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Updated examples

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This commit is contained in:
Alec Helbling
2022-12-30 22:32:44 -05:00
parent b1a85ea782
commit f8701c8a91
2 changed files with 40 additions and 35 deletions
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73
examples/cnn/cnn.py
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@ -1,26 +1,30 @@
from manim import * from manim import *
from PIL import Image from PIL import Image
from manim_ml.neural_network.layers.convolutional import ConvolutionalLayer from manim_ml.neural_network.layers.convolutional3d import Convolutional3DLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.image import ImageLayer from manim_ml.neural_network.layers.image import ImageLayer
from manim_ml.neural_network.neural_network import NeuralNetwork from manim_ml.neural_network.neural_network import NeuralNetwork
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0
def make_code_snippet(): def make_code_snippet():
code_str = """ code_str = """
# Make nn # Make nn
nn = NeuralNetwork([ nn = NeuralNetwork([
ImageLayer(numpy_image), ImageLayer(numpy_image, height=1.5),
ConvolutionalLayer(3, 3, 3), Convolutional3DLayer(1, 7, 7, 3, 3),
ConvolutionalLayer(5, 2, 2), Convolutional3DLayer(3, 5, 5, 3, 3),
ConvolutionalLayer(10, 2, 1), Convolutional3DLayer(5, 3, 3, 1, 1),
FeedForwardLayer(3), FeedForwardLayer(3),
FeedForwardLayer(1) FeedForwardLayer(3),
], layer_spacing=0.2) ])
# Center the nn
self.play(Create(nn))
# Play animation # Play animation
self.play(nn.make_forward_pass_animation(run_time=5)) self.play(nn.make_forward_pass_animation())
""" """
code = Code( code = Code(
@ -33,40 +37,41 @@ def make_code_snippet():
#background="window", #background="window",
language="py", language="py",
) )
code.scale(0.6) code.scale(0.50)
return code return code
class CombinedScene(ThreeDScene):
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 12.0
config.frame_width = 12.0
class CombinedScene(ThreeDScene, Scene):
def construct(self): def construct(self):
image = Image.open('../../assets/mnist/digit.jpeg') image = Image.open('../../assets/mnist/digit.jpeg')
numpy_image = np.asarray(image) numpy_image = np.asarray(image)
# Make nn # Make nn
nn = NeuralNetwork([ nn = NeuralNetwork([
ImageLayer(numpy_image, height=3.5), ImageLayer(numpy_image, height=1.5),
ConvolutionalLayer(3, 3, 3, filter_spacing=0.2), Convolutional3DLayer(1, 7, 7, 3, 3, filter_spacing=0.32),
ConvolutionalLayer(5, 2, 2, filter_spacing=0.2), Convolutional3DLayer(3, 5, 5, 3, 3, filter_spacing=0.32),
ConvolutionalLayer(10, 2, 1, filter_spacing=0.2), Convolutional3DLayer(5, 3, 3, 1, 1, filter_spacing=0.18),
FeedForwardLayer(3, rectangle_stroke_width=4, node_stroke_width=4).scale(2), FeedForwardLayer(3),
FeedForwardLayer(1, rectangle_stroke_width=4, node_stroke_width=4).scale(2) FeedForwardLayer(3),
], layer_spacing=0.2) ],
nn.scale(0.9) layer_spacing=0.25,
)
# Center the nn
nn.move_to(ORIGIN) nn.move_to(ORIGIN)
nn.shift(UP*1.8) self.add(nn)
# Make code snippet # Make code snippet
code = make_code_snippet() code = make_code_snippet()
code.shift(DOWN*1.8) code.next_to(nn, DOWN)
# Center the nn
self.play(Create(nn))
self.add(code) self.add(code)
# Group it all
group = Group(nn, code)
group.move_to(ORIGIN)
# Play animation # Play animation
# self.set_camera_orientation(phi=280* DEGREES, theta=-20*DEGREES, gamma=90 * DEGREES) forward_pass = nn.make_forward_pass_animation(
# self.begin_ambient_camera_rotation() corner_pulses=False,
self.play(nn.make_forward_pass_animation(run_time=5)) all_filters_at_once=False
)
self.wait(1)
self.play(
forward_pass
)

2
manim_ml/neural_network/layers/parent_layers.py
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@ -45,7 +45,7 @@ class ThreeDLayer(ABC):
three_d_x_rotation = 90 * DEGREES #-90 * DEGREES three_d_x_rotation = 90 * DEGREES #-90 * DEGREES
three_d_y_rotation = 0 * DEGREES # -10 * DEGREES three_d_y_rotation = 0 * DEGREES # -10 * DEGREES
rotation_angle = 60 * DEGREES rotation_angle = 60 * DEGREES
rotation_axis = [0.1, 0.9, 0] rotation_axis = [0.0, 0.9, 0.0]
class ConnectiveLayer(VGroupNeuralNetworkLayer): class ConnectiveLayer(VGroupNeuralNetworkLayer):
"""Forward pass animation for a given pair of layers""" """Forward pass animation for a given pair of layers"""