_name] = encrypted_param
|
|
|
|
return EncryptedModel(encrypted_state, public_key)
|
|
|
|
async def decrypt_model(self, encrypted_model: EncryptedModel) -> nn.Module:
|
|
"""Decrypt model"""
|
|
# Implementation for model decryption
|
|
# - Parameter decryption
|
|
# - Key management
|
|
# - Model reconstruction
|
|
# - Validation
|
|
|
|
# Decrypt parameters
|
|
decrypted_state = {}
|
|
for param_name, encrypted_param in encrypted_model.state_dict.items():
|
|
decrypted_param = await self.encryption_scheme.decrypt(
|
|
encrypted_param, encrypted_model.private_key
|
|
)
|
|
decrypted_state[param_name] = decrypted_param
|
|
|
|
# Reconstruct model
|
|
model = self.reconstruct_model(decrypted_state)
|
|
|
|
return model
|
|
```
|
|
|
|
## 4. Advanced AI Applications
|
|
|
|
### 4.1 Advanced Computer Vision
|
|
|
|
```python
|
|
class AdvancedComputerVision:
|
|
def __init__(self, config: VisionConfig):
|
|
self.config = config
|
|
self.instance_segmentation = InstanceSegmentation(config)
|
|
self.depth_estimation = DepthEstimation(config)
|
|
self.optical_flow = OpticalFlow(config)
|
|
self.object_tracking = ObjectTracking(config)
|
|
|
|
async def process_frame(self, frame: torch.Tensor) -> VisionResults:
|
|
"""Process frame with advanced computer vision"""
|
|
# Task: Implement advanced computer vision
|
|
# - Instance segmentation
|
|
# - Depth estimation
|
|
# - Optical flow
|
|
# - Object tracking
|
|
|
|
# Instance segmentation
|
|
segmentation = await self.instance_segmentation.segment(frame)
|
|
|
|
# Depth estimation
|
|
depth = await self.depth_estimation.estimate_depth(frame)
|
|
|
|
# Optical flow
|
|
flow = await self.optical_flow.compute_flow(frame)
|
|
|
|
# Object tracking
|
|
tracking = await self.object_tracking.track_objects(frame)
|
|
|
|
return VisionResults(segmentation, depth, flow, tracking)
|
|
|
|
class InstanceSegmentation(nn.Module):
|
|
def __init__(self, config: VisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.backbone = ResNetBackbone(config)
|
|
self.fpn = FeaturePyramidNetwork(config)
|
|
self.mask_head = MaskHead(config)
|
|
self.box_head = BoxHead(config)
|
|
|
|
async def segment(self, frame: torch.Tensor) -> SegmentationResult:
|
|
"""Perform instance segmentation"""
|
|
# Implementation for instance segmentation
|
|
# - Feature extraction
|
|
# - Proposal generation
|
|
# - Mask prediction
|
|
# - Post-processing
|
|
|
|
# Extract features
|
|
features = self.backbone(frame)
|
|
|
|
# Feature pyramid
|
|
pyramid_features = self.fpn(features)
|
|
|
|
# Generate proposals
|
|
proposals = await self.generate_proposals(pyramid_features)
|
|
|
|
# Predict masks
|
|
masks = await self.mask_head.predict_masks(pyramid_features, proposals)
|
|
|
|
# Predict boxes
|
|
boxes = await self.box_head.predict_boxes(pyramid_features, proposals)
|
|
|
|
# Post-process
|
|
results = await self.post_process(masks, boxes)
|
|
|
|
return results
|
|
|
|
async def generate_proposals(self, features: List[torch.Tensor]) -> torch.Tensor:
|
|
"""Generate object proposals"""
|
|
# Implementation for proposal generation
|
|
# - Anchor generation
|
|
# - Proposal scoring
|
|
# - Non-maximum suppression
|
|
# - Proposal refinement
|
|
|
|
# Generate anchors
|
|
anchors = self.generate_anchors(features)
|
|
|
|
# Score proposals
|
|
proposal_scores = self.score_proposals(features, anchors)
|
|
|
|
# Apply NMS
|
|
filtered_proposals = self.apply_nms(anchors, proposal_scores)
|
|
|
|
return filtered_proposals
|
|
|
|
class DepthEstimation(nn.Module):
|
|
def __init__(self, config: VisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.encoder = DepthEncoder(config)
|
|
self.decoder = DepthDecoder(config)
|
|
self.uncertainty_estimator = UncertaintyEstimator(config)
|
|
|
|
async def estimate_depth(self, frame: torch.Tensor) -> DepthResult:
|
|
"""Estimate depth from monocular image"""
|
|
# Implementation for depth estimation
|
|
# - Monocular depth estimation
|
|
# - Multi-view stereo
|
|
# - Uncertainty quantification
|
|
# - Depth refinement
|
|
|
|
# Encode features
|
|
encoded_features = self.encoder(frame)
|
|
|
|
# Decode depth
|
|
depth_map = self.decoder(encoded_features)
|
|
|
|
# Estimate uncertainty
|
|
uncertainty = await self.uncertainty_estimator.estimate_uncertainty(depth_map)
|
|
|
|
# Refine depth
|
|
refined_depth = await self.refine_depth(depth_map, uncertainty)
|
|
|
|
return DepthResult(refined_depth, uncertainty)
|
|
|
|
async def refine_depth(self, depth_map: torch.Tensor, uncertainty: torch.Tensor) -> torch.Tensor:
|
|
"""Refine depth estimation"""
|
|
# Implementation for depth refinement
|
|
# - Multi-scale refinement
|
|
# - Uncertainty-aware refinement
|
|
# - Temporal consistency
|
|
# - Geometric constraints
|
|
|
|
# Multi-scale refinement
|
|
refined_depth = depth_map
|
|
for scale in [1.0, 0.5, 0.25]:
|
|
scaled_depth = F.interpolate(depth_map, scale_factor=scale)
|
|
refined_depth = await self.refine_at_scale(refined_depth, scaled_depth, uncertainty)
|
|
|
|
return refined_depth
|
|
|
|
@dataclass
|
|
class VisionConfig:
|
|
backbone: str = "resnet50"
|
|
fpn_channels: int = 256
|
|
num_classes: int = 80
|
|
min_size: int = 800
|
|
max_size: int = 1333
|
|
rpn_batch_size_per_image: int = 256
|
|
rpn_positive_fraction: float = 0.5
|
|
box_batch_size_per_image: int = 512
|
|
box_positive_fraction: float = 0.25
|
|
bbox_reg_weights: Tuple[float, ...] = (1.0, 1.0, 1.0, 1.0)
|
|
```
|
|
|
|
### 4.2 Natural Language Processing
|
|
|
|
```python
|
|
class NaturalLanguageProcessing:
|
|
def __init__(self, config: NLPConfig):
|
|
self.config = config
|
|
self.speech_recognition = SpeechRecognition(config)
|
|
self.language_understanding = LanguageUnderstanding(config)
|
|
self.dialogue_system = DialogueSystem(config)
|
|
|
|
async def process_input(self, input_data: Union[str, torch.Tensor]) -> NLPResult:
|
|
"""Process natural language input"""
|
|
# Task: Implement natural language processing
|
|
# - Speech recognition
|
|
# - Language understanding
|
|
# - Dialogue management
|
|
# - Response generation
|
|
|
|
# Speech recognition if audio input
|
|
if isinstance(input_data, torch.Tensor):
|
|
text = await self.speech_recognition.recognize_speech(input_data)
|
|
else:
|
|
text = input_data
|
|
|
|
# Language understanding
|
|
understanding = await self.language_understanding.understand(text)
|
|
|
|
# Dialogue management
|
|
response = await self.dialogue_system.generate_response(understanding)
|
|
|
|
return NLPResult(text, understanding, response)
|
|
|
|
class SpeechRecognition(nn.Module):
|
|
def __init__(self, config: NLPConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.feature_extractor = AudioFeatureExtractor(config)
|
|
self.acoustic_model = AcousticModel(config)
|
|
self.language_model = LanguageModel(config)
|
|
self.decoder = SpeechDecoder(config)
|
|
|
|
async def recognize_speech(self, audio: torch.Tensor) -> str:
|
|
"""Recognize speech from audio"""
|
|
# Implementation for speech recognition
|
|
# - Feature extraction
|
|
# - Acoustic modeling
|
|
# - Language modeling
|
|
# - Decoding
|
|
|
|
# Extract features
|
|
features = await self.feature_extractor.extract_features(audio)
|
|
|
|
# Acoustic modeling
|
|
acoustic_output = await self.acoustic_model(features)
|
|
|
|
# Language modeling
|
|
language_output = await self.language_model(acoustic_output)
|
|
|
|
# Decode
|
|
transcription = await self.decoder.decode(acoustic_output, language_output)
|
|
|
|
return transcription
|
|
|
|
async def extract_features(self, audio: torch.Tensor) -> torch.Tensor:
|
|
"""Extract audio features"""
|
|
# Implementation for feature extraction
|
|
# - Mel-frequency cepstral coefficients
|
|
# - Spectrogram computation
|
|
# - Feature normalization
|
|
# - Temporal alignment
|
|
|
|
# Compute spectrogram
|
|
spectrogram = torch.stft(audio, n_fft=1024, hop_length=256)
|
|
|
|
# Convert to mel spectrogram
|
|
mel_spectrogram = self.mel_filterbank(spectrogram)
|
|
|
|
# Apply log
|
|
log_mel = torch.log(mel_spectrogram + 1e-8)
|
|
|
|
# Normalize
|
|
normalized_features = self.normalize_features(log_mel)
|
|
|
|
return normalized_features
|
|
|
|
class DialogueSystem:
|
|
def __init__(self, config: NLPConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.context_manager = ContextManager(config)
|
|
self.response_generator = ResponseGenerator(config)
|
|
self.personality_engine = PersonalityEngine(config)
|
|
|
|
async def generate_response(self, understanding: LanguageUnderstanding) -> str:
|
|
"""Generate contextual response"""
|
|
# Implementation for dialogue system
|
|
# - Context management
|
|
# - Response generation
|
|
# - Personality adaptation
|
|
# - Multi-turn dialogue
|
|
|
|
# Update context
|
|
context = await self.context_manager.update_context(understanding)
|
|
|
|
# Generate response
|
|
response = await self.response_generator.generate(context)
|
|
|
|
# Apply personality
|
|
personalized_response = await self.personality_engine.apply_personality(response)
|
|
|
|
return personalized_response
|
|
|
|
async def update_context(self, understanding: LanguageUnderstanding) -> DialogueContext:
|
|
"""Update dialogue context"""
|
|
# Implementation for context management
|
|
# - Context tracking
|
|
# - Memory management
|
|
# - Topic modeling
|
|
# - Intent recognition
|
|
|
|
# Extract intent
|
|
intent = await self.extract_intent(understanding)
|
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# Update topic
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topic = await self.update_topic(understanding)
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# Update memory
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memory = await self.update_memory(understanding)
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return DialogueContext(intent, topic, memory)
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|
@dataclass
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class NLPConfig:
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model_name: str = "gpt2"
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max_length: int = 512
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|
num_layers: int = 12
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hidden_size: int = 768
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|
num_attention_heads: int = 12
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vocab_size: int = 50257
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|
dropout: float = 0.1
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|
learning_rate: float = 5e-5
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|
```
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|
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---
|
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*This comprehensive AI enhancement implementation provides detailed guidance for deploying advanced neural networks that leverage every available channel for seamless integration.* |