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https://github.com/helblazer811/ManimML.git
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80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
from manim import *
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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.layers.image import ImageLayer
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from manim_ml.neural_network.neural_network import NeuralNetwork
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class SingleConvolutionalLayerScene(ThreeDScene):
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def construct(self):
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# Make nn
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layers = [Convolutional2DLayer(3, 4)]
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nn = NeuralNetwork(layers)
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nn.scale(1.3)
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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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self.set_camera_orientation(
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phi=280 * DEGREES, theta=-10 * DEGREES, gamma=90 * DEGREES
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)
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# self.play(nn.make_forward_pass_animation(run_time=5))
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class Simple3DConvScene(ThreeDScene):
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def construct(self):
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"""
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TODO
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- [X] Make grid lines for the CNN filters
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- [ ] Make Scanning filter effect
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- [ ] Have filter box go accross each input feature map
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- [ ] Make filter lines effect
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- [ ] Make flowing animation down filter lines
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"""
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# Make nn
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layers = [
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Convolutional2DLayer(num_feature_maps=1, feature_map_size=3, filter_size=3),
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Convolutional2DLayer(num_feature_maps=1, feature_map_size=3, filter_size=3),
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]
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nn = NeuralNetwork(layers)
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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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# self.set_camera_orientation(phi=280*DEGREES, theta=-10*DEGREES, gamma=90*DEGREES)
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self.play(nn.make_forward_pass_animation(run_time=30))
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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, 7, filter_spacing=0.32),
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Convolutional2DLayer(3, 5, 3, filter_spacing=0.32),
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Convolutional2DLayer(5, 3, 3, filter_spacing=0.18),
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FeedForwardLayer(3),
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FeedForwardLayer(3),
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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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