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Preview back propogation scene tweaked to my liking
This commit is contained in:
Grant Sanderson
parent
5048fe80a5
commit
151f733f09
1 changed files with 61 additions and 19 deletions
80
nn/part2.py
80
nn/part2.py
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@ -103,6 +103,8 @@ class PreviewLearning(NetworkScene):
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"max_stroke_width" : 3,
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"stroke_width_exp" : 3,
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"eta" : 5.0,
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"positive_change_color" : GREEN_B,
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"negative_change_color" : RED_B,
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}
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def construct(self):
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self.initialize_network()
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@ -115,7 +117,7 @@ class PreviewLearning(NetworkScene):
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self.color_network_edges()
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def add_training_words(self):
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words = TextMobject("Training in \\\\ progress $\\dots$")
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words = TextMobject("Training in \\\\ progress$\\dots$")
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words.scale(1.5)
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words.to_corner(UP+LEFT)
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@ -127,12 +129,14 @@ class PreviewLearning(NetworkScene):
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image = get_training_image_group(train_in, train_out)
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self.activate_network(train_in, FadeIn(image))
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self.backprop_one_example(
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train_in, train_out, FadeOut(image)
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train_in, train_out,
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FadeOut(image), self.network_mob.layers.restore
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)
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def activate_network(self, train_in, *added_anims):
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network_mob = self.network_mob
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layers = network_mob.layers
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layers.save_state()
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activations = self.network.get_activation_of_all_layers(train_in)
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active_layers = [
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self.network_mob.get_active_layer(i, vect)
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@ -173,6 +177,8 @@ class PreviewLearning(NetworkScene):
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delta_neuron_groups, neuron_groups,
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delta_edge_groups, edge_groups
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)
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pc_color = self.positive_change_color
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nc_color = self.negative_change_color
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for i, nb, nw, delta_neurons, neurons, delta_edges, edges in reversed(tups):
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shown_nw = self.get_adjusted_first_matrix(nw)
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if np.max(shown_nw) == 0:
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@ -180,42 +186,45 @@ class PreviewLearning(NetworkScene):
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max_b = np.max(np.abs(nb))
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max_w = np.max(np.abs(shown_nw))
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for neuron, b in zip(delta_neurons, nb):
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color = RED_E if b > 0 else GREEN_E
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color = nc_color if b > 0 else pc_color
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# neuron.set_fill(color, abs(b)/max_b)
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neuron.set_stroke(color, 3)
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for edge, w in zip(delta_edges.split(), shown_nw.T.flatten()):
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edge.set_stroke(
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RED_E if w > 0 else GREEN_E,
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nc_color if w > 0 else pc_color,
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3*abs(w)/max_w
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)
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edge.rotate_in_place(np.pi)
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if i == 0:
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if i == 2:
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delta_edges.submobjects = [
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delta_edges[j]
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delta_edges[-(j+1)]
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for j in np.argsort(shown_nw.T.flatten())
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]
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network = self.network
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network.weights[i] -= self.eta*nw
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network.biases[i] -= self.eta*nb
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reversed_delta_edges = VGroup(*reversed(delta_edge_groups))
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reversed_delta_edges = VGroup(*it.chain(*reversed(delta_edge_groups)))
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reversed_delta_neurons = VGroup(*reversed(delta_neuron_groups))
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edge_groups.save_state()
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self.play(
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ShowCreation(
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LaggedStart(
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ShowCreation,
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reversed_delta_edges,
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run_time = 2,
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submobject_mode = "lagged_start",
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lag_factor = 6,
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run_time = 1.5,
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lag_ratio = 0.15,
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),
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FadeIn(
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reversed_delta_neurons,
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run_time = 2,
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submobject_mode = "lagged_start",
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lag_factor = 4,
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rate_func = None,
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)
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)
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self.color_network_edges()
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self.remove(edge_groups)
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self.play(*it.chain(
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[ReplacementTransform(
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edge_groups.saved_state, edge_groups,
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@ -281,6 +290,8 @@ class TrainingVsTestData(Scene):
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self.get_examples() for x in range(2)
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]
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training_examples.next_to(ORIGIN, LEFT)
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test_examples.next_to(ORIGIN, RIGHT)
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self.play(
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LaggedStart(FadeIn, training_examples),
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LaggedStart(FadeIn, test_examples),
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@ -339,7 +350,7 @@ class TrainingVsTestData(Scene):
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self.remove(train_ex)
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self.add(new_ex)
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new_ex[0][0].highlight(color)
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self.dither(1./10)
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self.dither(1./30)
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training_examples = new_examples
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class NotSciFi(TeacherStudentsScene):
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@ -348,20 +359,51 @@ class NotSciFi(TeacherStudentsScene):
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self.student_says(
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"Machines learning?!?",
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student_index = 0,
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target_mode = "confused",
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target_mode = "pleading",
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run_time = 1,
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)
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bubble = students[0].bubble
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students[0].bubble = None
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self.student_says(
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"Run!", student_index = 2,
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target_mode = "pleading",
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bubble_kwargs = {"direction" : LEFT}
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"Should we \\\\ be worried?", student_index = 2,
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target_mode = "confused",
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bubble_kwargs = {"direction" : LEFT},
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run_time = 1,
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)
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self.dither()
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students[0].bubble = bubble
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self.teacher_says(
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"It's actually \\\\ just calculus.",
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run_time = 1
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)
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self.teacher.bubble = None
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self.dither()
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self.student_says(
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"Even worse!",
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target_mode = "horrified",
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bubble_kwargs = {
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"direction" : LEFT,
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"width" : 3,
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"height" : 2,
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},
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)
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self.dither(2)
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class FunctionMinmization(GraphScene):
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CONFIG = {
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"x_labeled_nums" : range(-1, 10),
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}
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def construct(self):
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self.setup_axes()
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def func(x):
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x -= 5
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return 0.1*(x**3 - 9*x) + 4
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graph = self.get_graph(func)
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graph_label = self.get_graph_label(graph, "C(x)")
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self.add(graph, graph_label)
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dot = Dot(color = YELLOW)
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x =
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