Network Architecture Sizing - Research Notes

Experiment Overview

Context

Training runs produced models at ~72 MB instead of expected ~148 MB. Investigation revealed the hidden_dims configuration determines model size, with the first layer dominating total parameter count due to multiplication with observation dimensions.

Architecture Comparison

Model Size vs Architecture

Why First Layer Dominates

With 53 features × 100 lookback = 5,300 input dimensions:

• Large: 2048 × 5300 = 10.9M params (86% of network)
• Medium: 1024 × 5300 = 5.4M params (89% of network)
• Small: 512 × 5300 = 2.7M params (90% of network)

Key insight: The first hidden layer dimension has exponentially more impact on model size than deeper layers.

Configuration Locations

Current defaults in ppo_trainer_native.py:

Verified Workflow

To use larger architecture (148 MB models):

from alpaca_trading.gpu.ppo_trainer_native import get_auto_config

config = get_auto_config(total_timesteps=200_000_000, training_mode='production')
config.hidden_dims = (2048, 1024, 512, 256)  # Override to 4-layer large

trainer = NativePPOTrainer(env, config)

To verify model architecture before training:

import torch

# Check expected size
obs_dim = 5300  # 53 features × 100 lookback
hidden_dims = (2048, 1024, 512, 256)

params = obs_dim * hidden_dims[0]  # First layer
for i in range(len(hidden_dims) - 1):
    params += hidden_dims[i] * hidden_dims[i+1]
params += hidden_dims[-1] * 64 * 2  # Actor + critic heads

print(f"Expected params: {params:,}")
print(f"Expected size: ~{params * 4 * 3 / 1024 / 1024:.0f} MB")  # float32 × 3 (weights + optimizer state)

To inspect existing model:

import torch

ckpt = torch.load('model.pt', map_location='cpu', weights_only=False)
print(f"hidden_dims: {ckpt['config'].hidden_dims}")
print(f"Total params: {sum(v.numel() for v in ckpt['policy_state_dict'].values()):,}")

Failed Attempts (Critical)

Performance Considerations

Larger Architecture (2048, 1024, 512, 256)

Pros:

• Higher model capacity for complex patterns
• Better for symbols with rich feature interactions
• May capture longer-term dependencies

Cons:

• 2x file size (~148 MB vs ~72 MB)
• Slower inference (~1.5-2x)
• Higher VRAM usage during training
• More prone to overfitting with limited data

Smaller Architecture (1024, 512, 256)

Pros:

• Faster inference (important for live trading)
• Lower VRAM requirements
• Faster training iterations
• Better generalization on limited data

Cons:

• May underfit complex market dynamics
• Less capacity for feature interactions

Recommended Architecture by Use Case

Key Insights

• First layer width dominates model size - doubling first layer ~doubles total params
• File size ≈ params × 12 bytes (float32 weights + Adam optimizer moments)
• Current v2.3 defaults favor smaller models - optimized for speed over capacity
• Architecture mismatch = inference failure - models trained with different hidden_dims are incompatible
• Always log hidden_dims - critical for reproducibility and debugging

Diagnostic Commands

# Compare two model architectures
def compare_models(path1, path2):
    m1 = torch.load(path1, map_location='cpu', weights_only=False)
    m2 = torch.load(path2, map_location='cpu', weights_only=False)

    print(f"Model 1: {m1['config'].hidden_dims}")
    print(f"Model 2: {m2['config'].hidden_dims}")
    print(f"Params 1: {sum(v.numel() for v in m1['policy_state_dict'].values()):,}")
    print(f"Params 2: {sum(v.numel() for v in m2['policy_state_dict'].values()):,}")

References

• alpaca_trading/gpu/ppo_trainer_native.py: Lines 1314, 1339, 1726, 1760
• alpaca_trading/gpu/ppo_trainer_native.py: NativeActorCritic class (line 305)
• CLAUDE.md: GPU Optimized Settings table