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Finished oracle guidance video. Integrated various changes necessary to complete this.

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This commit is contained in:
Alec Helbling
2022-04-28 01:37:52 -04:00
parent 95a36eb234
commit 9310b48c56
38 changed files with 1039 additions and 157 deletions
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143
manim_ml/neural_network/layers/embedding.py
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@ -9,6 +9,8 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
covariance=np.array([[1.0, 0], [0, 1.0]]), dist_theme="gaussian",
paired_query_mode=False, **kwargs):
super(VGroupNeuralNetworkLayer, self).__init__(**kwargs)
self.gaussian_distributions = VGroup()
self.add(self.gaussian_distributions)
self.point_radius = point_radius
self.dist_theme = dist_theme
self.paired_query_mode = paired_query_mode
@ -16,8 +18,8 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
tips=False,
x_length=0.8,
y_length=0.8,
x_range=(-2.0, 2.0),
y_range=(-2.0, 2.0),
x_range=(-1.4, 1.4),
y_range=(-1.8, 1.8),
x_axis_config={
"include_ticks": False,
"stroke_width": 0.0
@ -33,8 +35,20 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
self.point_cloud = self.construct_gaussian_point_cloud(mean, covariance)
self.add(self.point_cloud)
# Make latent distribution
self.latent_distribution = GaussianDistribution(self.axes, mean=mean, cov=covariance,
dist_theme=self.dist_theme) # Use defaults
self.latent_distribution = GaussianDistribution(self.axes, mean=mean, cov=covariance) # Use defaults
def add_gaussian_distribution(self, gaussian_distribution):
"""Adds given GaussianDistribution to the list"""
self.gaussian_distributions.add(gaussian_distribution)
return Create(gaussian_distribution)
def remove_gaussian_distribution(self, gaussian_distribution):
"""Removes the given gaussian distribution from the embedding"""
for gaussian in self.gaussian_distributions:
if gaussian == gaussian_distribution:
self.gaussian_distributions.remove(gaussian_distribution)
return FadeOut(gaussian)
def sample_point_location_from_distribution(self):
"""Samples from the current latent distribution"""
@ -50,57 +64,112 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
"""Returns mean of latent distribution in axes frame"""
return self.axes.coords_to_point(self.latent_distribution.mean)
def construct_gaussian_point_cloud(self, mean, covariance, point_color=BLUE,
num_points=200):
def construct_gaussian_point_cloud(self, mean, covariance, point_color=WHITE,
num_points=400):
"""Plots points sampled from a Gaussian with the given mean and covariance"""
# Sample points from a Gaussian
np.random.seed(5)
points = np.random.multivariate_normal(mean, covariance, num_points)
# Add each point to the axes
point_dots = VGroup()
for point in points:
point_location = self.axes.coords_to_point(*point)
dot = Dot(point_location, color=point_color, radius=self.point_radius/2)
dot.set_z_index(-1)
point_dots.add(dot)
return point_dots
def make_paired_query_embedding_animation(self):
"""Embed paired query"""
animations = []
# Make the animation
# Animation group
animation_group = AnimationGroup(
*animations,
lag_ratio=1.0
)
return animation_group
def make_forward_pass_animation(self, layer_args={}, **kwargs):
"""Forward pass animation"""
animations = []
if not self.paired_query_mode:
# Normal embedding mode
# Make ellipse object corresponding to the latent distribution
self.latent_distribution = GaussianDistribution(
self.axes,
dist_theme=self.dist_theme,
cov=np.array([[0.8, 0], [0.0, 0.8]])
) # Use defaults
# Create animation
#create_distribution = Create(self.latent_distribution.construct_gaussian_distribution(self.latent_distribution.mean, self.latent_distribution.cov)) #Create(self.latent_distribution)
create_distribution = Create(self.latent_distribution.ellipses)
animations.append(create_distribution)
animation_group = AnimationGroup(*animations)
animations = []
if "triplet_args" in layer_args:
triplet_args = layer_args["triplet_args"]
positive_dist_args = triplet_args["positive_dist"]
negative_dist_args = triplet_args["negative_dist"]
anchor_dist_args = triplet_args["anchor_dist"]
# Create each dist
anchor_dist = GaussianDistribution(
self.axes,
**anchor_dist_args
)
animations.append(Create(anchor_dist))
return animation_group
positive_dist = GaussianDistribution(
self.axes,
**positive_dist_args
)
animations.append(Create(positive_dist))
negative_dist = GaussianDistribution(
self.axes,
**negative_dist_args
)
animations.append(Create(negative_dist))
# Draw edges in between anchor and positive, anchor and negative
anchor_positive = Line(
anchor_dist.get_center(),
positive_dist.get_center(),
color=GOLD,
stroke_width=DEFAULT_STROKE_WIDTH/2
)
anchor_positive.set_z_index(3)
animations.append(Create(anchor_positive))
anchor_negative = Line(
anchor_dist.get_center(),
negative_dist.get_center(),
color=GOLD,
stroke_width=DEFAULT_STROKE_WIDTH/2
)
anchor_negative.set_z_index(3)
animations.append(Create(anchor_negative))
elif not self.paired_query_mode:
# Normal embedding mode
if "dist_args" in layer_args:
scale_factor = 1.0
if "scale_factor" in layer_args:
scale_factor = layer_args["scale_factor"]
self.latent_distribution = GaussianDistribution(
self.axes,
**layer_args["dist_args"]
).scale(scale_factor)
else:
# Make ellipse object corresponding to the latent distribution
# self.latent_distribution = GaussianDistribution(
# self.axes,
# dist_theme=self.dist_theme,
# cov=np.array([[0.8, 0], [0.0, 0.8]])
# )
pass
# Create animation
create_distribution = Create(self.latent_distribution)
animations.append(create_distribution)
else:
# Paired Query Mode
assert "positive_dist_args" in layer_args
assert "negative_dist_args" in layer_args
positive_dist_args = layer_args["positive_dist_args"]
negative_dist_args = layer_args["negative_dist_args"]
# Handle logic for embedding a paired query into the embedding layer
paired_query_embedding_animation = self.make_paired_query_embedding_animation()
return paired_query_embedding_animation
positive_dist = GaussianDistribution(
self.axes,
**positive_dist_args
)
self.gaussian_distributions.add(positive_dist)
negative_dist = GaussianDistribution(
self.axes,
**negative_dist_args
)
self.gaussian_distributions.add(negative_dist)
animations.append(Create(positive_dist))
animations.append(Create(negative_dist))
animation_group = AnimationGroup(*animations)
return animation_group
@override_animation(Create)
def _create_override(self, **kwargs):