Refactor hopper training and add brain state analysis with resonance space #67
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- Clean up the hopper training loop by removing unnecessary code and improving readability. - Update model loading to handle scenarios where no saved model is available. - Introduce weight checkpointing and automatic recovery mechanisms to stabilize training. - Implement adaptive noise scaling and warmup periods to enhance performance. - Improve tensor handling for state inputs across different devices. These changes aim to improve the maintainability and robustness of the training process.
Reviewer's Guide by SourceryThe pull request implements a comprehensive refactoring and enhancement of the hopper training process through two main components: a layered brain architecture (BrainInABoxV4) and a resonance space system. The changes focus on improving model training stability, state handling, and monitoring capabilities through enhanced architectures, adaptive mechanisms, and visualization tools. Class diagram for BrainInABoxV4classDiagram
class BrainInABoxV4 {
+int vocab_size
+int embed_dim
+int hidden_dim
+float dropout
+int memory_size
+int num_layers
+int num_heads
+string activation
+nn.Embedding embedding
+nn.TransformerEncoder reasoning
+nn.Sequential state_repr
+nn.Linear output_projection
+deque memory_states
+deque attention_patterns
+list attention_maps
+dict metrics
+nn.Parameter adaptive_threshold
+nn.Parameter confidence_weights
+StateAnalyzer state_analyzer
+forward(x, state=None, return_attention=False)
+initialize_state(x)
+visualize_brain_activity(show_memory=True, show_attention=True)
}
class StateAnalyzer {
+int hidden_dim
+list state_history
+analyze_state(state)
}
Class diagram for ResonanceSpace and AudioInterfaceclassDiagram
class ResonanceSpace {
+int sample_rate
+int buffer_size
+int memory_depth
+List memory
+List resonance_patterns
+process_input(audio_buffer: np.ndarray) np.ndarray
+_find_dominant_frequencies(spectrum: np.ndarray, freqs: np.ndarray, num_peaks: int) List[float]
+_generate_resonance_patterns(frequencies: List[float])
+_create_response() np.ndarray
}
class AudioInterface {
+ResonanceSpace resonance_space
+pyaudio.PyAudio p
+start_stream()
+stop_stream()
}
class ResonancePattern {
+float frequency
+float amplitude
+float phase
+float duration
}
ResonanceSpace --> ResonancePattern
AudioInterface --> ResonanceSpace
File-Level Changes
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- Remove unnecessary "gymnasium[other]" dependency from requirements.txt. - Ensure pygame is listed correctly for improved compatibility. These changes streamline the requirements for the project, enhancing clarity and ensuring that the necessary dependencies are properly defined.
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Hey @leonvanbokhorst - I've reviewed your changes - here's some feedback:
Overall Comments:
- Consider splitting this into multiple files - separate the core model, visualization, and analysis components. This would improve maintainability and readability.
- Clean up the duplicate imports at the top of the file. Multiple imports of the same modules indicates poor organization.
Here's what I looked at during the review
- 🟡 General issues: 3 issues found
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟡 Complexity: 1 issue found
- 🟢 Documentation: all looks good
Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| response += wave | ||
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| max_val = np.max(np.abs(response)) | ||
| if max_val > 1e-9: |
There was a problem hiding this comment.
issue: Add more robust handling of near-zero maximum values
The current threshold of 1e-9 could still lead to numerical instability. Consider using np.finfo(response.dtype).eps as a more robust threshold and handle the case where max_val is below this threshold.
| audio_buffer = audio_buffer.astype(np.float64) | ||
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| # Update geheugen | ||
| self.memory.append(audio_buffer) |
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suggestion (performance): Use collections.deque instead of list for memory buffer
Using list.pop(0) is O(n) complexity. Replace the memory list with collections.deque which provides O(1) operations for both ends.
from collections import deque
# Earlier in __init__:
self.memory = deque(maxlen=self.memory_depth)
# Replace the append/pop logic with:
self.memory.append(audio_buffer)| import random | ||
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| @dataclass |
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suggestion: Make ResonancePattern immutable using frozen=True
Since ResonancePattern represents a pattern at a specific point in time, it should be immutable. Add frozen=True to the dataclass decorator.
Suggested change
| @dataclass | |
| @dataclass(frozen=True) |
three_layered/layered_brain.py
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| } | ||
| return activations.get(activation_name.lower(), nn.ReLU()) | ||
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| def forward(self, x, state=None, return_attention=False): |
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issue (complexity): Consider extracting monitoring logic into a separate BrainMonitor class to handle state tracking and metrics collection
The forward() method mixes core model logic with monitoring and state management, making it hard to maintain. Consider separating these concerns:
class BrainMonitor:
def __init__(self):
self.memory_states = deque(maxlen=100)
self.attention_patterns = deque(maxlen=100)
self.metrics = {"memory_evolution": [], "attention_dynamics": []}
def on_forward(self, attention_weights, state, confidence):
self.memory_states.append(state.mean(dim=0).detach().cpu())
if attention_weights:
self.attention_patterns.append(self._process_attention(attention_weights))
# Additional monitoring logic...
class BrainInABoxV4(nn.Module):
def __init__(self, ...):
self.monitor = BrainMonitor()
# Other initialization...
def forward(self, x, state=None):
if state is None:
state = self.initialize_state(x)
# Core neural network operations
emb = self.embedding(x)
reasoned = self.reasoning(emb)
new_state = self.state_repr(reasoned[:, -1, :])
# Simple confidence calculation
confidence_score = torch.sigmoid(torch.matmul(new_state, state.transpose(-2, -1)).mean())
new_state = confidence_score * new_state + (1 - confidence_score) * state
output = self.output_projection(reasoned)
# Notify monitor
self.monitor.on_forward(self.get_attention_weights(), new_state, confidence_score)
return output, new_stateThis approach:
- Separates monitoring from core model logic
- Eliminates the need for hooks
- Makes the forward() method focused and clear
- Maintains all existing functionality
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| # Voeg subtiele random variatie toe, niet alleen door random selectie | ||
| result_freqs = [] | ||
| for peak in top_peaks: | ||
| base_freq = pos_freqs[peak] | ||
| result_freqs.append(base_freq * (1 + random.uniform(-0.05, 0.05))) | ||
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| return result_freqs |
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suggestion (code-quality): We've found these issues:
- Convert for loop into list comprehension (
list-comprehension) - Inline variable that is only used once (
inline-variable) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
Suggested change
| # Voeg subtiele random variatie toe, niet alleen door random selectie | |
| result_freqs = [] | |
| for peak in top_peaks: | |
| base_freq = pos_freqs[peak] | |
| result_freqs.append(base_freq * (1 + random.uniform(-0.05, 0.05))) | |
| return result_freqs | |
| return [ | |
| pos_freqs[peak] * (1 + random.uniform(-0.05, 0.05)) | |
| for peak in top_peaks | |
| ] |
three_layered/layered_brain.py
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| if len(self.state_history) < 3: | ||
| return None | ||
| # Implement pattern detection logic |
There was a problem hiding this comment.
suggestion (code-quality): We've found these issues:
- Lift code into else after jump in control flow (
reintroduce-else) - Hoist repeated code outside conditional statement (
hoist-statement-from-if)
Suggested change
| if len(self.state_history) < 3: | |
| return None | |
| # Implement pattern detection logic |
three_layered/layered_brain.py
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| try: | ||
| # Convert to numpy and ensure 2D | ||
| attention_display = attention_map.cpu().numpy() |
There was a problem hiding this comment.
issue (code-quality): Extract code out into function (extract-method)
three_layered/layered_brain.py
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| def compare_text_types(self): | ||
| """Compare patterns between narrative and analytical texts""" | ||
| if not all( |
There was a problem hiding this comment.
issue (code-quality): We've found these issues:
- Invert any/all to simplify comparisons (
invert-any-all) - Extract duplicate code into method (
extract-duplicate-method)
three_layered/layered_brain.py
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| if not all( | ||
| len(self.pattern_store[t]["memory"]) > 0 |
There was a problem hiding this comment.
suggestion (code-quality): Invert any/all to simplify comparisons (invert-any-all)
Suggested change
| if not all( | |
| len(self.pattern_store[t]["memory"]) > 0 | |
| if any( | |
| len(self.pattern_store[t]["memory"]) <= 0 |
three_layered/layered_brain.py
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| temporal_features = { | ||
| "memory_evolution": [], | ||
| "attention_shifts": [], | ||
| "state_transitions": [], | ||
| } |
There was a problem hiding this comment.
issue (code-quality): Inline variable that is immediately returned (inline-immediately-returned-variable)
leonvanbokhorst
changed the title
sourcery-ai
bot
changed the title
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These changes aim to improve the maintainability and robustness of the training process.
Summary by Sourcery
Refactor and enhance the hopper training process by cleaning up the training loop, updating model loading, and improving tensor handling. Introduce new features such as weight checkpointing, automatic recovery, adaptive noise scaling, and warmup periods to stabilize and enhance training. Add a new component for brain state analysis and develop a resonance space for audio processing.
New Features:
Enhancements: