Readme changes

Readme.md

@@ -0,0 +1,26 @@
+# Manim Machine Learning
+
+Manim Machine Learning is a project focused on providing animations and visualizations of common machine learning concepts with the [Manim Community Library](https://www.manim.community/). We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine learning concepts. 
+
+## Getting Started 
+First you will want to install manim. Then you can run the following to generate the example videos. 
+
+`make video`
+
+or 
+
+`manim -pqh src/vae.py VAEScene`
+
+## Examples
+
+### Variational Autoencoders
+
+This is a visualization of a Variational Autoencoder. You can also find a video form in examples/ 
+
+<img src="examples/VAEImage.png" width="600">
+
+### Neural Networks
+
+This is a visualization of a Neural Network. You can find a video animation of a neural network in examples/
+
+<img src="examples/NNImage.png" width="600">

src/neural_network.py

@@ -134,6 +134,11 @@ class NeuralNetwork(VGroup):
 
         return animation_group
 
+config.pixel_height = 720 
+config.pixel_width = 1280 
+config.frame_height = 6.0
+config.frame_width = 6.0
+
 class TestNeuralNetworkScene(Scene):
     """Test Scene for the Neural Network"""
 

src/vae.py

@@ -297,3 +297,18 @@ class VAEScene(Scene):
         interpolation_images = mnist_image_handler.interpolation_images
         interpolation_animation = vae.make_interpolation_animation(interpolation_images)
         self.play(interpolation_animation)
+
+class VAEImage(Scene):
+
+    def construct(self):
+        # Set Scene config
+        vae = VariationalAutoencoder()
+        mnist_image_handler = MNISTImageHandler()
+        image_pair = mnist_image_handler.image_pairs[3]
+        vae.move_to(ORIGIN)
+        vae.scale(1.3)
+        self.play(Create(vae), run_time=3)
+        # Make a forward pass animation
+        forward_pass_animation = vae.make_forward_pass_animation(image_pair)
+        self.play(forward_pass_animation)
+