Redis Connected
Vector Search Active
Semantic Cache: 94.2%

🔧 Model Configuration

Network Architecture

Layers: [128, 256, 512, 256, 128]
Activation: ReLU
Dropout: 0.3

Training Parameters

Learning Rate: 0.001
Batch Size: 32
Optimizer: Adam

🎯 Redis Vector Suggestions

↗ Increase learning rate to 0.0015 (87% similar models)
🔄 Try batch size 64 for better cache utilization
⚡ Add L2 regularization (0.001) based on similar architectures

Similar High-Performance Models

ResNet-Variant-A
Accuracy: 94.7% Similarity: 0.89
DenseNet-Custom
Accuracy: 93.2% Similarity: 0.84
EfficientNet-B2
Accuracy: 92.8% Similarity: 0.81

📊 Real-Time Training Analytics

[2025-08-10 15:42:33] ✓ Redis semantic cache initialized
[2025-08-10 15:42:34] ✓ Vector search index loaded (10,847 models)
[2025-08-10 15:42:35] 📡 Starting training with Redis optimization...
[2025-08-10 15:42:36] ⚡ Cache hit! Forward pass accelerated by 340ms
[2025-08-10 15:42:37] 🎯 Epoch 1/100 - Loss: 0.8234, Acc: 67.8%
[2025-08-10 15:42:38] ⚠ Gradient norm high (3.2) - adjusting learning rate
[2025-08-10 15:42:39] ✓ Learning rate adjusted to 0.0008 via Redis analytics
[2025-08-10 15:42:40] 🚀 Training speed increased by 45% with semantic caching

⚡ Semantic Cache Performance

2,847
Cache Hits
23.4s
Time Saved
0.95
Similarity Threshold
156MB
Redis Memory

📈 Performance Metrics

Current Accuracy
89.4%
Training Loss
0.2847
Speed Improvement
+45.2%
Redis Operations/sec
12,847

🔍 Vector Search Analytics

Real-time Hyperparameter Optimization

Optimal Batch Size: 64
Suggested LR: 0.0012
Next Architecture: [256, 512, 256]