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Added CNN example to the readme.

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Alec Helbling
2023-01-25 08:49:19 -05:00
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@ -22,7 +22,7 @@ Then install the package form source or
Then you can run the following to generate the example videos from python scripts.
`manim -pqh src/vae.py VAEScene`
`manim -pqh examples/cnn/cnn.py`
## Examples
@ -32,48 +32,36 @@ Checkout the ```examples``` directory for some example videos with source code.
This is a visualization of a Convolutional Neural Network.
<img src="examples/media/CNNScene.gif">
### Neural Networks
This is a visualization of a Variational Autoencoder made using ManimML. It has a Pytorch style list of layers that can be composed in arbitrary order. The following video is made with the code from below.
<img src="examples/media/VAEScene.gif">
<img src="assets/BasicCNNGIF.gif">
```python
class VariationalAutoencoderScene(Scene):
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 NeuralNetwork
class ConvolutinoalNetworkScene(Scene):
def construct(self):
embedding_layer = EmbeddingLayer(dist_theme="ellipse").scale(2)
image = Image.open('images/image.jpeg')
image = Image.open(ROOT_DIR / "assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
# Make nn
neural_network = NeuralNetwork([
ImageLayer(numpy_image, height=1.4),
FeedForwardLayer(5),
FeedForwardLayer(3),
embedding_layer,
FeedForwardLayer(3),
FeedForwardLayer(5),
ImageLayer(numpy_image, height=1.4),
], layer_spacing=0.1)
neural_network.scale(1.3)
self.play(Create(neural_network))
self.play(neural_network.make_forward_pass_animation(run_time=15))
nn = NeuralNetwork([
ImageLayer(numpy_image, height=1.5),
Convolutional2DLayer(1, 7, 3, filter_spacing=0.32),
Convolutional2DLayer(3, 5, 3, filter_spacing=0.32),
Convolutional2DLayer(5, 3, 3, filter_spacing=0.18),
FeedForwardLayer(3),
FeedForwardLayer(3),
],
layer_spacing=0.25,
)
# Center the nn
nn.move_to(ORIGIN)
self.add(nn)
self.play(neural_network.make_forward_pass_animation())
```
### Generative Adversarial Network
This is a visualization of a Generative Adversarial Network made using ManimML.
<img src="examples/media/GANScene.gif">
### VAE Disentanglement
This is a visualization of disentanglement with a Variational Autoencoder
<img src="examples/media/DisentanglementScene.gif">