Files

123 lines
4.3 KiB
Python

from manim import *
from manim_ml.neural_network.layers.parent_layers import VGroupNeuralNetworkLayer
class FeedForwardLayer(VGroupNeuralNetworkLayer):
"""Handles rendering a layer for a neural network"""
def __init__(
self,
num_nodes,
layer_buffer=SMALL_BUFF / 2,
node_radius=0.08,
node_color=BLUE,
node_outline_color=WHITE,
rectangle_color=WHITE,
node_spacing=0.3,
rectangle_fill_color=BLACK,
node_stroke_width=2.0,
rectangle_stroke_width=2.0,
animation_dot_color=RED,
**kwargs
):
super(VGroupNeuralNetworkLayer, self).__init__(**kwargs)
self.num_nodes = num_nodes
self.layer_buffer = layer_buffer
self.node_radius = node_radius
self.node_color = node_color
self.node_stroke_width = node_stroke_width
self.node_outline_color = node_outline_color
self.rectangle_stroke_width = rectangle_stroke_width
self.rectangle_color = rectangle_color
self.node_spacing = node_spacing
self.rectangle_fill_color = rectangle_fill_color
self.animation_dot_color = animation_dot_color
self.node_group = VGroup()
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):
node_object = Circle(
radius=self.node_radius,
color=self.node_color,
stroke_width=self.node_stroke_width,
)
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
self.surrounding_rectangle = SurroundingRectangle(
self.node_group,
color=self.rectangle_color,
fill_color=self.rectangle_fill_color,
fill_opacity=1.0,
buff=self.layer_buffer,
stroke_width=self.rectangle_stroke_width,
)
self.surrounding_rectangle.set_z_index(1)
# Add the objects to the class
self.add(self.surrounding_rectangle, self.node_group)
def make_dropout_forward_pass_animation(self, layer_args, **kwargs):
"""Makes a forward pass animation with dropout"""
# Make sure proper dropout information was passed
assert "dropout_node_indices" in layer_args
dropout_node_indices = layer_args["dropout_node_indices"]
# Only highlight nodes that were note dropped out
nodes_to_highlight = []
for index, node in enumerate(self.node_group):
if not index in dropout_node_indices:
nodes_to_highlight.append(node)
nodes_to_highlight = VGroup(*nodes_to_highlight)
# Make highlight animation
succession = Succession(
ApplyMethod(
nodes_to_highlight.set_color, self.animation_dot_color, run_time=0.25
),
Wait(1.0),
ApplyMethod(nodes_to_highlight.set_color, self.node_color, run_time=0.25),
)
return succession
def make_forward_pass_animation(self, layer_args={}, **kwargs):
# Check if dropout is a thing
if "dropout_node_indices" in layer_args:
# Drop out certain nodes
return self.make_dropout_forward_pass_animation(
layer_args=layer_args, **kwargs
)
else:
# Make highlight animation
succession = Succession(
ApplyMethod(
self.node_group.set_color, self.animation_dot_color, run_time=0.25
),
Wait(1.0),
ApplyMethod(self.node_group.set_color, self.node_color, run_time=0.25),
)
return succession
@override_animation(Create)
def _create_override(self, **kwargs):
animations = []
animations.append(Create(self.surrounding_rectangle))
for node in self.node_group:
animations.append(Create(node))
animation_group = AnimationGroup(*animations, lag_ratio=0.0)
return animation_group