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andreasjansson
/
create-dynamic-system
- Public
- 58 runs
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CPU
Input
Output
code
class WaveInterferenceOscillator(DynamicSystem):
"""A dynamic system that generates oscillating patterns through wave interactions and
non-linear feedback, with very few parameters."""
def __init__(self):
super().__init__()
# Use a small grid to reduce parameter count
self.grid_size = 16
# Define only 3 channels (RGB)
self.channels = 3
# Wave parameters (wavelength, direction, phase)
self.wave_params = nn.Parameter(torch.rand(3, 4))
# Interaction parameters between channels
self.interaction = nn.Parameter(0.2 * torch.randn(3, 3))
# Feedback strength and bias
self.feedback = nn.Parameter(0.1 * torch.randn(3))
self.bias = nn.Parameter(0.1 * torch.randn(3))
# Non-linearity control
self.activation = nn.Parameter(torch.ones(1) * 2.0)
# Memory decay factor
self.memory = nn.Parameter(torch.tensor([0.7]))
# Core pattern - small 4x4 pattern that we'll tile for efficiency
self.pattern_seed = nn.Parameter(0.1 * torch.randn(3, 4, 4))
# Reset state variables
self.reset_state()
def reset_state(self):
# Initialize state by tiling the pattern seed
self.state = self.pattern_seed.repeat(1,
self.grid_size // 4,
self.grid_size // 4)
self.prev_state = self.state.clone()
self.phase = torch.zeros(self.channels, device=self.pattern_seed.device)
def spatial_filter(self, x):
# Create simple spatial filtering with minimal parameters
# This is much more parameter-efficient than a full Conv2d
padded = torch.nn.functional.pad(x, (1, 1, 1, 1), mode='circular')
# Apply blurring/diffusion using a 3x3 kernel
neighbors = 0
neighbors += padded[:, :-2, 1:-1] # left
neighbors += padded[:, 2:, 1:-1] # right
neighbors += padded[:, 1:-1, :-2] # top
neighbors += padded[:, 1:-1, 2:] # bottom
return 0.1 * neighbors + 0.6 * x
def step(self):
# Update phase for wave effects
self.phase = (self.phase + 0.1) % (2 * torch.pi)
# Store previous state with memory decay
self.prev_state = self.memory * self.prev_state + (1 - self.memory) * self.state
# Generate wave influence at each point in the grid
y_grid, x_grid = torch.meshgrid(
torch.linspace(0, 1, self.grid_size, device=self.phase.device),
torch.linspace(0, 1, self.grid_size, device=self.phase.device),
indexing='ij'
)
wave_effect = torch.zeros_like(self.state)
for c in range(self.channels):
# Different wave patterns for each channel
wx, wy, freq, phase_offset = self.wave_params[c]
wave = torch.sin(
2 * torch.pi * freq * (wx * x_grid + wy * y_grid) +
self.phase[c] + phase_offset
)
wave_effect[c] = 0.1 * wave
# Calculate interactions between channels
channel_interaction = torch.einsum('ij,jkl->ikl', self.interaction, self.state)
# Add feedback from previous state
feedback_term = self.feedback.view(-1, 1, 1) * self.prev_state
# Update state with diffusion, interactions and waves
diffused = self.spatial_filter(self.state)
state_update = (
diffused +
channel_interaction +
wave_effect +
feedback_term +
self.bias.view(-1, 1, 1)
)
# Apply non-linear activation with learnable steepness
self.state = torch.tanh(self.activation * state_update)
def get_image(self, width, height):
# Ensure values are in [0,1] range
img = (self.state + 1) * 0.5
img = torch.clamp(img, 0, 1)
# Resize to requested dimensions
if self.grid_size != height or self.grid_size != width:
img = torch.nn.functional.interpolate(img.unsqueeze(0), size=(height, width),
mode='bilinear', align_corners=False).squeeze(0)
# Return in [H, W, C] format
return img.permute(1, 2, 0)
critique
NO, this dynamic system needs significant improvement. Let me provide a detailed critique:
1. Regarding vividness and color:
- The generated images do have colorful elements, but they lack crispness and definition
- The colors appear blurred and smeared together without clear boundaries
- The vibrance is there, but without structure to make it visually meaningful
2. Regarding similarity to targets:
- Target 1 shows a unicorn with rainbow elements and distinct features, but the odd cycles (1, 3, 5) show only vague pink/purple/blue color blobs without any recognizable unicorn shape
- Target 2 shows a rainbow/prismatic landscape with green base, but even cycles (2, 4, 6) only capture the general blue/green/yellow color palette without the structural elements
- The "Distance to Targets Over Steps" graph confirms this - there's cyclical behavior but the loss never gets particularly low for either target
3. Overall assessment:
- The system does successfully oscillate between two different states
- The loss graph shows the training converged, which is positive
- However, the quality of reproduction is extremely poor - these are at best abstract color fields loosely inspired by the targets
- The system captures only the broadest color distributions but none of the structural elements or details
This appears to be a very simple dynamic system that can capture basic color patterns but lacks the complexity needed to generate the detailed features in the target images. You would need a more sophisticated model with higher dimensional state representation to capture the detailed structures in the targets.
animation
model_state
tmp14uty2nk.ptcycle_images
visualization
optimizer_state
tmpgkm07hl4.pthistorical_losses
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cycle_losses_target1
[
0.1358814537525177,
0.23682372272014618,
0.11118382215499878,
0.23416918516159058,
0.22229433059692383,
0.1737680286169052
]
cycle_losses_target2
[
0.3061038851737976,
0.11514925956726074,
0.3049167990684509,
0.08771948516368866,
0.2762921452522278,
0.20045599341392517
]
Generated in
Steps
| ID | Model | Status |
|---|---|---|
| z0qawvz6chrm80cp1zmvqk0pz4 | anthropic/claude-3.7-sonnet | Succeeded |
| ae8aneq2xnrma0cp1zktp3phvg | andreasjansson/train-dynamic-system | Succeeded |
| azhs6tjps9rmc0cp1zkrpdj98m | anthropic/claude-3.7-sonnet | Succeeded |
| 3y5xm67x99rme0cp1zka6x4xk0 | andreasjansson/train-dynamic-system | Failed |
| 8f6aqj2hbsrme0cp1zk9hrdhc4 | anthropic/claude-3.7-sonnet | Succeeded |
Examples
Run time and cost
This model runs on CPU hardware. We don't yet have enough runs of this model to provide performance information.