Files
ManimML/examples/cnn/resnet_block.py
2023-02-01 22:33:42 -05:00

66 lines
1.7 KiB
Python

from manim import *
from PIL import Image
import numpy as np
from manim_ml.neural_network import Convolutional2DLayer, NeuralNetwork
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 6.0
config.frame_width = 6.0
def make_code_snippet():
code_str = """
# Make the neural network
nn = NeuralNetwork({
"layer1": Convolutional2DLayer(1, 5, padding=1),
"layer2": Convolutional2DLayer(1, 5, 3, padding=1),
"layer3": Convolutional2DLayer(1, 5, 3, padding=1)
})
# Add the residual connection
nn.add_connection("layer1", "layer3")
# Make the animation
self.play(nn.make_forward_pass_animation())
"""
code = Code(
code=code_str,
tab_width=4,
background_stroke_width=1,
background_stroke_color=WHITE,
insert_line_no=False,
style="monokai",
# background="window",
language="py",
)
code.scale(0.38)
return code
class ConvScene(ThreeDScene):
def construct(self):
image = Image.open("../../assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
nn = NeuralNetwork(
{
"layer1": Convolutional2DLayer(1, 5, padding=1),
"layer2": Convolutional2DLayer(1, 5, 3, padding=1),
"layer3": Convolutional2DLayer(1, 5, 3, padding=1),
},
layer_spacing=0.25,
)
nn.add_connection("layer1", "layer3")
self.add(nn)
code = make_code_snippet()
code.next_to(nn, DOWN)
self.add(code)
Group(code, nn).move_to(ORIGIN)
self.play(nn.make_forward_pass_animation(), run_time=8)