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General changes, got basic visualization of an activation function working for a

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convolutinoal layer.
This commit is contained in:
Alec Helbling
2023-01-24 15:44:48 -05:00
parent 5291d9db8a
commit ce184af78e
34 changed files with 1575 additions and 479 deletions
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38
tests/test_activation_function.py Normal file
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@ -0,0 +1,38 @@
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.neural_network import NeuralNetwork
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 6.0
config.frame_width = 6.0
class CombinedScene(ThreeDScene):
def construct(self):
image = Image.open("../assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
# Make nn
nn = NeuralNetwork([
ImageLayer(numpy_image, height=1.5),
Convolutional2DLayer(1, 7, filter_spacing=0.32),
Convolutional2DLayer(3, 5, 3, filter_spacing=0.32, activation_function="ReLU"),
FeedForwardLayer(3),
],
layer_spacing=0.25,
)
# Center the nn
nn.move_to(ORIGIN)
self.add(nn)
# Play animation
forward_pass = nn.make_forward_pass_animation(
corner_pulses=False,
all_filters_at_once=False
)
self.wait(1)
self.play(forward_pass)

15
tests/test_convolutional_2d_layer.py
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@ -34,10 +34,14 @@ class Simple3DConvScene(ThreeDScene):
# Make nn
layers = [
Convolutional2DLayer(
1, feature_map_size=3, filter_size=3
num_feature_maps=1,
feature_map_size=3,
filter_size=3
),
Convolutional2DLayer(
1, feature_map_size=3, filter_size=3
num_feature_maps=1,
feature_map_size=3,
filter_size=3
),
]
nn = NeuralNetwork(layers)
@ -59,12 +63,11 @@ class CombinedScene(ThreeDScene):
image = Image.open("../assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
# Make nn
nn = NeuralNetwork(
[
nn = NeuralNetwork([
ImageLayer(numpy_image, height=1.5),
Convolutional2DLayer(1, 7, 3, filter_spacing=0.32),
Convolutional2DLayer(1, 7, filter_spacing=0.32),
Convolutional2DLayer(3, 5, 3, filter_spacing=0.32),
Convolutional2DLayer(5, 3, 1, filter_spacing=0.18),
Convolutional2DLayer(5, 3, 3, filter_spacing=0.18),
FeedForwardLayer(3),
FeedForwardLayer(3),
],

34
tests/test_max_pool.py
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@ -19,12 +19,36 @@ class CombinedScene(ThreeDScene):
image = Image.open("../assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
# Make nn
nn = NeuralNetwork(
[
nn = NeuralNetwork([
ImageLayer(numpy_image, height=1.5),
Convolutional2DLayer(1, 8, 8, 3, 3, filter_spacing=0.32),
Convolutional2DLayer(1, 8, filter_spacing=0.32),
MaxPooling2DLayer(kernel_size=2),
Convolutional2DLayer(3, 3, 2, filter_spacing=0.32),
],
layer_spacing=0.25,
)
# Center the nn
nn.move_to(ORIGIN)
self.add(nn)
self.wait(5)
# Play animation
forward_pass = nn.make_forward_pass_animation(
corner_pulses=False, all_filters_at_once=False
)
print(forward_pass)
print(forward_pass.animations)
self.wait(1)
self.play(forward_pass)
class SmallNetwork(ThreeDScene):
def construct(self):
image = Image.open("../assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
# Make nn
nn = NeuralNetwork([
ImageLayer(numpy_image, height=1.5),
Convolutional2DLayer(1, 8, filter_spacing=0.32),
MaxPooling2DLayer(kernel_size=2),
Convolutional2DLayer(3, 5, 5, 3, 3, filter_spacing=0.32),
],
layer_spacing=0.25,
)
@ -36,4 +60,4 @@ class CombinedScene(ThreeDScene):
corner_pulses=False, all_filters_at_once=False
)
self.wait(1)
self.play(forward_pass)
self.play(forward_pass)

19
tests/test_succession.py Normal file
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@ -0,0 +1,19 @@
from manim import *
class TestSuccession(Scene):
def construct(self):
white_dot = Dot(color=WHITE)
white_dot.shift(UP)
red_dot = Dot(color=RED)
self.play(
Succession(
Create(white_dot),
white_dot.animate.shift(RIGHT),
Create(red_dot),
Wait(1),
Uncreate(red_dot),
)
)