mirror of
https://github.com/helblazer811/ManimML.git
synced 2025-05-17 10:45:54 +08:00
85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
from manim import *
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from manim_ml.neural_network.layers.image import ImageLayer
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import numpy as np
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from PIL import Image
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from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
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from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
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from manim_ml.neural_network.neural_network import NeuralNetwork
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ROOT_DIR = Path(__file__).parents[2]
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# Make the specific scene
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config.pixel_height = 1200
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config.pixel_width = 1900
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config.frame_height = 6.0
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config.frame_width = 6.0
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def make_code_snippet():
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code_str = """
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# Make nn
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nn = NeuralNetwork([
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ImageLayer(numpy_image),
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Convolutional2DLayer(1, 6, 1, padding=1),
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Convolutional2DLayer(3, 6, 3),
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FeedForwardLayer(3),
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FeedForwardLayer(1),
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])
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# Play animation
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self.play(nn.make_forward_pass_animation())
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"""
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code = Code(
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code=code_str,
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tab_width=4,
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background_stroke_width=1,
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background_stroke_color=WHITE,
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insert_line_no=False,
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style="monokai",
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# background="window",
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language="py",
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)
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code.scale(0.38)
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return code
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class CombinedScene(ThreeDScene):
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def construct(self):
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# Make nn
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image = Image.open(ROOT_DIR / "assets/mnist/digit.jpeg")
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numpy_image = np.asarray(image)
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# Make nn
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nn = NeuralNetwork(
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[
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ImageLayer(numpy_image, height=1.5),
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Convolutional2DLayer(
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num_feature_maps=1,
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feature_map_size=6,
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padding=1,
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padding_dashed=True,
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),
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Convolutional2DLayer(
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num_feature_maps=3,
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feature_map_size=6,
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filter_size=3,
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padding=0,
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padding_dashed=False,
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),
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FeedForwardLayer(3),
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FeedForwardLayer(1),
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],
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layer_spacing=0.25,
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)
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# Center the nn
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self.add(nn)
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code = make_code_snippet()
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code.next_to(nn, DOWN)
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self.add(code)
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Group(code, nn).move_to(ORIGIN)
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# Play animation
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forward_pass = nn.make_forward_pass_animation()
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self.wait(1)
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self.play(forward_pass, run_time=20)
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