Files
ManimML/tests/test_residual_connection.py
2023-02-01 22:33:42 -05:00

71 lines
1.8 KiB
Python

from manim import *
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.utils.testing.frames_comparison import frames_comparison
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer, ImageLayer
from PIL import Image
import numpy as np
__module_test__ = "residual"
@frames_comparison
def test_ResidualConnectionScene(scene):
"""Tests the appearance of a residual connection"""
nn = NeuralNetwork(
{
"layer1": FeedForwardLayer(3),
"layer2": FeedForwardLayer(5),
"layer3": FeedForwardLayer(3),
}
)
scene.add(nn)
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 6.0
config.frame_width = 6.0
class FeedForwardScene(Scene):
def construct(self):
nn = NeuralNetwork(
{
"layer1": FeedForwardLayer(4),
"layer2": FeedForwardLayer(4),
"layer3": FeedForwardLayer(4),
},
layer_spacing=0.45,
)
nn.add_connection("layer1", "layer3")
self.add(nn)
self.play(nn.make_forward_pass_animation(), run_time=8)
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)
self.play(nn.make_forward_pass_animation(), run_time=8)