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https://github.com/helblazer811/ManimML.git
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63 lines
2.0 KiB
Python
63 lines
2.0 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.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.layers.image import ImageLayer
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from manim_ml.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
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from manim_ml.neural_network.neural_network import 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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class CombinedScene(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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# 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(1, 8, filter_spacing=0.32),
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Convolutional2DLayer(3, 6, 3, filter_spacing=0.32),
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MaxPooling2DLayer(kernel_size=2),
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Convolutional2DLayer(5, 2, 2, filter_spacing=0.32),
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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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nn.move_to(ORIGIN)
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self.add(nn)
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self.wait(5)
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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)
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class SmallNetwork(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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# 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(1, 8, filter_spacing=0.32),
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MaxPooling2DLayer(kernel_size=2),
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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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nn.move_to(ORIGIN)
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self.add(nn)
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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)
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