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65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
"""Variational Autoencoder Manim Visualizations
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In this module I define Manim visualizations for Variational Autoencoders
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and Traditional Autoencoders.
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"""
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from manim import *
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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 import FeedForwardLayer, EmbeddingLayer, ImageLayer
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from manim_ml.neural_network.neural_network import NeuralNetwork
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class VariationalAutoencoder(VGroup):
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"""Variational Autoencoder Manim Visualization"""
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def __init__(self, encoder_nodes_per_layer=[5, 3], decoder_nodes_per_layer=[3, 5],
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point_color=BLUE, dot_radius=0.05, ellipse_stroke_width=1.0,
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layer_spacing=0.5):
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super(VGroup, self).__init__()
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self.encoder_nodes_per_layer = encoder_nodes_per_layer
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self.decoder_nodes_per_layer = decoder_nodes_per_layer
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self.point_color = point_color
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self.dot_radius = dot_radius
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self.layer_spacing = layer_spacing
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self.ellipse_stroke_width = ellipse_stroke_width
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# Make the VMobjects
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self.neural_network, self.embedding_layer = self._construct_neural_network()
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def _construct_neural_network(self):
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"""Makes the VAE encoder, embedding layer, and decoder"""
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embedding_layer = EmbeddingLayer()
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neural_network = NeuralNetwork([
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FeedForwardLayer(5),
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FeedForwardLayer(3),
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embedding_layer,
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FeedForwardLayer(3),
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FeedForwardLayer(5)
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])
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return neural_network, embedding_layer
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@override_animation(Create)
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def _create_vae(self):
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return Create(self.neural_network)
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def make_triplet_forward_pass(self, triplet):
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pass
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def make_image_forward_pass(self, input_image, output_image, run_time=1.5):
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"""Override forward pass animation specific to a VAE"""
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# Make a wrapper NN with images
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wrapper_neural_network = NeuralNetwork([
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ImageLayer(input_image),
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self.neural_network,
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ImageLayer(output_image)
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])
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# Make animation
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animation_group = AnimationGroup(
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Create(wrapper_neural_network),
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wrapper_neural_network.make_forward_pass_animation(),
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lag_ratio=1.0
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)
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return animation_group |