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calesthioandHanzo Dev
7ffb943132
11 production pipelines, 47 tools, 124 agent skills. Supports cloud APIs (fal.ai, OpenAI, ElevenLabs, Suno, HeyGen, Runway) and free local providers (diffusers, Piper TTS, WAN 2.1, Hunyuan, CogVideo). Co-authored-by: Hanzo Dev <[email protected]>
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
"""
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MLP Network Icon - A simple visualization of a multilayer perceptron structure
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Shows dots arranged in layers with connecting lines between neurons.
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"""
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from manimlib import *
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import random
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class MLPNetworkIcon(InteractiveScene):
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"""
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Creates a classic MLP icon with three layers:
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- Input layer
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- Hidden layer (wider)
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- Output layer
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Demonstrates: VGroup organization, Line connections, random styling
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"""
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def construct(self):
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# Create the MLP icon
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network = self.get_mlp_icon(layer_buff=2.5, layer0_size=5)
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# Animate the network appearing
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self.play(Write(network, stroke_width=0.5, lag_ratio=1e-2, run_time=3))
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self.wait()
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# Show data propagating through the network
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lines = VGroup(network[1].family_members_with_points()).copy()
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for line in lines:
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line.set_stroke(width=2 * line.get_width())
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line.insert_n_curves(20)
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self.play(
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LaggedStartMap(
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VShowPassingFlash,
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lines,
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time_width=1.5,
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lag_ratio=5e-3,
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run_time=3
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)
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)
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self.wait()
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def get_mlp_icon(self, dot_buff=0.15, layer_buff=1.5, layer0_size=5):
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"""
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Creates an MLP icon with three layers.
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Args:
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dot_buff: Spacing between neurons in a layer
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layer_buff: Spacing between layers
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layer0_size: Number of neurons in input/output layers
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"""
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# Create three layers of dots
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layers = VGroup(
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Dot().get_grid(layer0_size, 1, buff=dot_buff),
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Dot().get_grid(2 * layer0_size, 1, buff=dot_buff), # Hidden layer is wider
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Dot().get_grid(layer0_size, 1, buff=dot_buff),
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)
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layers.set_height(4)
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layers.arrange(RIGHT, buff=layer_buff)
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# Set random opacities for visual interest
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for layer in layers:
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for dot in layer:
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dot.set_fill(opacity=random.random())
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layers.set_stroke(WHITE, 0.5)
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# Create connection lines between layers
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lines = VGroup(
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Line(
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dot1.get_center(),
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dot2.get_center(),
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buff=dot1.get_width() / 2
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)
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for l1, l2 in zip(layers, layers[1:])
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for dot1 in l1
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for dot2 in l2
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)
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# Color and style the lines randomly
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for line in lines:
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line.set_stroke(
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color=self.value_to_color(random.uniform(-10, 10)),
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width=3 * random.random()**3
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)
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return VGroup(layers, lines)
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def value_to_color(
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self,
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value,
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low_positive_color=BLUE_E,
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high_positive_color=BLUE_B,
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low_negative_color=RED_E,
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high_negative_color=RED_B,
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min_value=0.0,
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max_value=10.0
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):
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"""Maps a numeric value to a color based on its sign and magnitude."""
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alpha = clip(float(inverse_interpolate(min_value, max_value, abs(value))), 0, 1)
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if value >= 0:
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colors = (low_positive_color, high_positive_color)
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else:
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colors = (low_negative_color, high_negative_color)
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return interpolate_color_by_hsl(*colors, alpha)
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