commit ea63ef5ec0d921df18812885f543e257fd10cb8d
Author: Alec Helbling <alechelbling@gatech.edu>
Date:   Mon Jan 31 01:27:38 2022 -0500

    Created neural network visualization and feed forward animation

diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..3792cee
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,6 @@
+video:
+	manim -pqh src/autoencoder.py Autoencoder
+checkstyle:
+	pycodestyle src
+	pydocstyle src
+#docs:	
diff --git a/src/neural_network.py b/src/neural_network.py
new file mode 100644
index 0000000..b501598
--- /dev/null
+++ b/src/neural_network.py
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+"""Neural Network Manim Visualization
+
+This module is responsible for generating a neural network visualization with
+manim, specifically a fully connected neural network diagram. 
+
+Example:
+    # Specify how many nodes are in each node layer
+    layer_node_count = [5, 3, 5] 
+    # Create the object with default style settings
+    NeuralNetwork(layer_node_count)
+"""
+from manim import *
+
+class NeuralNetworkLayer(VGroup):
+    """Handles rendering a layer for a neural network"""
+
+    def __init__(
+            self, num_nodes, layer_width=0.3, node_radius=0.2, 
+            node_color=BLUE, node_outline_color=WHITE, rectangle_color=WHITE,
+            node_spacing=0.6, rectangle_fill_color=BLACK):
+        super(VGroup, self).__init__()
+        self.num_nodes = num_nodes
+        self.layer_width = layer_width
+        self.node_radius = node_radius
+        self.node_color = node_color
+        self.node_outline_color = node_outline_color
+        self.rectangle_color = rectangle_color
+        self.node_spacing = node_spacing
+        self.rectangle_fill_color = rectangle_fill_color
+
+        self.node_group = VGroup()
+
+        self._construct_neural_network_layer()
+
+    def _construct_neural_network_layer(self):
+        """Creates the neural network layer"""
+        # Add Nodes
+        for node_number in range(self.num_nodes):
+            node_object = Circle(radius=self.node_radius, color=self.node_color)
+            self.node_group.add(node_object)
+        # Space the nodes
+        # Assumes Vertical orientation
+        for node_index, node_object in enumerate(self.node_group):
+            location = node_index * self.node_spacing
+            node_object.move_to([0, location, 0])
+        # Create Surrounding Rectangle
+        surrounding_rectangle = SurroundingRectangle(
+            self.node_group, color=self.rectangle_color, fill_color=self.rectangle_fill_color, fill_opacity=1.0)
+        # Add the objects to the class
+        self.add(surrounding_rectangle, self.node_group)
+
+class NeuralNetwork(VGroup):
+
+    def __init__(
+            self, layer_node_count, layer_width=1.0, node_radius=1.0, 
+            node_color=BLUE, edge_color=WHITE, layer_spacing=1.5,
+            animation_dot_color=ORANGE):
+        super(VGroup, self).__init__()
+        self.layer_node_count = layer_node_count
+        self.layer_width = layer_width
+        self.node_radius = node_radius
+        self.node_color = node_color
+        self.edge_color = edge_color
+        self.layer_spacing = layer_spacing
+        self.animation_dot_color = animation_dot_color
+        
+        self.layers = self._construct_layers()
+        self.edge_layers = self._construct_edges()
+
+        self.add(self.edge_layers)
+        self.add(self.layers)
+
+    def _construct_layers(self):
+        """Creates the neural network"""
+        layers = VGroup()
+        # Create each layer
+        for layer_index, node_count in enumerate(self.layer_node_count):
+            layer = NeuralNetworkLayer(node_count)
+            # Manage spacing
+            layer.move_to([self.layer_spacing * layer_index, 0, 0])
+            # Add layer to VGroup
+            layers.add(layer)
+        # Create the connecting edges
+        layers.z_index = 1
+        return layers
+
+    def _construct_edges(self):
+        """Draws connecting lines between layers"""
+        edge_layers = VGroup()
+        for layer_index in range(len(self.layer_node_count) - 1):
+            current_layer = self.layers[layer_index]
+            next_layer = self.layers[layer_index + 1] 
+            edge_layer = VGroup()
+            # Go through each node in the two layers and make a connecting line
+            for node_i in current_layer.node_group:
+                for node_j in next_layer.node_group:
+                    line = Line(node_i.get_center(), node_j.get_center(), color=self.edge_color)
+                    edge_layer.add(line)
+            edge_layers.add(edge_layer)
+        edge_layers.z_index = 1
+        return edge_layers
+
+    def make_forward_propagation_animation(self, run_time=4):
+        """Generates an animation for feed forward propogation"""
+        all_animations = []
+        per_layer_run_time = run_time / len(self.edge_layers)
+        for edge_layer in self.edge_layers:
+            path_animations = []
+            for edge in edge_layer:
+                dot = Dot(color=self.animation_dot_color)
+                dot.z_index = 0
+                anim = MoveAlongPath(dot, edge, run_time=per_layer_run_time, rate_function=linear)
+                path_animations.append(anim)
+            path_animation_group = AnimationGroup(*path_animations)
+            all_animations.append(path_animation_group)
+
+        animation_group = AnimationGroup(*all_animations, lag_ratio=1)
+
+        return animation_group
+
+class TestNeuralNetworkScene(Scene):
+    """Test Scene for the Neural Network"""
+    def construct(self):
+        # Make the Layer object
+        num_nodes = [5, 3, 5]
+        nn = NeuralNetwork(num_nodes)
+        nn.move_to(ORIGIN)
+        # Make Animation
+        self.add(nn)
+        forward_propagation_animation = nn.make_forward_propagation_animation()
+        self.play(forward_propagation_animation)