mirror of
https://github.com/helblazer811/ManimML.git
synced 2025-08-06 17:29:45 +08:00
66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
from manim import *
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from PIL import Image
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import numpy as np
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from manim_ml.neural_network import Convolutional2DLayer, NeuralNetwork
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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 the neural network
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nn = NeuralNetwork({
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"layer1": Convolutional2DLayer(1, 5, padding=1),
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"layer2": Convolutional2DLayer(1, 5, 3, padding=1),
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"layer3": Convolutional2DLayer(1, 5, 3, padding=1)
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})
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# Add the residual connection
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nn.add_connection("layer1", "layer3")
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# Make the 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 ConvScene(ThreeDScene):
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def construct(self):
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image = Image.open("../../assets/mnist/digit.jpeg")
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numpy_image = np.asarray(image)
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nn = NeuralNetwork(
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{
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"layer1": Convolutional2DLayer(1, 5, padding=1),
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"layer2": Convolutional2DLayer(1, 5, 3, padding=1),
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"layer3": Convolutional2DLayer(1, 5, 3, padding=1),
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},
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layer_spacing=0.25,
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)
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nn.add_connection("layer1", "layer3")
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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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self.play(nn.make_forward_pass_animation(), run_time=8)
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