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 = 6.0 config.frame_width = 6.0 def make_code_snippet(): code_str = """ # Make the neural network nn = NeuralNetwork([ ImageLayer(image), Convolutional2DLayer(1, 8), MaxPooling2DLayer(kernel_size=2), Convolutional2DLayer(3, 2, 3), ]) # Play 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", font="Monospace", background="window", language="py", ) code.scale(0.4) return code 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, 8, filter_spacing=0.32), MaxPooling2DLayer(kernel_size=2), Convolutional2DLayer(3, 2, 3, filter_spacing=0.32), ], 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) Group(code, nn).move_to(ORIGIN) self.add(code) self.wait(5) # Play animation forward_pass = nn.make_forward_pass_animation() self.wait(1) self.play(forward_pass)