generate animate video for below # Save this as autoencoder_animation.py # Run with: manim -pql autoencoder_animation.py AutoencoderAnimation from manim import * class AutoencoderAnimation(Scene): def construct(self): # --- Title Scene --- title = Text("Autoencoder for Recommendations", font_size=48) self.play(Write(title)) self.wait(2) self.play(FadeOut(title)) self.wait(0.5) # --- 1. Input: User Ratings Vector --- input_label = Text("Input: User's Rating History", font_size=32).to_edge(UP) ratings = ["5", "0", "1", "0", "0", "4", "0", "0"] # 0 means unrated input_vector = self.create_vector_vis(ratings, "Input Vector", color=WHITE) input_vector.to_edge(LEFT) self.play(Write(input_label)) self.play(FadeIn(input_vector, shift=RIGHT)) self.wait(2) # --- 2. Encoder Architecture --- encoder_label = Text("Encoder", font_size=32).move_to(UP * 2) encoder_layers = self.create_mlp_tower([8, 5, 3], colors=[WHITE, BLUE, BLUE]) encoder_layers.move_to(ORIGIN) self.play(input_vector.animate.scale(0.8).next_to(encoder_layers, LEFT, buff=0.5)) self.play(Write(encoder_label), FadeIn(encoder_layers)) self.wait(1) # Animate compression self.play_network_flow(input_vector.squares, encoder_layers[0]) self.play_network_flow(encoder_layers[0], encoder_layers[1]) self.play_network_flow(encoder_layers[1], encoder_layers[2]) self.wait(1) # --- 3. Latent Vector (User DNA) --- latent_vector_vis = encoder_layers[2].copy() latent_label = Text("Latent Vector ('User DNA')", font_size=28).next_to(latent_vector_vis, DOWN, buff=0.5) self.play(latent_vector_vis.animate.move_to(ORIGIN).scale(1.2), FadeIn(latent_label)) self.play(FadeOut(input_vector, encoder_layers, encoder_label)) self.wait(2) # --- 4. Decoder Architecture --- decoder_label = Text("Decoder", font_size=32).move_to(UP * 2) decoder_layers = self.create_mlp_tower([3, 5, 8], colors=[BLUE, GREEN, GREEN]) decoder_layers.move_to(ORIGIN) self.play(latent_vector_vis.animate.scale(0.8).next_to(decoder_layers, LEFT, buff=0.5)) self.play(Write(decoder_label), FadeIn(decoder_layers)) self.wait(1) # Animate decompression self.play_network_flow(latent_vector_vis, decoder_layers[0]) self.play_network_flow(decoder_layers[0], decoder_layers[1]) self.play_network_flow(decoder_layers[1], decoder_layers[2]) self.wait(1) # --- 5. Output: Reconstructed Vector --- output_ratings = ["5.1", "2.3", "0.9", "3.5", "1.1", "4.2", "2.8", "3.9"] output_vector = self.create_vector_vis(output_ratings, "Reconstructed Vector", color=WHITE) output_vector.to_edge(RIGHT) self.play(ReplacementTransform(decoder_layers[2].copy(), output_vector)) self.play(FadeOut(decoder_label, decoder_layers, latent_vector_vis)) self.wait(2) # --- 6. Highlight Recommendations --- original_input_small = self.create_vector_vis(ratings, "Original", color=GRAY).scale(0.7).to_edge(DOWN + LEFT) reconstructed_small = self.create_vector_vis(output_ratings, "Reconstructed", color=WHITE).scale(0.7).to_edge(DOWN + RIGHT) self.play(FadeOut(input_vector), FadeIn(original_input_small, reconstructed_small)) self.wait(1) recommendation_label = Text("Recommendations are the filled-in blanks!", color=YELLOW).next_to(input_label, DOWN) self.play(Write(recommendation_label)) highlights = VGroup() for i in range(len(ratings)): if ratings[i] == "0": rect = SurroundingRectangle(reconstructed_small.squares[i], color=YELLOW) highlights.add(rect) self.play(Create(highlights)) self.wait(3) def create_vector_vis(self, values, label_text, color): squares = VGroup() for val in values: square = Square(side_length=0.8, color=color, stroke_width=2) text = Text(str(val), font_size=24).move_to(square.get_center()) squares.add(VGroup(square, text)) squares.arrange(RIGHT, buff=0.1) label = Text(label_text, font_size=24).next_to(squares, DOWN, buff=0.3) return VGroup(squares, label) def create_mlp_tower(self, layer_sizes, colors): layers = VGroup() for i, size in enumerate(layer_sizes): layer = VGroup([Circle(radius=0.2, color=colors[i], stroke_width=2, fill_opacity=0.5) for _ in range(size)]) layer.arrange(DOWN, buff=0.3) layers.add(layer) layers.arrange(RIGHT, buff=1.5) return layers def play_network_flow(self, layer1, layer2): edges = VGroup([Line(n1.get_center(), n2.get_center(), stroke_width=1.5, color=YELLOW) for n1 in layer1 for n2 in layer2]) self.play(LaggedStart( *[ShowPassingFlash(edge.copy().set_color(ORANGE), time_width=0.2) for edge in edges], lag_ratio=0.05 ), run_time=1)

视频信息

视频地址

封面地址

Provider

视频字幕

Autoencoders are a special type of neural network that learn to compress data into a smaller representation and then reconstruct it back to its original form. Think of them as learning to create a compressed summary of information that captures the most important features. In recommendation systems, autoencoders can learn to encode user preferences and decode them to predict missing ratings. The autoencoder architecture consists of three main components. The encoder progressively compresses the input data through layers of decreasing size, learning to capture the most important features. The latent space is the bottleneck layer that contains the compressed representation. Finally, the decoder expands this compressed information back to the original dimensions, learning to reconstruct the input data from the latent representation. In recommendation systems, user rating data is structured as vectors where each position represents a different item like movies or products. Users typically rate only a small fraction of available items, leaving many positions unrated or represented as zeros. This creates sparse data that poses a challenge for traditional methods. The autoencoder takes this incomplete rating vector as input and learns to understand user preferences from the limited available information. The encoding process transforms the input rating vector through multiple layers of decreasing size. Starting with 8 dimensions representing different items, the first hidden layer compresses this to 5 dimensions, then to 3, and finally to just 2 dimensions in the latent space. Each layer learns to identify and preserve the most important patterns while discarding noise. The final latent vector acts like the user's DNA, capturing their core preferences in a highly compressed form that can be used to understand their taste profile. The decoding process reverses the compression by expanding the latent representation through layers of increasing size. Starting from the 2-dimensional user DNA, the decoder progressively reconstructs the information through 3 dimensions, then 5, and finally back to the original 8 dimensions. This process learns to fill in the missing ratings based on the compressed user preferences. The decoder essentially asks: given what we know about this user's core preferences, what would they likely rate the items they haven't seen yet?

视频信息

答案文本 复制

视频字幕 复制

答案文本

视频字幕