Files
2023-02-01 22:33:42 -05:00

56 lines
1.7 KiB
Python

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.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 20.0
config.frame_width = 20.0
ROOT_DIR = Path(__file__).parents[2]
class CombinedScene(ThreeDScene):
def construct(self):
image = Image.open(ROOT_DIR / "assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
# Make nn
nn = NeuralNetwork(
[
ImageLayer(numpy_image, height=4.5),
Convolutional2DLayer(1, 28),
Convolutional2DLayer(6, 28, 5),
MaxPooling2DLayer(kernel_size=2),
Convolutional2DLayer(16, 10, 5),
MaxPooling2DLayer(kernel_size=2),
FeedForwardLayer(8),
FeedForwardLayer(3),
FeedForwardLayer(2),
],
layer_spacing=0.25,
)
# Center the nn
nn.move_to(ORIGIN)
self.add(nn)
# Make code snippet
# code = make_code_snippet()
# code.next_to(nn, DOWN)
# self.add(code)
# Group it all
# group = Group(nn, code)
# group.move_to(ORIGIN)
nn.move_to(ORIGIN)
# Play animation
# forward_pass = nn.make_forward_pass_animation()
# self.wait(1)
# self.play(forward_pass)