Bug fixes and linting for the activation functions addition.

examples/basic_neural_network/basic_neural_network.py

@@ -8,7 +8,11 @@ class NeuralNetworkScene(Scene):
 
     def construct(self):
         # Make the Layer object
-        layers = [FeedForwardLayer(3), FeedForwardLayer(5), FeedForwardLayer(3)]
+        layers = [
+            FeedForwardLayer(3), 
+            FeedForwardLayer(5), 
+            FeedForwardLayer(3)
+        ]
         nn = NeuralNetwork(layers)
         nn.scale(2)
         nn.move_to(ORIGIN)

examples/cnn/activation_functions.py

@@ -0,0 +1,75 @@
+from pathlib import Path
+
+from manim import *
+from PIL import Image
+
+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 = 7.0
+config.frame_width = 7.0
+ROOT_DIR = Path(__file__).parents[2]
+
+def make_code_snippet():
+    code_str = """
+        # Make the neural network
+        nn = NeuralNetwork([
+            # ... Layers at start
+            Convolutional2DLayer(3, 5, 3, activation_function="ReLU"),
+            # ... Layers at end
+        ])
+        # 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(0.45)
+
+    return code
+
+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=1.5),
+                Convolutional2DLayer(1, 7),
+                Convolutional2DLayer(3, 5, 3, activation_function="ReLU"),
+                Convolutional2DLayer(5, 3, 3, activation_function="ReLU"),
+                FeedForwardLayer(3),
+                FeedForwardLayer(1),
+            ],
+            layer_spacing=0.25,
+        )
+        # nn.scale(0.7)
+        # 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)
+        nn.move_to(ORIGIN)
+        # Move everything up
+        Group(nn, code).move_to(ORIGIN)
+        # Play animation
+        forward_pass = nn.make_forward_pass_animation()
+        self.wait(1)
+        self.play(forward_pass)

examples/cnn/cnn.py

@@ -15,7 +15,6 @@ config.frame_height = 7.0
 config.frame_width = 7.0
 ROOT_DIR = Path(__file__).parents[2]
 
-
 def make_code_snippet():
     code_str = """
         # Make nn
@@ -45,7 +44,6 @@ def make_code_snippet():
 
     return code
 
-
 class CombinedScene(ThreeDScene):
     def construct(self):
         image = Image.open(ROOT_DIR / "assets/mnist/digit.jpeg")

examples/decision_tree/decision_tree_surface.py

@@ -0,0 +1,90 @@
+import numpy as np
+from collections import deque
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import _tree as ctree
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+
+
+class AABB:
+    """Axis-aligned bounding box"""
+
+    def __init__(self, n_features):
+        self.limits = np.array([[-np.inf, np.inf]] * n_features)
+
+    def split(self, f, v):
+        left = AABB(self.limits.shape[0])
+        right = AABB(self.limits.shape[0])
+        left.limits = self.limits.copy()
+        right.limits = self.limits.copy()
+        left.limits[f, 1] = v
+        right.limits[f, 0] = v
+
+        return left, right
+
+
+def tree_bounds(tree, n_features=None):
+    """Compute final decision rule for each node in tree"""
+    if n_features is None:
+        n_features = np.max(tree.feature) + 1
+    aabbs = [AABB(n_features) for _ in range(tree.node_count)]
+    queue = deque([0])
+    while queue:
+        i = queue.pop()
+        l = tree.children_left[i]
+        r = tree.children_right[i]
+        if l != ctree.TREE_LEAF:
+            aabbs[l], aabbs[r] = aabbs[i].split(tree.feature[i], tree.threshold[i])
+            queue.extend([l, r])
+    return aabbs
+
+
+def decision_areas(tree_classifier, maxrange, x=0, y=1, n_features=None):
+    """Extract decision areas.
+
+    tree_classifier: Instance of a sklearn.tree.DecisionTreeClassifier
+    maxrange: values to insert for [left, right, top, bottom] if the interval is open (+/-inf)
+    x: index of the feature that goes on the x axis
+    y: index of the feature that goes on the y axis
+    n_features: override autodetection of number of features
+    """
+    tree = tree_classifier.tree_
+    aabbs = tree_bounds(tree, n_features)
+    maxrange = np.array(maxrange)
+    rectangles = []
+    for i in range(len(aabbs)):
+        if tree.children_left[i] != ctree.TREE_LEAF:
+            continue
+        l = aabbs[i].limits
+        r = [l[x, 0], l[x, 1], l[y, 0], l[y, 1], np.argmax(tree.value[i])]
+        # clip out of bounds indices
+        """
+        if r[0] < maxrange[0]:
+            r[0] = maxrange[0]
+        if r[1] > maxrange[1]:
+            r[1] = maxrange[1]
+        if r[2] < maxrange[2]:
+            r[2] = maxrange[2]
+        if r[3] > maxrange[3]:
+            r[3] = maxrange[3]
+        print(r)
+        """
+        rectangles.append(r)
+    rectangles = np.array(rectangles)
+    rectangles[:, [0, 2]] = np.maximum(rectangles[:, [0, 2]], maxrange[0::2])
+    rectangles[:, [1, 3]] = np.minimum(rectangles[:, [1, 3]], maxrange[1::2])
+    return rectangles
+
+
+def plot_areas(rectangles):
+    for rect in rectangles:
+        color = ["b", "r"][int(rect[4])]
+        print(rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1])
+        rp = Rectangle(
+            [rect[0], rect[2]],
+            rect[1] - rect[0],
+            rect[3] - rect[2],
+            color=color,
+            alpha=0.3,
+        )
+        plt.gca().add_artist(rp)

examples/decision_tree/split_scene.py

@@ -0,0 +1,690 @@
+from sklearn import datasets
+from decision_tree_surface import *
+from manim import *
+from sklearn.tree import DecisionTreeClassifier
+from scipy.stats import entropy
+import math
+from PIL import Image
+
+iris = datasets.load_iris()
+font = "Source Han Sans"
+font_scale = 0.75
+
+images = [
+    Image.open("iris_dataset/SetosaFlower.jpeg"),
+    Image.open("iris_dataset/VeriscolorFlower.jpeg"),
+    Image.open("iris_dataset/VirginicaFlower.jpeg"),
+]
+
+
+def entropy(class_labels, base=2):
+    # compute the class counts
+    unique, counts = np.unique(class_labels, return_counts=True)
+    dictionary = dict(zip(unique, counts))
+    total = 0.0
+    num_samples = len(class_labels)
+    for class_index in range(0, 3):
+        if not class_index in dictionary:
+            continue
+        prob = dictionary[class_index] / num_samples
+        total += prob * math.log(prob, base)
+    # higher set
+    return -total
+
+
+def merge_overlapping_polygons(all_polygons, colors=[BLUE, GREEN, ORANGE]):
+    # get all polygons of each color
+    polygon_dict = {
+        str(BLUE).lower(): [],
+        str(GREEN).lower(): [],
+        str(ORANGE).lower(): [],
+    }
+    for polygon in all_polygons:
+        print(polygon_dict)
+        polygon_dict[str(polygon.color).lower()].append(polygon)
+
+    return_polygons = []
+    for color in colors:
+        color = str(color).lower()
+        polygons = polygon_dict[color]
+        points = set()
+        for polygon in polygons:
+            vertices = polygon.get_vertices().tolist()
+            vertices = [tuple(vert) for vert in vertices]
+            for pt in vertices:
+                if pt in points:  # Shared vertice, remove it.
+                    points.remove(pt)
+                else:
+                    points.add(pt)
+        points = list(points)
+        sort_x = sorted(points)
+        sort_y = sorted(points, key=lambda x: x[1])
+
+        edges_h = {}
+        edges_v = {}
+
+        i = 0
+        while i < len(points):
+            curr_y = sort_y[i][1]
+            while i < len(points) and sort_y[i][1] == curr_y:
+                edges_h[sort_y[i]] = sort_y[i + 1]
+                edges_h[sort_y[i + 1]] = sort_y[i]
+                i += 2
+        i = 0
+        while i < len(points):
+            curr_x = sort_x[i][0]
+            while i < len(points) and sort_x[i][0] == curr_x:
+                edges_v[sort_x[i]] = sort_x[i + 1]
+                edges_v[sort_x[i + 1]] = sort_x[i]
+                i += 2
+
+        # Get all the polygons.
+        while edges_h:
+            # We can start with any point.
+            polygon = [(edges_h.popitem()[0], 0)]
+            while True:
+                curr, e = polygon[-1]
+                if e == 0:
+                    next_vertex = edges_v.pop(curr)
+                    polygon.append((next_vertex, 1))
+                else:
+                    next_vertex = edges_h.pop(curr)
+                    polygon.append((next_vertex, 0))
+                if polygon[-1] == polygon[0]:
+                    # Closed polygon
+                    polygon.pop()
+                    break
+            # Remove implementation-markers from the polygon.
+            poly = [point for point, _ in polygon]
+            for vertex in poly:
+                if vertex in edges_h:
+                    edges_h.pop(vertex)
+                if vertex in edges_v:
+                    edges_v.pop(vertex)
+            polygon = Polygon(*poly, color=color, fill_opacity=0.3, stroke_opacity=1.0)
+            return_polygons.append(polygon)
+    return return_polygons
+
+
+class IrisDatasetPlot(VGroup):
+    def __init__(self):
+        points = iris.data[:, 0:2]
+        labels = iris.feature_names
+        targets = iris.target
+        # Make points
+        self.point_group = self._make_point_group(points, targets)
+        # Make axes
+        self.axes_group = self._make_axes_group(points, labels)
+        # Make legend
+        self.legend_group = self._make_legend(
+            [BLUE, ORANGE, GREEN], iris.target_names, self.axes_group
+        )
+        # Make title
+        # title_text = "Iris Dataset Plot"
+        # self.title = Text(title_text).match_y(self.axes_group).shift([0.5, self.axes_group.height / 2 + 0.5, 0])
+        # Make all group
+        self.all_group = Group(self.point_group, self.axes_group, self.legend_group)
+        # scale the groups
+        self.point_group.scale(1.6)
+        self.point_group.match_x(self.axes_group)
+        self.point_group.match_y(self.axes_group)
+        self.point_group.shift([0.2, 0, 0])
+        self.axes_group.scale(0.7)
+        self.all_group.shift([0, 0.2, 0])
+
+    @override_animation(Create)
+    def create_animation(self):
+        animation_group = AnimationGroup(
+            # Perform the animations
+            Create(self.point_group, run_time=2),
+            Wait(0.5),
+            Create(self.axes_group, run_time=2),
+            # add title
+            # Create(self.title),
+            Create(self.legend_group),
+        )
+        return animation_group
+
+    def _make_point_group(self, points, targets, class_colors=[BLUE, ORANGE, GREEN]):
+        point_group = VGroup()
+        for point_index, point in enumerate(points):
+            # draw the dot
+            current_target = targets[point_index]
+            color = class_colors[current_target]
+            dot = Dot(point=np.array([point[0], point[1], 0])).set_color(color)
+            dot.scale(0.5)
+            point_group.add(dot)
+        return point_group
+
+    def _make_legend(self, class_colors, feature_labels, axes):
+        legend_group = VGroup()
+        # Make Text
+        setosa = Text("Setosa", color=BLUE)
+        verisicolor = Text("Verisicolor", color=ORANGE)
+        virginica = Text("Virginica", color=GREEN)
+        labels = VGroup(setosa, verisicolor, virginica).arrange(
+            direction=RIGHT, aligned_edge=LEFT, buff=2.0
+        )
+        labels.scale(0.5)
+        legend_group.add(labels)
+        # surrounding rectangle
+        surrounding_rectangle = SurroundingRectangle(labels, color=WHITE)
+        surrounding_rectangle.move_to(labels)
+        legend_group.add(surrounding_rectangle)
+        # shift the legend group
+        legend_group.move_to(axes)
+        legend_group.shift([0, -3.0, 0])
+        legend_group.match_x(axes[0][0])
+
+        return legend_group
+
+    def _make_axes_group(self, points, labels):
+        axes_group = VGroup()
+        # make the axes
+        x_range = [
+            np.amin(points, axis=0)[0] - 0.2,
+            np.amax(points, axis=0)[0] - 0.2,
+            0.5,
+        ]
+        y_range = [np.amin(points, axis=0)[1] - 0.2, np.amax(points, axis=0)[1], 0.5]
+        axes = Axes(
+            x_range=x_range,
+            y_range=y_range,
+            x_length=9,
+            y_length=6.5,
+            # axis_config={"number_scale_value":0.75, "include_numbers":True},
+            tips=False,
+        ).shift([0.5, 0.25, 0])
+        axes_group.add(axes)
+        # make axis labels
+        # x_label
+        x_label = (
+            Text(labels[0], font=font)
+            .match_y(axes.get_axes()[0])
+            .shift([0.5, -0.75, 0])
+            .scale(font_scale)
+        )
+        axes_group.add(x_label)
+        # y_label
+        y_label = (
+            Text(labels[1], font=font)
+            .match_x(axes.get_axes()[1])
+            .shift([-0.75, 0, 0])
+            .rotate(np.pi / 2)
+            .scale(font_scale)
+        )
+        axes_group.add(y_label)
+
+        return axes_group
+
+
+class DecisionTreeSurface(VGroup):
+    def __init__(self, tree_clf, data, axes, class_colors=[BLUE, ORANGE, GREEN]):
+        # take the tree and construct the surface from it
+        self.tree_clf = tree_clf
+        self.data = data
+        self.axes = axes
+        self.class_colors = class_colors
+        self.surface_rectangles = self.generate_surface_rectangles()
+
+    def generate_surface_rectangles(self):
+        # compute data bounds
+        left = np.amin(self.data[:, 0]) - 0.2
+        right = np.amax(self.data[:, 0]) - 0.2
+        top = np.amax(self.data[:, 1])
+        bottom = np.amin(self.data[:, 1]) - 0.2
+        maxrange = [left, right, bottom, top]
+        rectangles = decision_areas(self.tree_clf, maxrange, x=0, y=1, n_features=2)
+        # turn the rectangle objects into manim rectangles
+        def convert_rectangle_to_polygon(rect):
+            # get the points for the rectangle in the plot coordinate frame
+            bottom_left = [rect[0], rect[3]]
+            bottom_right = [rect[1], rect[3]]
+            top_right = [rect[1], rect[2]]
+            top_left = [rect[0], rect[2]]
+            # convert those points into the entire manim coordinates
+            bottom_left_coord = self.axes.coords_to_point(*bottom_left)
+            bottom_right_coord = self.axes.coords_to_point(*bottom_right)
+            top_right_coord = self.axes.coords_to_point(*top_right)
+            top_left_coord = self.axes.coords_to_point(*top_left)
+            points = [
+                bottom_left_coord,
+                bottom_right_coord,
+                top_right_coord,
+                top_left_coord,
+            ]
+            # construct a polygon object from those manim coordinates
+            rectangle = Polygon(
+                *points, color=color, fill_opacity=0.3, stroke_opacity=0.0
+            )
+            return rectangle
+
+        manim_rectangles = []
+        for rect in rectangles:
+            color = self.class_colors[int(rect[4])]
+            rectangle = convert_rectangle_to_polygon(rect)
+            manim_rectangles.append(rectangle)
+
+        manim_rectangles = merge_overlapping_polygons(
+            manim_rectangles, colors=[BLUE, GREEN, ORANGE]
+        )
+
+        return manim_rectangles
+
+    @override_animation(Create)
+    def create_override(self):
+        # play a reveal of all of the surface rectangles
+        animations = []
+        for rectangle in self.surface_rectangles:
+            animations.append(Create(rectangle))
+        animation_group = AnimationGroup(*animations)
+
+        return animation_group
+
+    @override_animation(Uncreate)
+    def uncreate_override(self):
+        # play a reveal of all of the surface rectangles
+        animations = []
+        for rectangle in self.surface_rectangles:
+            animations.append(Uncreate(rectangle))
+        animation_group = AnimationGroup(*animations)
+
+        return animation_group
+
+
+class DecisionTree:
+    """
+    Draw a single tree node
+    """
+
+    def _make_node(
+        self,
+        feature,
+        threshold,
+        values,
+        is_leaf=False,
+        depth=0,
+        leaf_colors=[BLUE, ORANGE, GREEN],
+    ):
+        if not is_leaf:
+            node_text = f"{feature}\n    <= {threshold:.3f} cm"
+            # draw decision text
+            decision_text = Text(node_text, color=WHITE)
+            # draw a box
+            bounding_box = SurroundingRectangle(decision_text, buff=0.3, color=WHITE)
+            node = VGroup()
+            node.add(bounding_box)
+            node.add(decision_text)
+            # return the node
+        else:
+            # plot the appropriate image
+            class_index = np.argmax(values)
+            # get image
+            pil_image = images[class_index]
+            leaf_group = Group()
+            node = ImageMobject(pil_image)
+            node.scale(1.5)
+            rectangle = Rectangle(
+                width=node.width + 0.05,
+                height=node.height + 0.05,
+                color=leaf_colors[class_index],
+                stroke_width=6,
+            )
+            rectangle.move_to(node.get_center())
+            rectangle.shift([-0.02, 0.02, 0])
+            leaf_group.add(rectangle)
+            leaf_group.add(node)
+            node = leaf_group
+
+        return node
+
+    def _make_connection(self, top, bottom, is_leaf=False):
+        top_node_bottom_location = top.get_center()
+        top_node_bottom_location[1] -= top.height / 2
+        bottom_node_top_location = bottom.get_center()
+        bottom_node_top_location[1] += bottom.height / 2
+        line = Line(top_node_bottom_location, bottom_node_top_location, color=WHITE)
+        return line
+
+    def _make_tree(self, tree, feature_names=["Sepal Length", "Sepal Width"]):
+        tree_group = Group()
+        max_depth = tree.max_depth
+        # make the base node
+        feature_name = feature_names[tree.feature[0]]
+        threshold = tree.threshold[0]
+        values = tree.value[0]
+        nodes_map = {}
+        root_node = self._make_node(feature_name, threshold, values, depth=0)
+        nodes_map[0] = root_node
+        tree_group.add(root_node)
+        # save some information
+        node_height = root_node.height
+        node_width = root_node.width
+        scale_factor = 1.0
+        edge_map = {}
+        # tree height
+        tree_height = scale_factor * node_height * max_depth
+        tree_width = scale_factor * 2**max_depth * node_width
+        # traverse tree
+        def recurse(node, depth, direction, parent_object, parent_node):
+            # make sure it is a valid node
+            # make the node object
+            is_leaf = tree.children_left[node] == tree.children_right[node]
+            feature_name = feature_names[tree.feature[node]]
+            threshold = tree.threshold[node]
+            values = tree.value[node]
+            node_object = self._make_node(
+                feature_name, threshold, values, depth=depth, is_leaf=is_leaf
+            )
+            nodes_map[node] = node_object
+            node_height = node_object.height
+            # set the node position
+            direction_factor = -1 if direction == "left" else 1
+            shift_right_amount = (
+                0.8 * direction_factor * scale_factor * tree_width / (2**depth) / 2
+            )
+            if is_leaf:
+                shift_down_amount = -1.0 * scale_factor * node_height
+            else:
+                shift_down_amount = -1.8 * scale_factor * node_height
+            node_object.match_x(parent_object).match_y(parent_object).shift(
+                [shift_right_amount, shift_down_amount, 0]
+            )
+            tree_group.add(node_object)
+            # make a connection
+            connection = self._make_connection(
+                parent_object, node_object, is_leaf=is_leaf
+            )
+            edge_name = str(parent_node) + "," + str(node)
+            edge_map[edge_name] = connection
+            tree_group.add(connection)
+            # recurse
+            if not is_leaf:
+                recurse(tree.children_left[node], depth + 1, "left", node_object, node)
+                recurse(
+                    tree.children_right[node], depth + 1, "right", node_object, node
+                )
+
+        recurse(tree.children_left[0], 1, "left", root_node, 0)
+        recurse(tree.children_right[0], 1, "right", root_node, 0)
+
+        tree_group.scale(0.35)
+        return tree_group, nodes_map, edge_map
+
+    def color_example_path(
+        self, tree_group, nodes_map, tree, edge_map, example, color=YELLOW, thickness=2
+    ):
+        # get decision path
+        decision_path = tree.decision_path(example)[0]
+        path_indices = decision_path.indices
+        # highlight edges
+        for node_index in range(0, len(path_indices) - 1):
+            current_val = path_indices[node_index]
+            next_val = path_indices[node_index + 1]
+            edge_str = str(current_val) + "," + str(next_val)
+            edge = edge_map[edge_str]
+            animation_two = AnimationGroup(
+                nodes_map[current_val].animate.set_color(color)
+            )
+            self.play(animation_two, run_time=0.5)
+            animation_one = AnimationGroup(
+                edge.animate.set_color(color),
+                # edge.animate.set_stroke_width(4),
+            )
+            self.play(animation_one, run_time=0.5)
+        # surround the bottom image
+        last_path_index = path_indices[-1]
+        last_path_rectangle = nodes_map[last_path_index][0]
+        self.play(last_path_rectangle.animate.set_color(color))
+
+    def create_sklearn_tree(self, max_tree_depth=1):
+        # learn the decision tree
+        iris = load_iris()
+        tree = learn_iris_decision_tree(iris, max_depth=max_tree_depth)
+        feature_names = iris.feature_names[0:2]
+        return tree.tree_
+
+    def make_tree(self, max_tree_depth=2):
+        sklearn_tree = self.create_sklearn_tree()
+        # make the tree
+        tree_group, nodes_map, edge_map = self._make_tree(
+            sklearn_tree.tree_, feature_names
+        )
+        tree_group.shift([0, 5.5, 0])
+        return tree_group
+        # self.add(tree_group)
+        # self.play(SpinInFromNothing(tree_group), run_time=3)
+        # self.color_example_path(tree_group, nodes_map, tree, edge_map, iris.data[None, 0, 0:2])
+
+
+class DecisionTreeSplitScene(Scene):
+    def make_decision_tree_classifier(self, max_depth=4):
+        decision_tree = DecisionTreeClassifier(
+            random_state=1, max_depth=max_depth, max_leaf_nodes=8
+        )
+        decision_tree = decision_tree.fit(iris.data[:, :2], iris.target)
+        # output the decisioin tree in some format
+        return decision_tree
+
+    def make_split_animation(self, data, classes, data_labels, main_axes):
+
+        """
+        def make_entropy_animation_and_plot(dim=0, num_entropy_values=50):
+            # calculate the entropy values
+            axes_group = VGroup()
+            # make axes
+            range_vals = [np.amin(data, axis=0)[dim], np.amax(data, axis=0)[dim]]
+            axes = Axes(x_range=range_vals,
+                        y_range=[0, 1.0],
+                        x_length=9,
+                        y_length=4,
+            #            axis_config={"number_scale_value":0.75, "include_numbers":True},
+                        tips=False,
+                    )
+            axes_group.add(axes)
+            # make axis labels
+            # x_label
+            x_label = Text(data_labels[dim], font=font) \
+                            .match_y(axes.get_axes()[0]) \
+                            .shift([0.5, -0.75, 0]) \
+                            .scale(font_scale*1.2)
+            axes_group.add(x_label)
+            # y_label
+            y_label = Text("Information Gain", font=font) \
+                .match_x(axes.get_axes()[1]) \
+                .shift([-0.75, 0, 0]) \
+                .rotate(np.pi / 2) \
+                .scale(font_scale * 1.2)
+
+            axes_group.add(y_label)
+            # line animation
+            information_gains = []
+            def entropy_function(split_value):
+                # lower entropy
+                lower_set = np.nonzero(data[:, dim] <= split_value)[0]
+                lower_set = classes[lower_set]
+                lower_entropy = entropy(lower_set)
+                # higher entropy
+                higher_set = np.nonzero(data[:, dim] > split_value)[0]
+                higher_set = classes[higher_set]
+                higher_entropy = entropy(higher_set)
+                # calculate entropies
+                all_entropy = entropy(classes, base=2)
+                lower_entropy = entropy(lower_set, base=2)
+                higher_entropy = entropy(higher_set, base=2)
+                mean_entropy = (lower_entropy + higher_entropy) / 2
+                # calculate information gain
+                lower_prob = len(lower_set) / len(data[:, dim])
+                higher_prob = len(higher_set) / len(data[:, dim])
+                info_gain = all_entropy - (lower_prob * lower_entropy + higher_prob * higher_entropy)
+                information_gains.append((split_value, info_gain))
+                return info_gain
+
+            data_range = np.amin(data[:, dim]), np.amax(data[:, dim])
+
+            entropy_graph = axes.get_graph(
+                entropy_function, 
+            #    color=RED, 
+            #    x_range=data_range
+            )
+            axes_group.add(entropy_graph)
+            axes_group.shift([4.0, 2, 0])
+            axes_group.scale(0.5)
+            dot_animation = Dot(color=WHITE)
+            axes_group.add(dot_animation)
+            # make animations
+            animation_group = AnimationGroup(
+                Create(axes_group, run_time=2),
+                Wait(3),
+                MoveAlongPath(dot_animation, entropy_graph, run_time=20, rate_func=rate_functions.ease_in_out_quad),
+                Wait(2)
+            )
+
+            return axes_group, animation_group, information_gains
+        """
+
+        def make_split_line_animation(dim=0):
+            # make a line along one of the dims and move it up and down
+            origin_coord = [
+                np.amin(data, axis=0)[0] - 0.2,
+                np.amin(data, axis=0)[1] - 0.2,
+            ]
+            origin_point = main_axes.coords_to_point(*origin_coord)
+            top_left_coord = [origin_coord[0], np.amax(data, axis=0)[1]]
+            bottom_right_coord = [np.amax(data, axis=0)[0] - 0.2, origin_coord[1]]
+            if dim == 0:
+                other_coord = top_left_coord
+                moving_line_coord = bottom_right_coord
+            else:
+                other_coord = bottom_right_coord
+                moving_line_coord = top_left_coord
+            other_point = main_axes.coords_to_point(*other_coord)
+            moving_line_point = main_axes.coords_to_point(*moving_line_coord)
+            moving_line = Line(origin_point, other_point, color=RED)
+            movement_line = Line(origin_point, moving_line_point)
+            if dim == 0:
+                movement_line.shift([0, moving_line.height / 2, 0])
+            else:
+                movement_line.shift([moving_line.width / 2, 0, 0])
+            # move the moving line along the movement line
+            animation = MoveAlongPath(
+                moving_line,
+                movement_line,
+                run_time=20,
+                rate_func=rate_functions.ease_in_out_quad,
+            )
+            return animation, moving_line
+
+        # plot the line in white then make it invisible
+        # make an animation along the line
+        # make a
+        # axes_one_group, top_animation_group, info_gains = make_entropy_animation_and_plot(dim=0)
+        line_movement, first_moving_line = make_split_line_animation(dim=0)
+        # axes_two_group, bottom_animation_group, _ = make_entropy_animation_and_plot(dim=1)
+        second_line_movement, second_moving_line = make_split_line_animation(dim=1)
+        # axes_two_group.shift([0, -3, 0])
+        animation_group_one = AnimationGroup(
+            #    top_animation_group,
+            line_movement,
+        )
+
+        animation_group_two = AnimationGroup(
+            #    bottom_animation_group,
+            second_line_movement,
+        )
+        """
+        both_axes_group = VGroup(
+            axes_one_group,
+            axes_two_group
+        )
+        """
+
+        return (
+            animation_group_one,
+            animation_group_two,
+            first_moving_line,
+            second_moving_line,
+            None,
+            None,
+        )
+        #    both_axes_group, \
+        #    info_gains
+
+    def construct(self):
+        # make the points
+        iris_dataset_plot = IrisDatasetPlot()
+        iris_dataset_plot.all_group.scale(1.0)
+        iris_dataset_plot.all_group.shift([-3, 0.2, 0])
+        # make the entropy line graph
+        # entropy_line_graph = self.draw_entropy_line_graph()
+        # arrange the plots
+        # do animations
+        self.play(Create(iris_dataset_plot))
+        # make the decision tree classifier
+        decision_tree_classifier = self.make_decision_tree_classifier()
+        decision_tree_surface = DecisionTreeSurface(
+            decision_tree_classifier, iris.data, iris_dataset_plot.axes_group[0]
+        )
+        self.play(Create(decision_tree_surface))
+        self.wait(3)
+        self.play(Uncreate(decision_tree_surface))
+        main_axes = iris_dataset_plot.axes_group[0]
+        (
+            split_animation_one,
+            split_animation_two,
+            first_moving_line,
+            second_moving_line,
+            both_axes_group,
+            info_gains,
+        ) = self.make_split_animation(
+            iris.data[:, 0:2], iris.target, iris.feature_names, main_axes
+        )
+        self.play(split_animation_one)
+        self.wait(0.1)
+        self.play(Uncreate(first_moving_line))
+        self.wait(3)
+        self.play(split_animation_two)
+        self.wait(0.1)
+        self.play(Uncreate(second_moving_line))
+        self.wait(0.1)
+        # highlight the maximum on top
+        # sort by second key
+        """
+        highest_info_gain = sorted(info_gains, key=lambda x: x[1])[-1]
+        highest_info_gain_point = both_axes_group[0][0].coords_to_point(*highest_info_gain)
+        highlighted_peak = Dot(highest_info_gain_point, color=YELLOW)
+        # get location of highest info gain point
+        highest_info_gain_point_in_iris_graph = iris_dataset_plot.axes_group[0].coords_to_point(*[highest_info_gain[0], 0])
+        first_moving_line.start[0] = highest_info_gain_point_in_iris_graph[0]
+        first_moving_line.end[0] = highest_info_gain_point_in_iris_graph[0]
+        self.play(Create(highlighted_peak))
+        self.play(Create(first_moving_line))
+        text = Text("Highest Information Gain")
+        text.scale(0.4)
+        text.move_to(highlighted_peak)
+        text.shift([0, 0.5, 0])
+        self.play(Create(text))
+        """
+        self.wait(1)
+        # draw the basic tree
+        decision_tree_classifier = self.make_decision_tree_classifier(max_depth=1)
+        decision_tree_surface = DecisionTreeSurface(
+            decision_tree_classifier, iris.data, iris_dataset_plot.axes_group[0]
+        )
+        decision_tree_graph, _, _ = DecisionTree()._make_tree(
+            decision_tree_classifier.tree_
+        )
+        decision_tree_graph.match_y(iris_dataset_plot.axes_group)
+        decision_tree_graph.shift([4, 0, 0])
+        self.play(Create(decision_tree_surface))
+        uncreate_animation = AnimationGroup(
+            #    Uncreate(both_axes_group),
+            #    Uncreate(highlighted_peak),
+            Uncreate(second_moving_line),
+            #    Unwrite(text)
+        )
+        self.play(uncreate_animation)
+        self.wait(0.5)
+        self.play(FadeIn(decision_tree_graph))
+        # self.play(FadeIn(highlighted_peak))
+        self.wait(5)

examples/media/texts/6bb4864e46e7c499.svg

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="1920pt" height="1080pt" viewBox="0 0 1920 1080" version="1.1">
+<g id="surface6">
+</g>
+</svg>

examples/media/texts/762a31f3ee2af27b.svg

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="1920pt" height="1080pt" viewBox="0 0 1920 1080" version="1.1">
+<g id="surface1">
+</g>
+</svg>

manim_ml/decision_tree/decision_tree_surface.py

@@ -3,6 +3,7 @@ import numpy as np
 from collections import deque
 from sklearn.tree import _tree as ctree
 
+
 class AABB:
     """Axis-aligned bounding box"""
 
@@ -19,6 +20,7 @@ class AABB:
 
         return left, right
 
+
 def tree_bounds(tree, n_features=None):
     """Compute final decision rule for each node in tree"""
     if n_features is None:
@@ -34,6 +36,7 @@ def tree_bounds(tree, n_features=None):
             queue.extend([l, r])
     return aabbs
 
+
 def compute_decision_areas(tree_classifier, maxrange, x=0, y=1, n_features=None):
     """Extract decision areas.
 
@@ -70,6 +73,7 @@ def compute_decision_areas(tree_classifier, maxrange, x=0, y=1, n_features=None)
     rectangles[:, [1, 3]] = np.minimum(rectangles[:, [1, 3]], maxrange[1::2])
     return rectangles
 
+
 def plot_areas(rectangles):
     for rect in rectangles:
         color = ["b", "r"][int(rect[4])]
@@ -83,6 +87,7 @@ def plot_areas(rectangles):
         )
         plt.gca().add_artist(rp)
 
+
 def merge_overlapping_polygons(all_polygons, colors=[BLUE, GREEN, ORANGE]):
     # get all polygons of each color
     polygon_dict = {
@@ -156,6 +161,7 @@ def merge_overlapping_polygons(all_polygons, colors=[BLUE, GREEN, ORANGE]):
             return_polygons.append(polygon)
     return return_polygons
 
+
 class IrisDatasetPlot(VGroup):
     def __init__(self, iris):
         points = iris.data[:, 0:2]
@@ -269,7 +275,6 @@ class IrisDatasetPlot(VGroup):
 
 
 class DecisionTreeSurface(VGroup):
-    
     def __init__(self, tree_clf, data, axes, class_colors=[BLUE, ORANGE, GREEN]):
         # take the tree and construct the surface from it
         self.tree_clf = tree_clf
@@ -346,8 +351,8 @@ class DecisionTreeSurface(VGroup):
 
     def make_split_to_animation_map(self):
         """
-            Returns a dictionary mapping a given split 
-            node to an animation to be played
+        Returns a dictionary mapping a given split
+        node to an animation to be played
         """
         # Create an initial decision tree surface
         # Go through each split node

manim_ml/decision_tree/helpers.py

@@ -26,6 +26,7 @@ def compute_node_depths(tree):
 
     return node_depths
 
+
 def compute_level_order_traversal(tree):
     """Computes level order traversal of a sklearn tree"""
 
@@ -56,6 +57,7 @@ def compute_level_order_traversal(tree):
 
     return sorted_inds
 
+
 def compute_bfs_traversal(tree):
     """Traverses the tree in BFS order and returns the nodes in order"""
     traversal_order = []
@@ -73,10 +75,12 @@ def compute_bfs_traversal(tree):
 
     return traversal_order
 
+
 def compute_best_first_traversal(tree):
     """Traverses the tree according to the best split first order"""
     pass
 
+
 def compute_node_to_parent_mapping(tree):
     """Returns a hashmap mapping node indices to their parent indices"""
     node_to_parent = {0: -1}  # Root has no parent

manim_ml/diffusion/mcmc.py

@@ -9,6 +9,7 @@ from tqdm import tqdm
 
 from manim_ml.probability import GaussianDistribution
 
+
 def gaussian_proposal(x, sigma=0.2):
     """
     Gaussian proposal distribution.
@@ -39,7 +40,8 @@ def gaussian_proposal(x, sigma=0.2):
 
     return (x_star, qxx)
 
-class MultidimensionalGaussianPosterior():
+
+class MultidimensionalGaussianPosterior:
     """
     N-Dimensional Gaussian distribution with
 
@@ -49,8 +51,7 @@ class MultidimensionalGaussianPosterior():
     Prior on mean is U(-500, 500)
     """
 
-    def __init__(self, ndim=2, seed=12345, scale=3,
-            mu=None, var=None):
+    def __init__(self, ndim=2, seed=12345, scale=3, mu=None, var=None):
         """_summary_
 
         Parameters
@@ -71,10 +72,7 @@ class MultidimensionalGaussianPosterior():
             self.var = var
 
         if mu is None:
-            self.mu = scipy.stats.norm(
-                loc=0, 
-                scale=self.scale
-            ).rvs(ndim)
+            self.mu = scipy.stats.norm(loc=0, scale=self.scale).rvs(ndim)
         else:
             self.mu = mu
 
@@ -84,20 +82,18 @@ class MultidimensionalGaussianPosterior():
         """
 
         if np.all(x < 500) and np.all(x > -500):
-            return scipy.stats.multivariate_normal(
-                mean=self.mu, 
-                cov=self.var
-            ).logpdf(x)
+            return scipy.stats.multivariate_normal(mean=self.mu, cov=self.var).logpdf(x)
         else:
             return -1e6
 
+
 def metropolis_hastings_sampler(
-    log_prob_fn=MultidimensionalGaussianPosterior(), 
-    prop_fn=gaussian_proposal, 
-    initial_location : np.ndarray = np.array([0, 0]), 
+    log_prob_fn=MultidimensionalGaussianPosterior(),
+    prop_fn=gaussian_proposal,
+    initial_location: np.ndarray = np.array([0, 0]),
     iterations=25,
     warm_up=0,
-    ndim=2
+    ndim=2,
 ):
     """Samples using a Metropolis-Hastings sampler.
 
@@ -158,17 +154,18 @@ def metropolis_hastings_sampler(
 
     return chain, np.array([]), proposals
 
+
 class MCMCAxes(Group):
     """Container object for visualizing MCMC on a 2D axis"""
 
     def __init__(
-        self, 
-        dot_color=BLUE, 
+        self,
+        dot_color=BLUE,
         dot_radius=0.05,
         accept_line_color=GREEN,
         reject_line_color=RED,
         line_color=WHITE,
-        line_stroke_width=1
+        line_stroke_width=1,
     ):
         super().__init__()
         self.dot_color = dot_color
@@ -176,7 +173,7 @@ class MCMCAxes(Group):
         self.accept_line_color = accept_line_color
         self.reject_line_color = reject_line_color
         self.line_color = line_color
-        self.line_stroke_width=line_stroke_width
+        self.line_stroke_width = line_stroke_width
         # Make the axes
         self.axes = Axes(
             x_range=[-3, 3],
@@ -185,22 +182,16 @@ class MCMCAxes(Group):
             y_length=12,
             x_axis_config={"stroke_opacity": 0.0},
             y_axis_config={"stroke_opacity": 0.0},
-            tips=False
+            tips=False,
         )
         self.add(self.axes)
-    
+
     @override_animation(Create)
     def _create_override(self, **kwargs):
         """Overrides Create animation"""
-        return AnimationGroup(
-            Create(self.axes)
-        )
+        return AnimationGroup(Create(self.axes))
 
-    def visualize_gaussian_proposal_about_point(
-        self,
-        mean,
-        cov=None
-    ) -> AnimationGroup:
+    def visualize_gaussian_proposal_about_point(self, mean, cov=None) -> AnimationGroup:
         """Creates a Gaussian distribution about a certain point
 
         Parameters
@@ -216,21 +207,14 @@ class MCMCAxes(Group):
             animation of creating the proposal Gaussian distribution
         """
         gaussian = GaussianDistribution(
-            axes=self.axes,
-            mean=mean,
-            cov=cov,
-            dist_theme="gaussian"
+            axes=self.axes, mean=mean, cov=cov, dist_theme="gaussian"
         )
 
         create_guassian = Create(gaussian)
         return create_guassian
-        
+
     def make_transition_animation(
-        self, 
-        start_point, 
-        end_point,
-        candidate_point,
-        run_time=0.1
+        self, start_point, end_point, candidate_point, run_time=0.1
     ) -> AnimationGroup:
         """Makes an transition animation for a single point on a Markov Chain
 
@@ -255,38 +239,27 @@ class MCMCAxes(Group):
         if point_is_rejected:
             return AnimationGroup()
         else:
-            create_end_point = Create(
-                end_point
-            )
+            create_end_point = Create(end_point)
             create_line = Create(
                 Line(
                     start_point,
                     end_point,
                     color=self.line_color,
-                    stroke_width=self.line_stroke_width
+                    stroke_width=self.line_stroke_width,
                 )
             )
             return AnimationGroup(
-                create_end_point,
-                create_line,
-                lag_ratio=1.0,
-                run_time=run_time
+                create_end_point, create_line, lag_ratio=1.0, run_time=run_time
             )
 
     def show_ground_truth_gaussian(self, distribution):
-        """
-        """
+        """ """
         mean = distribution.mu
         var = np.eye(2) * distribution.var
         distribution_drawing = GaussianDistribution(
-            self.axes, 
-            mean, 
-            var,
-            dist_theme="gaussian"
+            self.axes, mean, var, dist_theme="gaussian"
         ).set_opacity(0.2)
-        return AnimationGroup(
-            Create(distribution_drawing)
-        )
+        return AnimationGroup(Create(distribution_drawing))
 
     def visualize_metropolis_hastings_chain_sampling(
         self,
@@ -295,7 +268,7 @@ class MCMCAxes(Group):
         sampling_kwargs={},
     ):
         """
-        Makes an animation for visualizing a 2D markov chain using 
+        Makes an animation for visualizing a 2D markov chain using
         metropolis hastings samplings
 
         Parameters
@@ -318,20 +291,18 @@ class MCMCAxes(Group):
         """
         # Compute the chain samples using a Metropolis Hastings Sampler
         mcmc_samples, warm_up_samples, candidate_samples = metropolis_hastings_sampler(
-            log_prob_fn=log_prob_fn,
-            prop_fn=prop_fn,
-            **sampling_kwargs
+            log_prob_fn=log_prob_fn, prop_fn=prop_fn, **sampling_kwargs
         )
         print(f"MCMC samples: {mcmc_samples}")
         print(f"Candidate samples: {candidate_samples}")
-        # Make the animation for visualizing the chain 
+        # Make the animation for visualizing the chain
         animations = []
         # Place the initial point
         current_point = mcmc_samples[0]
         current_point = Dot(
             self.axes.coords_to_point(current_point[0], current_point[1]),
             color=self.dot_color,
-            radius=self.dot_radius
+            radius=self.dot_radius,
         )
         create_initial_point = Create(current_point)
         animations.append(create_initial_point)
@@ -346,12 +317,12 @@ class MCMCAxes(Group):
             next_point = Dot(
                 self.axes.coords_to_point(next_sample[0], next_sample[1]),
                 color=self.dot_color,
-                radius=self.dot_radius
+                radius=self.dot_radius,
             )
             candidate_point = Dot(
                 self.axes.coords_to_point(candidate_sample[0], candidate_sample[1]),
                 color=self.dot_color,
-                radius=self.dot_radius
+                radius=self.dot_radius,
             )
             # Make a transition animation
             transition_animation = self.make_transition_animation(
@@ -361,9 +332,6 @@ class MCMCAxes(Group):
             # Setup for next iteration
             current_point = next_point
         # Make the final animation group
-        animation_group = AnimationGroup(
-            *animations,
-            lag_ratio=1.0
-        )
+        animation_group = AnimationGroup(*animations, lag_ratio=1.0)
 
         return animation_group

manim_ml/neural_network/activation_functions/__init__.py

@@ -1,8 +1,7 @@
 from manim_ml.neural_network.activation_functions.relu import ReLUFunction
 
-name_to_activation_function_map = {
-    "ReLU": ReLUFunction()
-}
+name_to_activation_function_map = {"ReLU": ReLUFunction}
+
 
 def get_activation_function_by_name(name):
-    return name_to_activation_function_map[name]
+    return name_to_activation_function_map[name]

manim_ml/neural_network/activation_functions/activation_function.py

@@ -4,12 +4,22 @@ import random
 
 import manim_ml.neural_network.activation_functions.relu as relu
 
+
 class ActivationFunction(ABC, VGroup):
     """Abstract parent class for defining activation functions"""
 
-    def __init__(self, function_name=None, x_range=[-1, 1], y_range=[-1, 1],
-        x_length=0.5, y_length=0.3, show_function_name=True, active_color=ORANGE, 
-        plot_color=BLUE, rectangle_color=WHITE):
+    def __init__(
+        self,
+        function_name=None,
+        x_range=[-1, 1],
+        y_range=[-1, 1],
+        x_length=0.5,
+        y_length=0.3,
+        show_function_name=True,
+        active_color=ORANGE,
+        plot_color=BLUE,
+        rectangle_color=WHITE,
+    ):
         super(VGroup, self).__init__()
         self.function_name = function_name
         self.x_range = x_range
@@ -25,7 +35,7 @@ class ActivationFunction(ABC, VGroup):
 
     def construct_activation_function(self):
         """Makes the activation function"""
-        # Make an axis 
+        # Make an axis
         self.axes = Axes(
             x_range=self.x_range,
             y_range=self.y_range,
@@ -35,17 +45,17 @@ class ActivationFunction(ABC, VGroup):
             axis_config={
                 "include_numbers": False,
                 "stroke_width": 0.5,
-                "include_ticks": False
-            }
+                "include_ticks": False,
+            },
         )
         self.add(self.axes)
-        # Surround the axis with a rounded rectangle. 
+        # Surround the axis with a rounded rectangle.
         self.surrounding_rectangle = SurroundingRectangle(
             self.axes,
             corner_radius=0.05,
             buff=0.05,
             stroke_width=2.0,
-            stroke_color=self.rectangle_color
+            stroke_color=self.rectangle_color,
         )
         self.add(self.surrounding_rectangle)
         # Plot function on axis by applying it and showing in given range
@@ -53,17 +63,15 @@ class ActivationFunction(ABC, VGroup):
             lambda x: self.apply_function(x),
             x_range=self.x_range,
             stroke_color=self.plot_color,
-            stroke_width=2.0
+            stroke_width=2.0,
         )
         self.add(self.graph)
         # Add the function name
         if self.show_function_name:
             function_name_text = Text(
-                self.function_name,
-                font_size=12,
-                font="sans-serif"
+                self.function_name, font_size=12, font="sans-serif"
             )
-            function_name_text.next_to(self.axes, UP*0.5)
+            function_name_text.next_to(self.axes, UP * 0.5)
             self.add(function_name_text)
 
     @abstractmethod
@@ -78,29 +86,21 @@ class ActivationFunction(ABC, VGroup):
         # TODO: Evaluate the function at the x_val and show a highlighted dot
         animation_group = Succession(
             AnimationGroup(
+                ApplyMethod(self.graph.set_color, self.active_color),
                 ApplyMethod(
-                    self.graph.set_color,
-                    self.active_color
+                    self.surrounding_rectangle.set_stroke_color, self.active_color
                 ),
-                ApplyMethod(
-                    self.surrounding_rectangle.set_stroke_color,
-                    self.active_color
-                ),
-                lag_ratio=0.0
+                lag_ratio=0.0,
             ),
             Wait(1),
             AnimationGroup(
+                ApplyMethod(self.graph.set_color, self.plot_color),
                 ApplyMethod(
-                    self.graph.set_color,
-                    self.plot_color
+                    self.surrounding_rectangle.set_stroke_color, self.rectangle_color
                 ),
-                ApplyMethod(
-                    self.surrounding_rectangle.set_stroke_color,
-                    self.rectangle_color
-                ), 
-                lag_ratio=0.0
+                lag_ratio=0.0,
             ),
-            lag_ratio=1.0
+            lag_ratio=1.0,
         )
 
-        return animation_group
+        return animation_group

manim_ml/neural_network/activation_functions/relu.py

@@ -1,6 +1,9 @@
 from manim import *
 
-from manim_ml.neural_network.activation_functions.activation_function import ActivationFunction
+from manim_ml.neural_network.activation_functions.activation_function import (
+    ActivationFunction,
+)
+
 
 class ReLUFunction(ActivationFunction):
     """Rectified Linear Unit Activation Function"""
@@ -12,4 +15,4 @@ class ReLUFunction(ActivationFunction):
         if x_val < 0:
             return 0
         else:
-            return x_val
+            return x_val

manim_ml/neural_network/layers/__init__.py

@@ -1,11 +1,15 @@
 from manim_ml.neural_network.layers.convolutional_2d_to_feed_forward import (
     Convolutional2DToFeedForward,
 )
-from manim_ml.neural_network.layers.convolutional_2d_to_max_pooling_2d import Convolutional2DToMaxPooling2D
+from manim_ml.neural_network.layers.convolutional_2d_to_max_pooling_2d import (
+    Convolutional2DToMaxPooling2D,
+)
 from manim_ml.neural_network.layers.image_to_convolutional_2d import (
     ImageToConvolutional2DLayer,
 )
-from manim_ml.neural_network.layers.max_pooling_2d_to_convolutional_2d import MaxPooling2DToConvolutional2D
+from manim_ml.neural_network.layers.max_pooling_2d_to_convolutional_2d import (
+    MaxPooling2DToConvolutional2D,
+)
 from .convolutional_2d_to_convolutional_2d import Convolutional2DToConvolutional2D
 from .convolutional_2d import Convolutional2DLayer
 from .feed_forward_to_vector import FeedForwardToVector

manim_ml/neural_network/layers/convolutional_2d.py

@@ -1,6 +1,8 @@
 from typing import Union
 from manim_ml.neural_network.activation_functions import get_activation_function_by_name
-from manim_ml.neural_network.activation_functions.activation_function import ActivationFunction
+from manim_ml.neural_network.activation_functions.activation_function import (
+    ActivationFunction,
+)
 import numpy as np
 from manim import *
 
@@ -10,6 +12,7 @@ from manim_ml.neural_network.layers.parent_layers import (
 )
 from manim_ml.gridded_rectangle import GriddedRectangle
 
+
 class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
     """Handles rendering a convolutional layer for a nn"""
 
@@ -33,11 +36,11 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
         self.num_feature_maps = num_feature_maps
         self.filter_color = filter_color
         if isinstance(feature_map_size, int):
-            self.feature_map_size = (feature_map_size, feature_map_size) 
+            self.feature_map_size = (feature_map_size, feature_map_size)
         else:
             self.feature_map_size = feature_map_size
         if isinstance(filter_size, int):
-            self.filter_size = (filter_size, filter_size) 
+            self.filter_size = (filter_size, filter_size)
         else:
             self.filter_size = filter_size
         self.cell_width = cell_width
@@ -50,10 +53,10 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
         self.activation_function = activation_function
 
     def construct_layer(
-        self, 
-        input_layer: 'NeuralNetworkLayer', 
-        output_layer: 'NeuralNetworkLayer', 
-        **kwargs
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs,
     ):
         # Make the feature maps
         self.feature_maps = self.construct_feature_maps()
@@ -71,7 +74,7 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
             if isinstance(self.activation_function, str):
                 activation_function = get_activation_function_by_name(
                     self.activation_function
-                )
+                )()
             else:
                 assert isinstance(self.activation_function, ActivationFunction)
                 activation_function = self.activation_function
@@ -108,14 +111,8 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
     def highlight_and_unhighlight_feature_maps(self):
         """Highlights then unhighlights feature maps"""
         return Succession(
-            ApplyMethod(
-                self.feature_maps.set_color,
-                self.pulse_color
-            ),
-            ApplyMethod(
-                self.feature_maps.set_color,
-                self.color
-            )
+            ApplyMethod(self.feature_maps.set_color, self.pulse_color),
+            ApplyMethod(self.feature_maps.set_color, self.color),
         )
 
     def make_forward_pass_animation(
@@ -146,7 +143,7 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
                 animation_group = AnimationGroup(
                     self.activation_function.make_evaluate_animation(),
                     self.highlight_and_unhighlight_feature_maps(),
-                    lag_ratio=0.0
+                    lag_ratio=0.0,
                 )
             else:
                 animation_group = AnimationGroup()
@@ -160,12 +157,19 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
     def get_center(self):
         """Overrides function for getting center
 
-        The reason for this is so that the center calculation 
-        does not include the activation function.  
+        The reason for this is so that the center calculation
+        does not include the activation function.
         """
-        print("Getting center")
         return self.feature_maps.get_center()
 
+    def get_width(self):
+        """Overrides get width function"""
+        return self.feature_maps.length_over_dim(0)
+
+    def get_height(self):
+        """Overrides get height function"""
+        return self.feature_maps.length_over_dim(1)
+
     @override_animation(Create)
     def _create_override(self, **kwargs):
         return FadeIn(self.feature_maps)

manim_ml/neural_network/layers/convolutional_2d_to_convolutional_2d.py

@@ -7,16 +7,14 @@ from manim_ml.gridded_rectangle import GriddedRectangle
 
 from manim.utils.space_ops import rotation_matrix
 
+
 def get_rotated_shift_vectors(input_layer, normalized=False):
     """Rotates the shift vectors"""
     # Make base shift vectors
     right_shift = np.array([input_layer.cell_width, 0, 0])
     down_shift = np.array([0, -input_layer.cell_width, 0])
     # Make rotation matrix
-    rot_mat = rotation_matrix(
-        ThreeDLayer.rotation_angle, 
-        ThreeDLayer.rotation_axis
-    )
+    rot_mat = rotation_matrix(ThreeDLayer.rotation_angle, ThreeDLayer.rotation_axis)
     # Rotate the vectors
     right_shift = np.dot(right_shift, rot_mat.T)
     down_shift = np.dot(down_shift, rot_mat.T)
@@ -27,6 +25,7 @@ def get_rotated_shift_vectors(input_layer, normalized=False):
 
     return right_shift, down_shift
 
+
 class Filters(VGroup):
     """Group for showing a collection of filters connecting two layers"""
 
@@ -61,8 +60,12 @@ class Filters(VGroup):
 
     def make_input_feature_map_rectangles(self):
         rectangles = []
-        rectangle_width = self.output_layer.filter_size[0] * self.output_layer.cell_width
-        rectangle_height = self.output_layer.filter_size[1] * self.output_layer.cell_width
+        rectangle_width = (
+            self.output_layer.filter_size[0] * self.output_layer.cell_width
+        )
+        rectangle_height = (
+            self.output_layer.filter_size[1] * self.output_layer.cell_width
+        )
         filter_color = self.output_layer.filter_color
 
         for index, feature_map in enumerate(self.input_layer.feature_maps):
@@ -263,8 +266,10 @@ class Filters(VGroup):
 
         return passing_flash
 
+
 class Convolutional2DToConvolutional2D(ConnectiveLayer, ThreeDLayer):
     """Feed Forward to Embedding Layer"""
+
     input_class = Convolutional2DLayer
     output_class = Convolutional2DLayer
 
@@ -301,7 +306,12 @@ class Convolutional2DToConvolutional2D(ConnectiveLayer, ThreeDLayer):
         self.show_grid_lines = show_grid_lines
         self.highlight_color = highlight_color
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs,
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def animate_filters_all_at_once(self, filters):
@@ -321,8 +331,12 @@ class Convolutional2DToConvolutional2D(ConnectiveLayer, ThreeDLayer):
         right_shift, down_shift = get_rotated_shift_vectors(self.input_layer)
         left_shift = -1 * right_shift
         # Make the animation
-        num_y_moves = int((self.feature_map_size[1] - self.filter_size[1]) / self.stride)
-        num_x_moves = int((self.feature_map_size[0] - self.filter_size[0]) / self.stride)
+        num_y_moves = int(
+            (self.feature_map_size[1] - self.filter_size[1]) / self.stride
+        )
+        num_x_moves = int(
+            (self.feature_map_size[0] - self.filter_size[0]) / self.stride
+        )
         for y_move in range(num_y_moves):
             # Go right num_x_moves
             for x_move in range(num_x_moves):
@@ -347,10 +361,7 @@ class Convolutional2DToConvolutional2D(ConnectiveLayer, ThreeDLayer):
         animations.append(FadeOut(filters))
         return Succession(*animations, lag_ratio=1.0)
 
-    def animate_filters_one_at_a_time(
-        self, 
-        highlight_active_feature_map=True
-    ):
+    def animate_filters_one_at_a_time(self, highlight_active_feature_map=True):
         """Animates each of the filters one at a time"""
         animations = []
         output_feature_maps = self.output_layer.feature_maps
@@ -418,18 +429,12 @@ class Convolutional2DToConvolutional2D(ConnectiveLayer, ThreeDLayer):
                     self.stride * num_x_moves * left_shift + self.stride * down_shift
                 )
                 # Make the animation
-                shift_animation = ApplyMethod(
-                    filters.shift, 
-                    shift_amount
-                )
+                shift_animation = ApplyMethod(filters.shift, shift_amount)
                 animations.append(shift_animation)
             # Do last row move right
             for x_move in range(num_x_moves):
                 # Shift right
-                shift_animation = ApplyMethod(
-                    filters.shift, 
-                    self.stride * right_shift
-                )
+                shift_animation = ApplyMethod(filters.shift, self.stride * right_shift)
                 # shift_animation = self.animate.shift(right_shift)
                 animations.append(shift_animation)
             # Remove the filters
@@ -440,18 +445,14 @@ class Convolutional2DToConvolutional2D(ConnectiveLayer, ThreeDLayer):
                 # Change the output feature map colors
                 change_color_animations = []
                 change_color_animations.append(
-                    ApplyMethod(
-                        feature_map.set_color, 
-                        original_feature_map_color
-                    )
+                    ApplyMethod(feature_map.set_color, original_feature_map_color)
                 )
                 # Change the input feature map colors
                 input_feature_maps = self.input_layer.feature_maps
                 for input_feature_map in input_feature_maps:
                     change_color_animations.append(
                         ApplyMethod(
-                            input_feature_map.set_color, 
-                            original_feature_map_color
+                            input_feature_map.set_color, original_feature_map_color
                         )
                     )
                 # Combine the animations

manim_ml/neural_network/layers/convolutional_2d_to_feed_forward.py

@@ -17,14 +17,15 @@ class Convolutional2DToFeedForward(ConnectiveLayer, ThreeDLayer):
         passing_flash_color=ORANGE,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.passing_flash_color = passing_flash_color
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, run_time=1.5, **kwargs):

manim_ml/neural_network/layers/convolutional_2d_to_max_pooling_2d.py

@@ -1,15 +1,19 @@
 import random
 from manim import *
 from manim_ml.gridded_rectangle import GriddedRectangle
-from manim_ml.neural_network.layers.convolutional_2d_to_convolutional_2d import get_rotated_shift_vectors
+from manim_ml.neural_network.layers.convolutional_2d_to_convolutional_2d import (
+    get_rotated_shift_vectors,
+)
 
 from manim_ml.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
 from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer, ThreeDLayer
 from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
 from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
 
+
 class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
     """Feed Forward to Embedding Layer"""
+
     input_class = Convolutional2DLayer
     output_class = MaxPooling2DLayer
 
@@ -20,27 +24,18 @@ class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
         active_color=ORANGE,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.active_color = active_color
 
     def construct_layer(
         self,
-        input_layer: 'NeuralNetworkLayer',
-        output_layer: 'NeuralNetworkLayer',
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
         **kwargs
     ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
-    def make_forward_pass_animation(
-        self, 
-        layer_args={}, 
-        run_time=1.5, 
-        **kwargs
-    ):
+    def make_forward_pass_animation(self, layer_args={}, run_time=1.5, **kwargs):
         """Forward pass animation from conv2d to max pooling"""
         cell_width = self.input_layer.cell_width
         feature_map_size = self.input_layer.feature_map_size
@@ -61,7 +56,7 @@ class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
         remove_gridded_rectangle_animations = []
 
         for feature_map_index, feature_map in enumerate(feature_maps):
-            # 1. Draw gridded rectangle with kernel_size x kernel_size 
+            # 1. Draw gridded rectangle with kernel_size x kernel_size
             #   box regions over the input feature maps.
             gridded_rectangle = GriddedRectangle(
                 color=self.active_color,
@@ -71,7 +66,7 @@ class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
                 grid_ystep=cell_width * kernel_size,
                 grid_stroke_width=grid_stroke_width,
                 grid_stroke_color=self.active_color,
-                show_grid_lines=True
+                show_grid_lines=True,
             )
             # 2. Randomly highlight one of the cells in the kernel.
             highlighted_cells = []
@@ -88,39 +83,32 @@ class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
                         height=cell_width,
                         width=cell_width,
                         stroke_width=0.0,
-                        fill_opacity=0.7
+                        fill_opacity=0.7,
                     )
                     # Move to the correct location
                     kernel_shift_vector = [
                         kernel_size * cell_width * kernel_x,
                         -1 * kernel_size * cell_width * kernel_y,
-                        0
+                        0,
                     ]
                     cell_shift_vector = [
                         (cell_index % kernel_size) * cell_width,
                         -1 * int(cell_index / kernel_size) * cell_width,
-                        0
+                        0,
                     ]
                     cell_rectangle.next_to(
                         gridded_rectangle.get_corners_dict()["top_left"],
-                        submobject_to_align=cell_rectangle.get_corners_dict()["top_left"],
-                        buff=0.0
+                        submobject_to_align=cell_rectangle.get_corners_dict()[
+                            "top_left"
+                        ],
+                        buff=0.0,
                     )
-                    cell_rectangle.shift(
-                        kernel_shift_vector
-                    )
-                    cell_rectangle.shift(
-                        cell_shift_vector
-                    )
-                    highlighted_cells.append(
-                        cell_rectangle
-                    )
-            # Rotate the gridded rectangles so they match the angle 
+                    cell_rectangle.shift(kernel_shift_vector)
+                    cell_rectangle.shift(cell_shift_vector)
+                    highlighted_cells.append(cell_rectangle)
+            # Rotate the gridded rectangles so they match the angle
             # of the conv maps
-            gridded_rectangle_group = VGroup(
-                gridded_rectangle,
-                *highlighted_cells
-            )
+            gridded_rectangle_group = VGroup(gridded_rectangle, *highlighted_cells)
             gridded_rectangle_group.rotate(
                 ThreeDLayer.rotation_angle,
                 about_point=gridded_rectangle.get_center(),
@@ -129,9 +117,9 @@ class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
             gridded_rectangle.next_to(
                 feature_map.get_corners_dict()["top_left"],
                 submobject_to_align=gridded_rectangle.get_corners_dict()["top_left"],
-                buff=0.0
+                buff=0.0,
             )
-            # 3. Make a create gridded rectangle 
+            # 3. Make a create gridded rectangle
             """
             create_rectangle = Create(
                 gridded_rectangle
@@ -185,31 +173,24 @@ class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
             create_and_remove_cell_animations = Succession(
                 Create(VGroup(*highlighted_cells)),
                 Wait(0.5),
-                Uncreate(VGroup(*highlighted_cells))
+                Uncreate(VGroup(*highlighted_cells)),
             )
             return create_and_remove_cell_animations
-            # 5. Move and resize the gridded rectangle to the output 
-            #   feature maps. 
+            # 5. Move and resize the gridded rectangle to the output
+            #   feature maps.
             resize_rectangle = Transform(
-                gridded_rectangle,
-                self.output_layer.feature_maps[feature_map_index]
+                gridded_rectangle, self.output_layer.feature_maps[feature_map_index]
             )
             move_rectangle = gridded_rectangle.animate.move_to(
                 self.output_layer.feature_maps[feature_map_index]
             )
             move_and_resize = Succession(
-                resize_rectangle,
-                move_rectangle,
-                lag_ratio=0.0
+                resize_rectangle, move_rectangle, lag_ratio=0.0
             )
-            move_and_resize_gridded_rectangle_animations.append(
-                move_and_resize
-            )
-            # 6. Make the gridded feature map(s) disappear. 
+            move_and_resize_gridded_rectangle_animations.append(move_and_resize)
+            # 6. Make the gridded feature map(s) disappear.
             remove_gridded_rectangle_animations.append(
-                Uncreate(
-                    gridded_rectangle_group
-                )
+                Uncreate(gridded_rectangle_group)
             )
 
             """
@@ -224,5 +205,5 @@ class Convolutional2DToMaxPooling2D(ConnectiveLayer, ThreeDLayer):
             #     *remove_gridded_rectangle_animations
             # ),
             # lag_ratio=1.0
-            lag_ratio=1.0
-        )
+            lag_ratio=1.0,
+        )

manim_ml/neural_network/layers/embedding.py

@@ -25,9 +25,9 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
         self.paired_query_mode = paired_query_mode
 
     def construct_layer(
-        self, 
-        input_layer: 'NeuralNetworkLayer', 
-        output_layer: 'NeuralNetworkLayer', 
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
         **kwargs
     ):
         self.axes = Axes(
@@ -43,14 +43,13 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
         self.axes.move_to(self.get_center())
         # Make point cloud
         self.point_cloud = self.construct_gaussian_point_cloud(
-            self.mean, 
-            self.covariance
+            self.mean, self.covariance
         )
         self.add(self.point_cloud)
         # Make latent distribution
         self.latent_distribution = GaussianDistribution(
             self.axes, mean=self.mean, cov=self.covariance
-        ) # Use defaults
+        )  # Use defaults
 
     def add_gaussian_distribution(self, gaussian_distribution):
         """Adds given GaussianDistribution to the list"""

manim_ml/neural_network/layers/embedding_to_feed_forward.py

@@ -3,6 +3,7 @@ from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
 from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
 from manim_ml.neural_network.layers.embedding import EmbeddingLayer
 
+
 class EmbeddingToFeedForward(ConnectiveLayer):
     """Feed Forward to Embedding Layer"""
 
@@ -17,17 +18,18 @@ class EmbeddingToFeedForward(ConnectiveLayer):
         dot_radius=0.03,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.feed_forward_layer = output_layer
         self.embedding_layer = input_layer
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, run_time=1.5, **kwargs):

manim_ml/neural_network/layers/feed_forward.py

@@ -35,7 +35,12 @@ class FeedForwardLayer(VGroupNeuralNetworkLayer):
 
         self.node_group = VGroup()
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         """Creates the neural network layer"""
         # Add Nodes
         for node_number in range(self.num_nodes):

manim_ml/neural_network/layers/feed_forward_to_embedding.py

@@ -18,17 +18,18 @@ class FeedForwardToEmbedding(ConnectiveLayer):
         dot_radius=0.03,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.feed_forward_layer = input_layer
         self.embedding_layer = output_layer
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, run_time=1.5, **kwargs):

manim_ml/neural_network/layers/feed_forward_to_feed_forward.py

@@ -5,6 +5,7 @@ from manim import *
 from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
 from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
 
+
 class FeedForwardToFeedForward(ConnectiveLayer):
     """Layer for connecting FeedForward layer to FeedForwardLayer"""
 
@@ -23,18 +24,19 @@ class FeedForwardToFeedForward(ConnectiveLayer):
         camera=None,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.passing_flash = passing_flash
         self.edge_color = edge_color
         self.dot_radius = dot_radius
         self.animation_dot_color = animation_dot_color
         self.edge_width = edge_width
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         self.edges = self.construct_edges()
         self.add(self.edges)
 

manim_ml/neural_network/layers/feed_forward_to_image.py

@@ -18,18 +18,19 @@ class FeedForwardToImage(ConnectiveLayer):
         dot_radius=0.05,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
         self.feed_forward_layer = input_layer
         self.image_layer = output_layer
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, **kwargs):

manim_ml/neural_network/layers/feed_forward_to_vector.py

@@ -18,18 +18,19 @@ class FeedForwardToVector(ConnectiveLayer):
         dot_radius=0.05,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
         self.feed_forward_layer = input_layer
         self.vector_layer = output_layer
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, **kwargs):

manim_ml/neural_network/layers/image.py

@@ -5,6 +5,7 @@ from manim_ml.neural_network.layers.parent_layers import NeuralNetworkLayer
 
 from PIL import Image
 
+
 class ImageLayer(NeuralNetworkLayer):
     """Single Image Layer for Neural Network"""
 
@@ -19,23 +20,22 @@ class ImageLayer(NeuralNetworkLayer):
 
         Parameters
         ----------
-        input_layer : 
+        input_layer :
             Input layer
-        output_layer : 
+        output_layer :
             Output layer
         """
         if len(np.shape(self.numpy_image)) == 2:
             # Assumed Grayscale
             self.num_channels = 1
             self.image_mobject = GrayscaleImageMobject(
-                self.numpy_image, 
-                height=self.image_height
+                self.numpy_image, height=self.image_height
             )
         elif len(np.shape(self.numpy_image)) == 3:
             # Assumed RGB
             self.num_channels = 3
             self.image_mobject = ImageMobject(self.numpy_image).scale_to_fit_height(
-                height
+                self.image_height
             )
         self.add(self.image_mobject)
 
@@ -63,10 +63,6 @@ class ImageLayer(NeuralNetworkLayer):
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
         return AnimationGroup()
 
-    # def move_to(self, location):
-    #     """Override of move to"""
-    #     self.image_mobject.move_to(location)
-
     def get_right(self):
         """Override get right"""
         return self.image_mobject.get_right()

manim_ml/neural_network/layers/image_to_convolutional_2d.py

@@ -9,6 +9,7 @@ from manim_ml.neural_network.layers.parent_layers import (
 )
 from manim_ml.gridded_rectangle import GriddedRectangle
 
+
 class ImageToConvolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
     """Handles rendering a convolutional layer for a nn"""
 
@@ -16,16 +17,18 @@ class ImageToConvolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
     output_class = Convolutional2DLayer
 
     def __init__(
-        self, 
-        input_layer: ImageLayer, 
-        output_layer: Convolutional2DLayer, 
-        **kwargs
+        self, input_layer: ImageLayer, output_layer: Convolutional2DLayer, **kwargs
     ):
         super().__init__(input_layer, output_layer, **kwargs)
         self.input_layer = input_layer
         self.output_layer = output_layer
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs,
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, run_time=5, layer_args={}, **kwargs):

manim_ml/neural_network/layers/image_to_feed_forward.py

@@ -18,18 +18,19 @@ class ImageToFeedForward(ConnectiveLayer):
         dot_radius=0.05,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
         self.feed_forward_layer = output_layer
         self.image_layer = input_layer
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, **kwargs):

manim_ml/neural_network/layers/max_pooling_2d.py

@@ -1,27 +1,31 @@
 from manim import *
 from manim_ml.gridded_rectangle import GriddedRectangle
 
-from manim_ml.neural_network.layers.parent_layers import ThreeDLayer, VGroupNeuralNetworkLayer
+from manim_ml.neural_network.layers.parent_layers import (
+    ThreeDLayer,
+    VGroupNeuralNetworkLayer,
+)
+
 
 class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
     """Max pooling layer for Convolutional2DLayer
 
     Note: This is for a Convolutional2DLayer even though
     it is called MaxPooling2DLayer because the 2D corresponds
-    to the 2 spatial dimensions of the convolution. 
+    to the 2 spatial dimensions of the convolution.
     """
 
     def __init__(
-            self, 
-            kernel_size=2, 
-            stride=1, 
-            cell_highlight_color=ORANGE,
-            cell_width=0.2,
-            filter_spacing=0.1,
-            color=BLUE,
-            show_grid_lines=False,
-            stroke_width=2.0,
-            **kwargs
+        self,
+        kernel_size=2,
+        stride=1,
+        cell_highlight_color=ORANGE,
+        cell_width=0.2,
+        filter_spacing=0.1,
+        color=BLUE,
+        show_grid_lines=False,
+        stroke_width=2.0,
+        **kwargs
     ):
         """Layer object for animating 2D Convolution Max Pooling
 
@@ -42,11 +46,15 @@ class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
         self.show_grid_lines = show_grid_lines
         self.stroke_width = stroke_width
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         # Make the output feature maps
         self.feature_maps = self._make_output_feature_maps(
-            input_layer.num_feature_maps,
-            input_layer.feature_map_size
+            input_layer.num_feature_maps, input_layer.feature_map_size
         )
         self.add(self.feature_maps)
         self.rotate(
@@ -58,17 +66,13 @@ class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
             input_layer.feature_map_size[0] / self.kernel_size,
             input_layer.feature_map_size[1] / self.kernel_size,
         )
-    
-    def _make_output_feature_maps(
-        self, 
-        num_input_feature_maps, 
-        input_feature_map_size
-    ):
+
+    def _make_output_feature_maps(self, num_input_feature_maps, input_feature_map_size):
         """Makes a set of output feature maps"""
-        # Compute the size of the feature maps 
+        # Compute the size of the feature maps
         output_feature_map_size = (
             input_feature_map_size[0] / self.kernel_size,
-            input_feature_map_size[1] / self.kernel_size
+            input_feature_map_size[1] / self.kernel_size,
         )
         # Draw rectangles that are filled in with opacity
         feature_maps = []
@@ -92,12 +96,10 @@ class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
             # rectangle.set_z_index(4)
             feature_maps.append(rectangle)
 
-        return VGroup(
-            *feature_maps
-        )
+        return VGroup(*feature_maps)
 
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
-        """Makes forward pass of Max Pooling Layer. 
+        """Makes forward pass of Max Pooling Layer.
 
         Parameters
         ----------

manim_ml/neural_network/layers/max_pooling_2d_to_convolutional_2d.py

@@ -1,7 +1,10 @@
 import numpy as np
 from manim import *
 
-from manim_ml.neural_network.layers.convolutional_2d_to_convolutional_2d import Convolutional2DToConvolutional2D, Filters
+from manim_ml.neural_network.layers.convolutional_2d_to_convolutional_2d import (
+    Convolutional2DToConvolutional2D,
+    Filters,
+)
 from manim_ml.neural_network.layers.max_pooling_2d import MaxPooling2DLayer
 from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer, ThreeDLayer
 from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
@@ -9,8 +12,10 @@ from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
 
 from manim.utils.space_ops import rotation_matrix
 
+
 class MaxPooling2DToConvolutional2D(Convolutional2DToConvolutional2D):
     """Feed Forward to Embedding Layer"""
+
     input_class = MaxPooling2DLayer
     output_class = Convolutional2DLayer
 
@@ -25,20 +30,16 @@ class MaxPooling2DToConvolutional2D(Convolutional2DToConvolutional2D):
         **kwargs
     ):
         input_layer.num_feature_maps = output_layer.num_feature_maps
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.passing_flash_color = passing_flash_color
         self.cell_width = cell_width
         self.stroke_width = stroke_width
         self.show_grid_lines = show_grid_lines
 
     def construct_layer(
-        self, 
-        input_layer: 'NeuralNetworkLayer', 
-        output_layer: 'NeuralNetworkLayer', 
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
         **kwargs
     ):
         """Constructs the MaxPooling to Convolution3D layer

manim_ml/neural_network/layers/paired_query.py

@@ -22,7 +22,12 @@ class PairedQueryLayer(NeuralNetworkLayer):
         self.add(self.assets)
         self.add(self.title)
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     @classmethod

manim_ml/neural_network/layers/paired_query_to_feed_forward.py

@@ -18,18 +18,19 @@ class PairedQueryToFeedForward(ConnectiveLayer):
         dot_radius=0.02,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
         self.paired_query_layer = input_layer
         self.feed_forward_layer = output_layer
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, **kwargs):

manim_ml/neural_network/layers/parent_layers.py

@@ -1,6 +1,7 @@
 from manim import *
 from abc import ABC, abstractmethod
 
+
 class NeuralNetworkLayer(ABC, Group):
     """Abstract Neural Network Layer class"""
 
@@ -12,8 +13,12 @@ class NeuralNetworkLayer(ABC, Group):
         # self.add(self.title)
 
     @abstractmethod
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', 
-            output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs,
+    ):
         """Constructs the layer at network construction time
 
         Parameters
@@ -36,6 +41,7 @@ class NeuralNetworkLayer(ABC, Group):
     def __repr__(self):
         return f"{type(self).__name__}"
 
+
 class VGroupNeuralNetworkLayer(NeuralNetworkLayer):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -49,6 +55,7 @@ class VGroupNeuralNetworkLayer(NeuralNetworkLayer):
     def _create_override(self):
         return super()._create_override()
 
+
 class ThreeDLayer(ABC):
     """Abstract class for 3D layers"""
 
@@ -58,6 +65,7 @@ class ThreeDLayer(ABC):
     rotation_angle = 60 * DEGREES
     rotation_axis = [0.0, 0.9, 0.0]
 
+
 class ConnectiveLayer(VGroupNeuralNetworkLayer):
     """Forward pass animation for a given pair of layers"""
 
@@ -86,6 +94,7 @@ class ConnectiveLayer(VGroupNeuralNetworkLayer):
             + ")"
         )
 
+
 class BlankConnective(ConnectiveLayer):
     """Connective layer to be used when the given pair of layers is undefined"""
 
@@ -97,4 +106,4 @@ class BlankConnective(ConnectiveLayer):
 
     @override_animation(Create)
     def _create_override(self):
-        return super()._create_override()
+        return super()._create_override()

manim_ml/neural_network/layers/triplet.py

@@ -26,7 +26,12 @@ class TripletLayer(NeuralNetworkLayer):
         self.stroke_width = stroke_width
         self.font_size = font_size
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         # Make the assets
         self.assets = self.make_assets()
         self.add(self.assets)

manim_ml/neural_network/layers/triplet_to_feed_forward.py

@@ -18,18 +18,19 @@ class TripletToFeedForward(ConnectiveLayer):
         dot_radius=0.02,
         **kwargs
     ):
-        super().__init__(
-            input_layer,
-            output_layer,
-            **kwargs
-        )
+        super().__init__(input_layer, output_layer, **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
         self.feed_forward_layer = output_layer
         self.triplet_layer = input_layer
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs
+    ):
         return super().construct_layer(input_layer, output_layer, **kwargs)
 
     def make_forward_pass_animation(self, layer_args={}, **kwargs):

manim_ml/neural_network/layers/util.py

@@ -4,9 +4,10 @@ from manim import *
 from manim_ml.neural_network.layers.parent_layers import BlankConnective, ThreeDLayer
 from manim_ml.neural_network.layers import connective_layers_list
 
+
 def get_connective_layer(input_layer, output_layer):
     """
-        Deduces the relevant connective layer
+    Deduces the relevant connective layer
     """
     connective_layer_class = None
     for candidate_class in connective_layers_list:

manim_ml/neural_network/layers/vector.py

@@ -12,7 +12,12 @@ class VectorLayer(VGroupNeuralNetworkLayer):
         self.num_values = num_values
         self.value_func = value_func
 
-    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+    def construct_layer(
+        self,
+        input_layer: "NeuralNetworkLayer",
+        output_layer: "NeuralNetworkLayer",
+        **kwargs,
+    ):
         # Make the vector
         self.vector_label = self.make_vector()
         self.add(self.vector_label)

manim_ml/neural_network/neural_network.py

@@ -22,6 +22,7 @@ from manim_ml.neural_network.neural_network_transformations import (
     RemoveLayer,
 )
 
+
 class NeuralNetwork(Group):
     """Neural Network Visualization Container Class"""
 
@@ -53,17 +54,11 @@ class NeuralNetwork(Group):
         # Construct all of the layers
         self._construct_input_layers()
         # Place the layers
-        self._place_layers(
-            layout=layout, 
-            layout_direction=layout_direction
-        )
+        self._place_layers(layout=layout, layout_direction=layout_direction)
         # Make the connective layers
         self.connective_layers, self.all_layers = self._construct_connective_layers()
         # Make overhead title
-        self.title = Text(
-            self.title_text, 
-            font_size=DEFAULT_FONT_SIZE / 2
-        )
+        self.title = Text(self.title_text, font_size=DEFAULT_FONT_SIZE / 2)
         self.title.next_to(self, UP, 1.0)
         self.add(self.title)
         # Place layers at correct z index
@@ -76,7 +71,7 @@ class NeuralNetwork(Group):
         print(repr(self))
 
     def _construct_input_layers(self):
-        """Constructs each of the input layers in context 
+        """Constructs each of the input layers in context
         of their adjacent layers"""
         prev_layer = None
         next_layer = None
@@ -105,64 +100,82 @@ class NeuralNetwork(Group):
                 previous_layer, EmbeddingLayer
             ):
                 if layout_direction == "left_to_right":
-                    shift_vector = np.array([
-                        (
-                            previous_layer.get_width() / 2
-                            + current_layer.get_width() / 2
-                            - 0.2
-                        ),
-                        0,
-                        0,
-                    ])
+                    shift_vector = np.array(
+                        [
+                            (
+                                previous_layer.get_width() / 2
+                                + current_layer.get_width() / 2
+                                - 0.2
+                            ),
+                            0,
+                            0,
+                        ]
+                    )
                 elif layout_direction == "top_to_bottom":
-                    shift_vector = np.array([
-                        0,
-                        -(
-                            previous_layer.get_width() / 2
-                            + current_layer.get_width() / 2
-                            - 0.2
-                        ),
-                        0,
-                    ])
+                    shift_vector = np.array(
+                        [
+                            0,
+                            -(
+                                previous_layer.get_width() / 2
+                                + current_layer.get_width() / 2
+                                - 0.2
+                            ),
+                            0,
+                        ]
+                    )
                 else:
                     raise Exception(
                         f"Unrecognized layout direction: {layout_direction}"
                     )
             else:
                 if layout_direction == "left_to_right":
-                    shift_vector = np.array([
-                        (
+                    shift_vector = np.array(
+                        [
                             previous_layer.get_width() / 2
                             + current_layer.get_width() / 2
-                        )
-                        + self.layer_spacing,
-                        0,
-                        0,
-                    ])
+                            + self.layer_spacing,
+                            0,
+                            0,
+                        ]
+                    )
                 elif layout_direction == "top_to_bottom":
-                    shift_vector = np.array([
-                        0,
-                        -(
-                            (
-                                previous_layer.get_width() / 2
-                                + current_layer.get_width() / 2
-                            )
-                            + self.layer_spacing
-                        ),
-                        0,
-                    ])
+                    shift_vector = np.array(
+                        [
+                            0,
+                            -(
+                                (
+                                    previous_layer.get_width() / 2
+                                    + current_layer.get_width() / 2
+                                )
+                                + self.layer_spacing
+                            ),
+                            0,
+                        ]
+                    )
                 else:
                     raise Exception(
                         f"Unrecognized layout direction: {layout_direction}"
                     )
             current_layer.shift(shift_vector)
+
+        # After all layers have been placed place their activation functions
+        for current_layer in self.input_layers:
             # Place activation function
             if hasattr(current_layer, "activation_function"):
                 if not current_layer.activation_function is None:
-                    current_layer.activation_function.next_to(
-                        current_layer, 
-                        direction=UP
+                    up_movement = np.array(
+                        [
+                            0,
+                            current_layer.get_height() / 2
+                            + current_layer.activation_function.get_height() / 2
+                            + 0.5 * self.layer_spacing,
+                            0,
+                        ]
                     )
+                    current_layer.activation_function.move_to(
+                        current_layer,
+                    )
+                    current_layer.activation_function.shift(up_movement)
                     self.add(current_layer.activation_function)
 
     def _construct_connective_layers(self):
@@ -228,8 +241,8 @@ class NeuralNetwork(Group):
             # Get the layer args
             if isinstance(layer, ConnectiveLayer):
                 """
-                    NOTE: By default a connective layer will get the combined
-                    layer_args of the layers it is connecting and itself.
+                NOTE: By default a connective layer will get the combined
+                layer_args of the layers it is connecting and itself.
                 """
                 before_layer_args = {}
                 current_layer_args = {}
@@ -252,16 +265,11 @@ class NeuralNetwork(Group):
                     current_layer_args = layer_args[layer]
             # Perform the forward pass of the current layer
             layer_forward_pass = layer.make_forward_pass_animation(
-                layer_args=current_layer_args, 
-                run_time=per_layer_runtime, 
-                **kwargs
+                layer_args=current_layer_args, run_time=per_layer_runtime, **kwargs
             )
             all_animations.append(layer_forward_pass)
         # Make the animation group
-        animation_group = Succession(
-            *all_animations, 
-            lag_ratio=1.0
-        )
+        animation_group = Succession(*all_animations, lag_ratio=1.0)
 
         return animation_group
 
@@ -332,6 +340,7 @@ class NeuralNetwork(Group):
         string_repr = "NeuralNetwork([\n" + inner_string + "])"
         return string_repr
 
+
 class FeedForwardNeuralNetwork(NeuralNetwork):
     """NeuralNetwork with just feed forward layers"""
 

manim_ml/probability.py

@@ -2,6 +2,7 @@ from manim import *
 import numpy as np
 import math
 
+
 class GaussianDistribution(VGroup):
     """Object for drawing a Gaussian distribution"""