"""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 cv2 import AGAST_FEATURE_DETECTOR_NONMAX_SUPPRESSION from manim import * import warnings import textwrap 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.util import get_connective_layer from manim_ml.list_group import ListGroup from manim_ml.neural_network.neural_network_transformations import InsertLayer, RemoveLayer class NeuralNetwork(Group): def __init__(self, input_layers, edge_color=WHITE, layer_spacing=0.2, animation_dot_color=RED, edge_width=2.5, dot_radius=0.03, title=" "): super(Group, self).__init__() self.input_layers = ListGroup(*input_layers) self.edge_width = edge_width self.edge_color = edge_color self.layer_spacing = layer_spacing self.animation_dot_color = animation_dot_color self.dot_radius = dot_radius self.title_text = title self.created = False # TODO take layer_node_count [0, (1, 2), 0] # and make it have explicit distinct subspaces self._place_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.next_to(self, UP, 1.0) self.add(self.title) # Place layers at correct z index self.connective_layers.set_z_index(2) self.input_layers.set_z_index(3) # Center the whole diagram by default self.all_layers.move_to(ORIGIN) self.add(self.all_layers) # Print neural network print(repr(self)) def _place_layers(self): """Creates the neural network""" # TODO implement more sophisticated custom layouts # Default: Linear layout for layer_index in range(1, len(self.input_layers)): previous_layer = self.input_layers[layer_index - 1] current_layer = self.input_layers[layer_index] current_layer.move_to(previous_layer) shift_vector = np.array([(previous_layer.get_width()/2 + current_layer.get_width()/2) + self.layer_spacing, 0, 0]) current_layer.shift(shift_vector) def _construct_connective_layers(self): """Draws connecting lines between layers""" connective_layers = ListGroup() all_layers = ListGroup() for layer_index in range(len(self.input_layers) - 1): current_layer = self.input_layers[layer_index] all_layers.add(current_layer) next_layer = self.input_layers[layer_index + 1] # Check if layer is actually a nested NeuralNetwork if isinstance(current_layer, NeuralNetwork): # Last layer of the current layer current_layer = current_layer.all_layers[-1] if isinstance(next_layer, NeuralNetwork): # First layer of the next layer next_layer = next_layer.all_layers[0] # Find connective layer with correct layer pair connective_layer = get_connective_layer(current_layer, next_layer) connective_layers.add(connective_layer) all_layers.add(connective_layer) # Add final layer all_layers.add(self.input_layers[-1]) # Handle layering return connective_layers, all_layers def insert_layer(self, layer, insert_index): """Inserts a layer at the given index""" neural_network = self insert_animation = InsertLayer(layer, insert_index, neural_network) return insert_animation def remove_layer(self, layer): """Removes layer object if it exists""" neural_network = self return RemoveLayer(layer, neural_network, layer_spacing=self.layer_spacing) def replace_layer(self, old_layer, new_layer): """Replaces given layer object""" remove_animation = self.remove_layer(insert_index) insert_animation = self.insert_layer(layer, insert_index) # Make the animation animation_group = AnimationGroup( FadeOut(self.all_layers[insert_index]), FadeIn(layer), lag_ratio=1.0 ) return animation_group def make_forward_pass_animation(self, run_time=10, passing_flash=True, layer_args={}, **kwargs): """Generates an animation for feed forward propagation""" all_animations = [] for layer_index, layer in enumerate(self.all_layers): # 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. """ before_layer_args = {} after_layer_args = {} if layer.input_layer in layer_args: before_layer_args = layer_args[layer.input_layer] if layer.output_layer in layer_args: after_layer_args = layer_args[layer.output_layer] # Merge the two dicts current_layer_args = {**before_layer_args, **after_layer_args} else: current_layer_args = {} if layer in layer_args: 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, **kwargs) all_animations.append(layer_forward_pass) # Make the animation group animation_group = AnimationGroup(*all_animations, run_time=run_time, lag_ratio=1.0) return animation_group @override_animation(Create) def _create_override(self, **kwargs): """Overrides Create animation""" # Stop the neural network from being created twice if self.created: return AnimationGroup() self.created = True animations = [] # Create the overhead title animations.append(Create(self.title)) # Create each layer one by one for layer in self.all_layers: layer_animation = Create(layer) # Make titles create_title = Create(layer.title) # Create layer animation group animation_group = AnimationGroup( layer_animation, create_title ) animations.append(animation_group) animation_group = AnimationGroup(*animations, lag_ratio=1.0) return animation_group def set_z_index(self, z_index_value: float, family=False): """Overriden set_z_index""" # Setting family=False stops sub-neural networks from inheriting parent z_index return super().set_z_index(z_index_value, family=False) def __repr__(self, metadata=["z_index", "title_text"]): """Print string representation of layers""" inner_string = "" for layer in self.all_layers: inner_string += f"{repr(layer)} (" for key in metadata: value = getattr(layer, key) if not value is "": inner_string += f"{key}={value}, " inner_string += "),\n" inner_string = textwrap.indent(inner_string, " ") string_repr = "NeuralNetwork([\n" + inner_string + "])" return string_repr class FeedForwardNeuralNetwork(NeuralNetwork): """NeuralNetwork with just feed forward layers""" def __init__(self, layer_node_count, node_radius=0.08, node_color=BLUE, **kwargs): # construct layers layers = [] for num_nodes in layer_node_count: layer = FeedForwardLayer(num_nodes, node_color=node_color, node_radius=node_radius) layers.append(layer) # call super class super().__init__(layers, **kwargs)