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ManimML_helblazer811/LICENSE.md | MIT License
Copyright (c) 2022 Alec Helbling
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, dis... |
ManimML_helblazer811/setup.py | from setuptools import setup, find_packages
setup(
name="manim_ml",
version="0.0.17",
description=("Machine Learning Animations in python using Manim."),
packages=find_packages(),
)
|
ManimML_helblazer811/Readme.md | # ManimML
<a href="https://github.com/helblazer811/ManimMachineLearning">
<img src="assets/readme/ManimMLLogo.gif">
</a>
[](https://github.com/helblazer811/ManimMachineLearning/blob/main/LICENSE.md)
[:
self._color_scheme = default_color_scheme
self.three_d_config = Namespace... |
ManimML_helblazer811/manim_ml/scene.py | from manim import *
class ManimML3DScene(ThreeDScene):
"""
This is a wrapper class for the Manim ThreeDScene
Note: the primary purpose of this is to make it so
that everything inside of a layer
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def play(s... |
ManimML_helblazer811/manim_ml/diffusion/mcmc.py | """
Tool for animating Markov Chain Monte Carlo simulations in 2D.
"""
from manim import *
import matplotlib
import matplotlib.pyplot as plt
from manim_ml.utils.mobjects.plotting import convert_matplotlib_figure_to_image_mobject
import numpy as np
import scipy
import scipy.stats
from tqdm import tqdm
import seabor... |
ManimML_helblazer811/manim_ml/utils/__init__.py | |
ManimML_helblazer811/manim_ml/utils/colorschemes/__init__.py | from manim_ml.utils.colorschemes.colorschemes import light_mode, dark_mode |
ManimML_helblazer811/manim_ml/utils/colorschemes/colorschemes.py | from manim import *
from dataclasses import dataclass
@dataclass
class ColorScheme:
primary_color: str
secondary_color: str
active_color: str
text_color: str
background_color: str
dark_mode = ColorScheme(
primary_color=BLUE,
secondary_color=WHITE,
active_color=ORANGE,
text_color=WH... |
ManimML_helblazer811/manim_ml/utils/mobjects/__init__.py | |
ManimML_helblazer811/manim_ml/utils/mobjects/connections.py | import numpy as np
from manim import *
class NetworkConnection(VGroup):
"""
This class allows for creating connections
between locations in a network
"""
direction_vector_map = {"up": UP, "down": DOWN, "left": LEFT, "right": RIGHT}
def __init__(
self,
start_mobject,
e... |
ManimML_helblazer811/manim_ml/utils/mobjects/image.py | from manim import *
import numpy as np
from PIL import Image
class GrayscaleImageMobject(Group):
"""Mobject for creating images in Manim from numpy arrays"""
def __init__(self, numpy_image, height=2.3):
super().__init__()
self.numpy_image = numpy_image
assert len(np.shape(self.numpy_im... |
ManimML_helblazer811/manim_ml/utils/mobjects/list_group.py | from manim import *
class ListGroup(Mobject):
"""Indexable Group with traditional list operations"""
def __init__(self, *layers):
super().__init__()
self.items = [*layers]
def __getitem__(self, indices):
"""Traditional list indexing"""
return self.items[indices]
def ... |
ManimML_helblazer811/manim_ml/utils/mobjects/probability.py | from manim import *
import numpy as np
import math
class GaussianDistribution(VGroup):
"""Object for drawing a Gaussian distribution"""
def __init__(
self, axes, mean=None, cov=None, dist_theme="gaussian", color=ORANGE, **kwargs
):
super(VGroup, self).__init__(**kwargs)
self.axes ... |
ManimML_helblazer811/manim_ml/utils/mobjects/plotting.py | from manim import *
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from PIL import Image
import io
def convert_matplotlib_figure_to_image_mobject(fig, dpi=200):
"""Takes a matplotlib figure and makes an image mobject from it
Parameters
----------
fig : matplotlib figure
m... |
ManimML_helblazer811/manim_ml/utils/mobjects/gridded_rectangle.py | from manim import *
import numpy as np
class GriddedRectangle(VGroup):
"""Rectangle object with grid lines"""
def __init__(
self,
color=ORANGE,
height=2.0,
width=4.0,
mark_paths_closed=True,
close_new_points=True,
grid_xstep=None,
grid_ystep=Non... |
ManimML_helblazer811/manim_ml/utils/testing/frames_comparison.py | from __future__ import annotations
import functools
import inspect
from pathlib import Path
from typing import Callable
from _pytest.fixtures import FixtureRequest
from manim import Scene
from manim._config import tempconfig
from manim._config.utils import ManimConfig
from manim.camera.three_d_camera import ThreeDCa... |
ManimML_helblazer811/manim_ml/utils/testing/doc_directive.py | r"""
A directive for including Manim videos in a Sphinx document
"""
from __future__ import annotations
import csv
import itertools as it
import os
import re
import shutil
import sys
from pathlib import Path
from timeit import timeit
import jinja2
from docutils import nodes
from docutils.parsers.rst import Directive,... |
ManimML_helblazer811/manim_ml/decision_tree/decision_tree_surface.py | from manim import *
import numpy as np
from collections import deque
from sklearn.tree import _tree as ctree
class AABB:
"""Axis-aligned bounding box"""
def __init__(self, n_features):
self.limits = np.array([[-np.inf, np.inf]] * n_features)
def split(self, f, v):
left = AABB(self.limits.... |
ManimML_helblazer811/manim_ml/decision_tree/helpers.py | def compute_node_depths(tree):
"""Computes the depths of nodes for level order traversal"""
def depth(node_index, current_node_index=0):
"""Compute the height of a node"""
if current_node_index == node_index:
return 0
elif (
tree.children_left[current_node_index]... |
ManimML_helblazer811/manim_ml/decision_tree/decision_tree.py | """
Module for visualizing decision trees in Manim.
It parses a decision tree classifier from sklearn.
TODO return a map from nodes to split animation for BFS tree expansion
TODO reimplement the decision 2D decision tree surface drawing.
"""
from manim import *
from manim_ml.decision_tree.decision_t... |
ManimML_helblazer811/manim_ml/neural_network/neural_network.py | """Neural Network Manim Visualization
This module is responsible for generating a neural network visualization with
manim, specifically a fully connected neural network diagram.
Example:
# Specify how many nodes are in each node layer
layer_node_count = [5, 3, 5]
# Create the object with default style set... |
ManimML_helblazer811/manim_ml/neural_network/__init__.py | from manim_ml.neural_network.neural_network import NeuralNetwork
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.convolutional_2d_to_convolutional_2d import (
Convolutional2DToConvolutional2D,
)
from manim_ml.neural_network.layers.convolutional_2d_to_feed... |
ManimML_helblazer811/manim_ml/neural_network/layers/math_operation_layer.py | from manim import *
from manim_ml.neural_network.activation_functions import get_activation_function_by_name
from manim_ml.neural_network.activation_functions.activation_function import (
ActivationFunction,
)
from manim_ml.neural_network.layers.parent_layers import VGroupNeuralNetworkLayer
class MathOperationLay... |
ManimML_helblazer811/manim_ml/neural_network/layers/embedding.py | from manim import *
from manim_ml.utils.mobjects.probability import GaussianDistribution
from manim_ml.neural_network.layers.parent_layers import VGroupNeuralNetworkLayer
class EmbeddingLayer(VGroupNeuralNetworkLayer):
"""NeuralNetwork embedding object that can show probability distributions"""
def __init__(... |
ManimML_helblazer811/manim_ml/neural_network/layers/paired_query.py | from manim import *
from manim_ml.neural_network.layers.parent_layers import NeuralNetworkLayer
from manim_ml.utils.mobjects.image import GrayscaleImageMobject, LabeledColorImage
import numpy as np
class PairedQueryLayer(NeuralNetworkLayer):
"""Paired Query Layer"""
def __init__(
self, positive, nega... |
ManimML_helblazer811/manim_ml/neural_network/layers/feed_forward_to_math_operation.py | from manim import *
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
from manim_ml.neural_network.layers.math_operation_layer import MathOperationLayer
from manim_ml.utils.mobjects.connections import NetworkConnection
clas... |
ManimML_helblazer811/manim_ml/neural_network/layers/image_to_feed_forward.py | from manim import *
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.parent_layers import ConnectiveLayer
class ImageToFeedForward(ConnectiveLayer):
"""Image Layer to FeedForward layer"""
i... |
ManimML_helblazer811/manim_ml/neural_network/layers/vector.py | from manim import *
import random
from manim_ml.neural_network.layers.parent_layers import VGroupNeuralNetworkLayer
class VectorLayer(VGroupNeuralNetworkLayer):
"""Shows a vector"""
def __init__(self, num_values, value_func=lambda: random.uniform(0, 1), **kwargs):
super().__init__(**kwargs)
... |
ManimML_helblazer811/manim_ml/neural_network/layers/__init__.py | from manim_ml.neural_network.layers.convolutional_2d_to_feed_forward import (
Convolutional2DToFeedForward,
)
from manim_ml.neural_network.layers.convolutional_2d_to_max_pooling_2d import (
Convolutional2DToMaxPooling2D,
)
from manim_ml.neural_network.layers.image_to_convolutional_2d import (
ImageToConvolu... |
ManimML_helblazer811/manim_ml/neural_network/layers/util.py | import warnings
from manim import *
from manim_ml.neural_network.layers.parent_layers import BlankConnective, ThreeDLayer
from manim_ml.neural_network.layers import connective_layers_list
def get_connective_layer(input_layer, output_layer):
"""
Deduces the relevant connective layer
"""
connective_lay... |
ManimML_helblazer811/manim_ml/neural_network/layers/max_pooling_2d_to_feed_forward.py | from manim import *
from manim_ml.neural_network.layers.convolutional_2d_to_feed_forward import (
Convolutional2DToFeedForward,
)
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
class MaxPooling2DToFeedForward(Con... |
ManimML_helblazer811/manim_ml/neural_network/layers/feed_forward_to_image.py | from manim import *
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.parent_layers import ConnectiveLayer
class FeedForwardToImage(ConnectiveLayer):
"""Image Layer to FeedForward layer"""
i... |
ManimML_helblazer811/manim_ml/neural_network/layers/feed_forward.py | from manim import *
from manim_ml.neural_network.activation_functions import get_activation_function_by_name
from manim_ml.neural_network.activation_functions.activation_function import (
ActivationFunction,
)
from manim_ml.neural_network.layers.parent_layers import VGroupNeuralNetworkLayer
import manim_ml
class ... |
ManimML_helblazer811/manim_ml/neural_network/layers/parent_layers.py | from manim import *
from abc import ABC, abstractmethod
class NeuralNetworkLayer(ABC, Group):
"""Abstract Neural Network Layer class"""
def __init__(self, text=None, *args, **kwargs):
super(Group, self).__init__()
self.title_text = kwargs["title"] if "title" in kwargs else " "
if "titl... |
ManimML_helblazer811/manim_ml/neural_network/layers/triplet_to_feed_forward.py | from manim import *
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
from manim_ml.neural_network.layers.triplet import TripletLayer
class TripletToFeedForward(ConnectiveLayer):
"""TripletLayer to FeedForward layer"""... |
ManimML_helblazer811/manim_ml/neural_network/layers/image.py | from manim import *
import numpy as np
from PIL import Image
from manim_ml.utils.mobjects.image import GrayscaleImageMobject
from manim_ml.neural_network.layers.parent_layers import NeuralNetworkLayer
class ImageLayer(NeuralNetworkLayer):
"""Single Image Layer for Neural Network"""
def __init__(
sel... |
ManimML_helblazer811/manim_ml/neural_network/layers/convolutional_2d_to_max_pooling_2d.py | import random
from manim import *
from manim_ml.utils.mobjects.gridded_rectangle import GriddedRectangle
from manim_ml.neural_network.layers.convolutional_2d_to_convolutional_2d import (
get_rotated_shift_vectors,
)
from manim_ml.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
from manim_ml.neural_ne... |
ManimML_helblazer811/manim_ml/neural_network/layers/feed_forward_to_vector.py | from manim import *
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
from manim_ml.neural_network.layers.vector import VectorLayer
class FeedForwardToVector(ConnectiveLayer):
"""Image Layer to FeedForward layer"""
... |
ManimML_helblazer811/manim_ml/neural_network/layers/feed_forward_to_feed_forward.py | from typing import List, Union
import numpy as np
from manim import *
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
import manim_ml
class FeedForwardToFeedForward(ConnectiveLayer):
"""Layer for connecting FeedForwa... |
ManimML_helblazer811/manim_ml/neural_network/layers/image_to_convolutional_2d.py | import numpy as np
from manim import *
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.image import ImageLayer
from manim_ml.neural_network.layers.parent_layers import (
ThreeDLayer,
VGroupNeuralNetworkLayer,
)
from manim_ml.utils.mobjects.gr... |
ManimML_helblazer811/manim_ml/neural_network/layers/max_pooling_2d_to_convolutional_2d.py | import numpy as np
from manim import *
from manim_ml.neural_network.layers.convolutional_2d_to_convolutional_2d import (
Convolutional2DToConvolutional2D,
Filters,
)
from manim_ml.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLa... |
ManimML_helblazer811/manim_ml/neural_network/layers/paired_query_to_feed_forward.py | from manim import *
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.paired_query import PairedQueryLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
class PairedQueryToFeedForward(ConnectiveLayer):
"""PairedQuery layer to Fe... |
ManimML_helblazer811/manim_ml/neural_network/layers/triplet.py | from manim import *
from manim_ml.neural_network.layers import NeuralNetworkLayer
from manim_ml.utils.mobjects.image import GrayscaleImageMobject, LabeledColorImage
import numpy as np
class TripletLayer(NeuralNetworkLayer):
"""Shows triplet images"""
def __init__(
self,
anchor,
positi... |
ManimML_helblazer811/manim_ml/neural_network/layers/convolutional_2d_to_convolutional_2d.py | import numpy as np
from manim import *
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer, ThreeDLayer
from manim_ml.utils.mobjects.gridded_rectangle import GriddedRectangle
import manim_ml
from manim.utils.space_op... |
ManimML_helblazer811/manim_ml/neural_network/layers/convolutional_2d_to_feed_forward.py | from manim import *
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer, ThreeDLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
class Convolutional2DToFeedForward(ConnectiveLayer, ThreeD... |
ManimML_helblazer811/manim_ml/neural_network/layers/convolutional_2d.py | from typing import Union
from manim_ml.neural_network.activation_functions import get_activation_function_by_name
from manim_ml.neural_network.activation_functions.activation_function import (
ActivationFunction,
)
import numpy as np
from manim import *
import manim_ml
from manim_ml.neural_network.layers.parent_la... |
ManimML_helblazer811/manim_ml/neural_network/layers/max_pooling_2d.py | from manim import *
from manim_ml.utils.mobjects.gridded_rectangle import GriddedRectangle
from manim_ml.neural_network.layers.parent_layers import (
ThreeDLayer,
VGroupNeuralNetworkLayer,
)
import manim_ml
class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
"""Max pooling layer for Convolutio... |
ManimML_helblazer811/manim_ml/neural_network/layers/embedding_to_feed_forward.py | from manim import *
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
from manim_ml.neural_network.layers.embedding import EmbeddingLayer
class EmbeddingToFeedForward(ConnectiveLayer):
"""Feed Forward to Embedding Laye... |
ManimML_helblazer811/manim_ml/neural_network/layers/feed_forward_to_embedding.py | from manim import *
from manim_ml.neural_network.layers.embedding import EmbeddingLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
class FeedForwardToEmbedding(ConnectiveLayer):
"""Feed Forward to Embedding Laye... |
ManimML_helblazer811/manim_ml/neural_network/architectures/__init__.py | |
ManimML_helblazer811/manim_ml/neural_network/architectures/feed_forward.py | import manim_ml
from manim_ml.neural_network.neural_network import NeuralNetwork
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
class FeedForwardNeuralNetwork(NeuralNetwork):
"""NeuralNetwork with just feed forward layers"""
def __init__(
self,
layer_node_count,
... |
ManimML_helblazer811/manim_ml/neural_network/architectures/variational_autoencoder.py | """Variational Autoencoder Manim Visualizations
In this module I define Manim visualizations for Variational Autoencoders
and Traditional Autoencoders.
"""
from manim import *
import numpy as np
from PIL import Image
from manim_ml.neural_network.layers import FeedForwardLayer, EmbeddingLayer, ImageLayer
from manim_ml... |
ManimML_helblazer811/manim_ml/neural_network/activation_functions/__init__.py | from manim_ml.neural_network.activation_functions.relu import ReLUFunction
from manim_ml.neural_network.activation_functions.sigmoid import SigmoidFunction
name_to_activation_function_map = {"ReLU": ReLUFunction, "Sigmoid": SigmoidFunction}
def get_activation_function_by_name(name):
assert (
name in name... |
ManimML_helblazer811/manim_ml/neural_network/activation_functions/sigmoid.py | from manim import *
import numpy as np
from manim_ml.neural_network.activation_functions.activation_function import (
ActivationFunction,
)
class SigmoidFunction(ActivationFunction):
"""Sigmoid Activation Function"""
def __init__(self, function_name="Sigmoid", x_range=[-5, 5], y_range=[0, 1]):
s... |
ManimML_helblazer811/manim_ml/neural_network/activation_functions/activation_function.py | from manim import *
from abc import ABC, abstractmethod
import random
import manim_ml.neural_network.activation_functions.relu as relu
import manim_ml
class ActivationFunction(ABC, VGroup):
"""Abstract parent class for defining activation functions"""
def __init__(
self,
function_name=None,
... |
ManimML_helblazer811/manim_ml/neural_network/activation_functions/relu.py | from manim import *
from manim_ml.neural_network.activation_functions.activation_function import (
ActivationFunction,
)
class ReLUFunction(ActivationFunction):
"""Rectified Linear Unit Activation Function"""
def __init__(self, function_name="ReLU", x_range=[-1, 1], y_range=[-1, 1]):
super().__in... |
ManimML_helblazer811/manim_ml/neural_network/animations/__init__.py | |
ManimML_helblazer811/manim_ml/neural_network/animations/neural_network_transformations.py | """
Transformations for manipulating a neural network object.
"""
from manim import *
from manim_ml.neural_network.layers.util import get_connective_layer
class RemoveLayer(AnimationGroup):
"""
Animation for removing a layer from a neural network.
Note: I needed to do something strange for creating ... |
ManimML_helblazer811/manim_ml/neural_network/animations/dropout.py | """
Code for making a dropout animation for the
feed forward layers of a neural network.
"""
from manim import *
import random
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.feed_forward_to_feed_forward import (
FeedForwardToFeedForward,
)
cl... |
ManimML_helblazer811/examples/neocognitron/neocognitron.py | from manim import *
from manim_ml.neural_network import NeuralNetwork
from manim_ml.neural_network.layers.parent_layers import NeuralNetworkLayer, ConnectiveLayer, ThreeDLayer
import manim_ml
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 10.5
config.frame_width = 10.5
class NeocognitronF... |
ManimML_helblazer811/examples/diffusion_process/diffusion_process.py | """
Shows video of diffusion process.
"""
import manim_ml
from manim import *
from PIL import Image
import os
from diffusers import StableDiffusionPipeline
import numpy as np
import scipy
from manim_ml.diffusion.mcmc import metropolis_hastings_sampler, gaussian_proposal
from manim_ml.diffusion.random_walk import ... |
ManimML_helblazer811/examples/basic_neural_network/residual_block.py | from manim import *
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.math_operation_layer import MathOperationLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 190... |
ManimML_helblazer811/examples/basic_neural_network/basic_neural_network.py | from manim import *
from manim_ml.neural_network.layers import FeedForwardLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
class NeuralNetworkScene(Scene):
"""Test Scene for the Neural Network"""
def construct(self):
# Make the Layer object
layers = [FeedForwardLayer(3),... |
ManimML_helblazer811/examples/code_snippet/vae_code_landscape.py | from manim import *
from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer, EmbeddingLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
from PIL import Image
import numpy as np
config.pixel_height = 720
config.pixel_width = 720
config.frame_height = 6.0
config.frame_width = 6.0
c... |
ManimML_helblazer811/examples/code_snippet/image_nn_code_snippet.py | from manim import *
from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
from PIL import Image
import numpy as np
config.pixel_height = 720
config.pixel_width = 1280
config.frame_height = 6.0
config.frame_width = 6.0
class ImageNeura... |
ManimML_helblazer811/examples/code_snippet/vae_nn_code_snippet.py | from manim import *
from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer, EmbeddingLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
from PIL import Image
import numpy as np
config.pixel_height = 720
config.pixel_width = 1280
config.frame_height = 6.0
config.frame_width = 6.0
... |
ManimML_helblazer811/examples/paper_visualizations/oracle_guidance/oracle_guidance.py | """
Here is a animated explanatory figure for the "Oracle Guided Image Synthesis with Relative Queries" paper.
"""
from pathlib import Path
from manim import *
from manim_ml.neural_network.layers import triplet
from manim_ml.neural_network.layers.image import ImageLayer
from manim_ml.neural_network.layers.paired_... |
ManimML_helblazer811/examples/mcmc/warmup_mcmc.py | from manim import *
import scipy.stats
from manim_ml.diffusion.mcmc import MCMCAxes
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('dark_background')
# Make the specific scene
config.pixel_height = 720
config.pixel_width = 720
config.frame_height = 7.0
config.frame_width = 7.0
class MCMCWarmupScen... |
ManimML_helblazer811/examples/disentanglement/disentanglement.py | """This module is dedicated to visualizing VAE disentanglement"""
from pathlib import Path
from manim import *
from manim_ml.neural_network.layers import FeedForwardLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
import pickle
ROOT_DIR = Path(__file__).parents[2]
def construct_image_mobject(... |
ManimML_helblazer811/examples/interpolation/interpolation.py | """Visualization of VAE Interpolation"""
import sys
import os
sys.path.append(os.environ["PROJECT_ROOT"])
from manim import *
import pickle
import numpy as np
import manim_ml.neural_network as neural_network
import examples.variational_autoencoder.variational_autoencoder as variational_autoencoder
"""
The VAE Sce... |
ManimML_helblazer811/examples/logo/logo.py | """
Logo for Manim Machine Learning
"""
from manim import *
import manim_ml
manim_ml.config.color_scheme = "light_mode"
from manim_ml.neural_network.architectures.feed_forward import FeedForwardNeuralNetwork
config.pixel_height = 1000
config.pixel_width = 1000
config.frame_height = 4.0
config.frame_width = 4.0
... |
ManimML_helblazer811/examples/logo/website_logo.py | """
Logo for Manim Machine Learning
"""
from manim import *
from manim_ml.neural_network.neural_network import FeedForwardNeuralNetwork
config.pixel_height = 400
config.pixel_width = 600
config.frame_height = 8.0
config.frame_width = 10.0
class ManimMLLogo(Scene):
def construct(self):
self.neural_net... |
ManimML_helblazer811/examples/logo/wide_logo.py | """
Logo for Manim Machine Learning
"""
from manim import *
import manim_ml
manim_ml.config.color_scheme = "light_mode"
from manim_ml.neural_network.architectures.feed_forward import FeedForwardNeuralNetwork
config.pixel_height = 1000
config.pixel_width = 2000
config.frame_height = 4.0
config.frame_width = 8.0
... |
ManimML_helblazer811/examples/lenet/lenet.py | from pathlib import Path
from manim import *
from PIL import Image
import numpy as np
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.image import ImageLayer
from manim_ml.neur... |
ManimML_helblazer811/examples/translation_equivariance/translation_equivariance.py | from manim import *
from PIL import Image
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
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.parent_layers import ThreeD... |
ManimML_helblazer811/examples/cross_attention_vis/cross_attention_vis.py | """
Here I thought it would be interesting to visualize the cross attention
maps produced by the UNet of a text-to-image diffusion model.
The key thing I want to show is how images and text are broken up into tokens (patches for images),
and those tokens are used to compute a cross attention score, wh... |
ManimML_helblazer811/examples/decision_tree/split_scene.py | from sklearn import datasets
from decision_tree_surface import *
from manim import *
from sklearn.tree import DecisionTreeClassifier
from scipy.stats import entropy
import math
from PIL import Image
iris = datasets.load_iris()
font = "Source Han Sans"
font_scale = 0.75
images = [
Image.open("iris_dataset/SetosaFl... |
ManimML_helblazer811/examples/decision_tree/decision_tree_surface.py | import numpy as np
from collections import deque
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import _tree as ctree
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
class AABB:
"""Axis-aligned bounding box"""
def __init__(self, n_features):
self.limits = n... |
ManimML_helblazer811/examples/variational_autoencoder/variational_autoencoder.py | """Autoencoder Manim Visualizations
In this module I define Manim visualizations for Variational Autoencoders
and Traditional Autoencoders.
"""
from pathlib import Path
from manim import *
import numpy as np
from PIL import Image
from manim_ml.neural_network.layers import EmbeddingLayer
from manim_ml.neural_network.... |
ManimML_helblazer811/examples/variational_autoencoder/autoencoder_models/generate_interpolation.py | import torch
from variational_autoencoder import VAE, load_dataset
import matplotlib.pyplot as plt
from torchvision import datasets
from torchvision import transforms
from tqdm import tqdm
import numpy as np
import pickle
# Load model
vae = VAE(latent_dim=16)
vae.load_state_dict(torch.load("saved_models/model.pth"))
d... |
ManimML_helblazer811/examples/variational_autoencoder/autoencoder_models/__init__.py | |
ManimML_helblazer811/examples/variational_autoencoder/autoencoder_models/generate_disentanglement.py | import pickle
import sys
import os
sys.path.append(os.environ["PROJECT_ROOT"])
from autoencoder_models.variational_autoencoder import (
VAE,
load_dataset,
load_vae_from_path,
)
import matplotlib.pyplot as plt
import numpy as np
import torch
import scipy
import scipy.stats
import cv2
def binned_images(mod... |
ManimML_helblazer811/examples/variational_autoencoder/autoencoder_models/generate_images.py | import torch
from variational_autoencoder import VAE
import matplotlib.pyplot as plt
from torchvision import datasets
from torchvision import transforms
from tqdm import tqdm
import numpy as np
import pickle
# Load model
vae = VAE(latent_dim=16)
vae.load_state_dict(torch.load("saved_models/model.pth"))
# Transforms im... |
ManimML_helblazer811/examples/variational_autoencoder/autoencoder_models/variational_autoencoder.py | import torch
from torchvision import datasets
from torchvision import transforms
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from tqdm import tqdm
import math
"""
These are utility functions that help to calculate the input and output
sizes of convolutional neural netw... |
ManimML_helblazer811/examples/gan/gan.py | import random
from pathlib import Path
from PIL import Image
from manim import *
from manim_ml.neural_network.layers.embedding import EmbeddingLayer
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.v... |
ManimML_helblazer811/examples/readme_example/first_neural_network.py | from manim import *
from manim_ml.neural_network import (
Convolutional2DLayer,
FeedForwardLayer,
NeuralNetwork,
)
# Make the specific scene
config.pixel_height = 700
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0
class CombinedScene(ThreeDScene):
def construct(self):
... |
ManimML_helblazer811/examples/readme_example/convolutional_neural_networks.py | from manim import *
from manim_ml.neural_network import (
Convolutional2DLayer,
FeedForwardLayer,
NeuralNetwork,
)
# Make the specific scene
config.pixel_height = 700
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0
class CombinedScene(ThreeDScene):
def construct(self):
... |
ManimML_helblazer811/examples/readme_example/activation_functions.py | from manim import *
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
... |
ManimML_helblazer811/examples/readme_example/neural_network_dropout.py | from manim import *
from manim_ml.neural_network.animations.dropout import (
make_neural_network_dropout_animation,
)
from manim_ml.neural_network import FeedForwardLayer, NeuralNetwork
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 5.0
config.frame_width = 5.0
class DropoutNeuralNetw... |
ManimML_helblazer811/examples/readme_example/convolutional_neural_network_with_images.py | from manim import *
from PIL import Image
import numpy as np
from manim_ml.neural_network import (
Convolutional2DLayer,
FeedForwardLayer,
NeuralNetwork,
ImageLayer,
)
# Make the specific scene
config.pixel_height = 700
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0
cla... |
ManimML_helblazer811/examples/readme_example/animating_the_forward_pass.py | from manim import *
from manim_ml.neural_network import (
Convolutional2DLayer,
FeedForwardLayer,
NeuralNetwork,
)
# Make the specific scene
config.pixel_height = 700
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0
class CombinedScene(ThreeDScene):
def construct(self):
... |
ManimML_helblazer811/examples/readme_example/a_simple_feed_forward_network.py | from manim import *
from manim_ml.neural_network import FeedForwardLayer, NeuralNetwork
# Make the specific scene
config.pixel_height = 700
config.pixel_width = 1200
config.frame_height = 4.0
config.frame_width = 4.0
class CombinedScene(ThreeDScene):
def construct(self):
# Make nn
nn = NeuralNet... |
ManimML_helblazer811/examples/readme_example/max_pooling.py | from manim import *
from PIL import Image
import numpy as np
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
# Make the specific scene
config.pi... |
ManimML_helblazer811/examples/readme_example/setting_up_a_scene.py | from manim import *
# Import modules here
class BasicScene(ThreeDScene):
def construct(self):
# Your code goes here
text = Text("Your first scene!")
self.add(text)
|
ManimML_helblazer811/examples/readme_example/example.py | from manim import *
from manim_ml.neural_network import (
Convolutional2DLayer,
FeedForwardLayer,
NeuralNetwork,
)
# Make the specific scene
config.pixel_height = 700
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0
class CombinedScene(ThreeDScene):
def construct(self):
... |
ManimML_helblazer811/examples/readme_example/old_example.py | from manim import *
from PIL import Image
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.image import ImageLayer
from manim_ml.neural_network.neural_network import NeuralNetwor... |
ManimML_helblazer811/examples/epsilon_nn_graph/epsilon_nn_graph.py | """
Example where I draw an epsilon nearest neighbor graph animation
"""
from cProfile import label
from manim import *
from sklearn.datasets import make_moons
from sklearn.cluster import SpectralClustering
import numpy as np
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1200
config.fra... |
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