mirror of
https://github.com/helblazer811/ManimML.git
synced 2025-06-01 23:44:52 +08:00
Added GAN visualization.
This commit is contained in:
Alec Helbling
@ -9,35 +9,15 @@ Example:
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# Create the object with default style settings
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NeuralNetwork(layer_node_count)
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"""
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from socket import create_connection
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from manim import *
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import warnings
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import textwrap
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from manim_ml.neural_network.layers import connective_layers_list
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from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
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from manim_ml.neural_network.layers.util import get_connective_layer
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from manim_ml.list_group import ListGroup
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class LazyAnimation(Animation):
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"""
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Lazily creates animation when the animation is called.
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This is helpful when creating the animation depends upon the internal
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state of some set of objects.
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"""
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def __init__(self, animation_func):
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self.animation_func = animation_func
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super().__init__(None)
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def begin(self):
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"""Begins animation"""
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self.mobject, animation = self.animation_func()
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animation = Create(self.mobject)
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animation.begin()
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class RemoveLayer(Succession):
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class RemoveLayer(AnimationGroup):
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"""
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Animation for removing a layer from a neural network.
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@ -183,6 +163,99 @@ class RemoveLayer(Succession):
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update_func_anim = UpdateFromFunc(self.neural_network, create_new_connective)
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return update_func_anim
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class InsertLayer(AnimationGroup):
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"""Animation for inserting layer at given index"""
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def __init__(self, layer, index, neural_network):
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self.layer = layer
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self.index = index
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self.neural_network = neural_network
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# Layers before and after
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self.layers_before = self.neural_network.all_layers[:self.index]
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self.layers_after = self.neural_network.all_layers[self.index:]
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remove_connective_layer = self.remove_connective_layer()
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move_layers = self.make_move_layers()
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# create_layer = self.make_create_layer()
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# create_connective_layers = self.make_create_connective_layers()
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animations = [
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remove_connective_layer,
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move_layers,
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# create_layer,
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# create_connective_layers
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]
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super().__init__(*animations, lag_ratio=1.0)
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def remove_connective_layer(self):
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"""Removes the connective layer before the insertion index"""
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# Check if connective layer exists
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if len(self.layers_before) > 0:
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removed_connective = self.layers_before[-1]
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self.neural_network.all_layers.remove(removed_connective)
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# Make remove animation
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remove_animation = FadeOut(removed_connective)
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return remove_animation
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return AnimationGroup()
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def make_move_layers(self):
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"""Shifts layers before and after"""
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# Before layer shift
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before_shift_animation = AnimationGroup()
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if len(self.layers_before) > 0:
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before_shift = np.array([-self.layer.width/2, 0, 0])
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# Shift layers before
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before_shift_animation = Group(*self.layers_before).animate.shift(before_shift)
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# After layer shift
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after_shift_animation = AnimationGroup()
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if len(self.layers_after) > 0:
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after_shift = np.array([self.layer.width/2, 0, 0])
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# Shift layers after
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after_shift_animation = Group(*self.layers_after).animate.shift(after_shift)
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# Make animation group
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shift_animations = AnimationGroup(
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before_shift_animation,
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after_shift_animation
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)
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return shift_animations
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def make_create_layer(self):
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"""Animates the creation of the layer"""
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pass
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def make_create_connective_layers(self):
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pass
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# Make connective layers and shift animations
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# Before layer
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if len(layers_before) > 0:
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before_connective = get_connective_layer(layers_before[-1], layer)
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before_shift = np.array([-layer.width/2, 0, 0])
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# Shift layers before
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before_shift_animation = Group(*layers_before).animate.shift(before_shift)
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else:
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before_connective = AnimationGroup()
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# After layer
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if len(layers_after) > 0:
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after_connective = get_connective_layer(layer, layers_after[0])
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after_shift = np.array([layer.width/2, 0, 0])
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# Shift layers after
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after_shift_animation = Group(*layers_after).animate.shift(after_shift)
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else:
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after_connective = AnimationGroup
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insert_animation = Create(layer)
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animation_group = AnimationGroup(
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shift_animations,
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insert_animation,
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lag_ratio=1.0
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)
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return animation_group
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class NeuralNetwork(Group):
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@ -202,9 +275,6 @@ class NeuralNetwork(Group):
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# and make it have explicit distinct subspaces
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self._place_layers()
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self.connective_layers, self.all_layers = self._construct_connective_layers()
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# Make layer titles
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self.layer_titles = self._make_layer_titles()
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self.add(self.layer_titles)
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# Make overhead title
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self.title = Text(self.title_text, font_size=DEFAULT_FONT_SIZE/2)
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self.title.next_to(self, UP, 1.0)
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@ -229,15 +299,6 @@ class NeuralNetwork(Group):
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shift_vector = np.array([(previous_layer.get_width()/2 + current_layer.get_width()/2) + self.layer_spacing, 0, 0])
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current_layer.shift(shift_vector)
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def _make_layer_titles(self):
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"""Makes titles"""
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titles = VGroup()
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for layer in self.all_layers:
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title = layer.title
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title.next_to(layer, UP, 0.2)
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titles.add(title)
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return titles
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def _construct_connective_layers(self):
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"""Draws connecting lines between layers"""
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connective_layers = ListGroup()
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@ -264,40 +325,9 @@ class NeuralNetwork(Group):
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def insert_layer(self, layer, insert_index):
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"""Inserts a layer at the given index"""
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layers_before = self.all_layers[:insert_index]
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layers_after = self.all_layers[insert_index:]
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# Make connective layers and shift animations
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# Before layer
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if len(layers_before) > 0:
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before_connective = get_connective_layer(layers_before[-1], layer)
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before_shift = np.array([-layer.width/2, 0, 0])
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# Shift layers before
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before_shift_animation = Group(*layers_before).animate.shift(before_shift)
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else:
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before_connective = AnimationGroup()
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# After layer
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if len(layers_after) > 0:
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after_connective = get_connective_layer(layer, layers_after[0])
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after_shift = np.array([layer.width/2, 0, 0])
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# Shift layers after
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after_shift_animation = Group(*layers_after).animate.shift(after_shift)
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else:
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after_connective = AnimationGroup
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# Make animation group
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shift_animations = AnimationGroup(
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before_shift_animation,
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after_shift_animation
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)
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insert_animation = Create(layer)
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animation_group = AnimationGroup(
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shift_animations,
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insert_animation,
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lag_ratio=1.0
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)
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return animation_group
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neural_network = self
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insert_animation = InsertLayer(layer, insert_index, neural_network)
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return insert_animation
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def remove_layer(self, layer):
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"""Removes layer object if it exists"""
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@ -317,17 +347,18 @@ class NeuralNetwork(Group):
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return animation_group
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def make_forward_pass_animation(self, run_time=10, passing_flash=True):
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def make_forward_pass_animation(self, run_time=10, passing_flash=True,
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**kwargs):
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"""Generates an animation for feed forward propagation"""
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all_animations = []
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for layer_index, layer in enumerate(self.input_layers[:-1]):
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layer_forward_pass = layer.make_forward_pass_animation()
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layer_forward_pass = layer.make_forward_pass_animation(**kwargs)
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all_animations.append(layer_forward_pass)
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connective_layer = self.connective_layers[layer_index]
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connective_forward_pass = connective_layer.make_forward_pass_animation()
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connective_forward_pass = connective_layer.make_forward_pass_animation(**kwargs)
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all_animations.append(connective_forward_pass)
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# Do last layer animation
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last_layer_forward_pass = self.input_layers[-1].make_forward_pass_animation()
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last_layer_forward_pass = self.input_layers[-1].make_forward_pass_animation(**kwargs)
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all_animations.append(last_layer_forward_pass)
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# Make the animation group
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animation_group = AnimationGroup(*all_animations, run_time=run_time, lag_ratio=1.0)
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