Finished nn/part3

nn/part3.py

@@ -3669,8 +3669,6 @@ class GeneralFormulas(SimplestNetworkExample):
         self.setup_bases()
 
     def construct(self):
-        self.force_skipping()
-
         self.setup_network_mob()
         self.show_all_a_labels()
         self.only_show_abstract_a_labels()
@@ -3925,22 +3923,34 @@ class GeneralFormulas(SimplestNetworkExample):
 
     def show_values_between_weight_and_cost(self):
         z_formula = TexMobject(
-            "z^{(L)}_j", "=", "\\cdots", "+" 
+            "z^{(L)}_j", "=", 
-            "w^{(L)}_{jk}", "a^{(L-1)}_k", "+", "\\cdots"
+            "w^{(L)}_{j0}", "a^{(L-1)}_0", "+",
+            "w^{(L)}_{j1}", "a^{(L-1)}_1", "+",
+            "w^{(L)}_{j2}", "a^{(L-1)}_2", "+", 
+            "b^{(L)}_j"
         )
-        z_formula.to_corner(UP+RIGHT)
+        compact_z_formula = TexMobject(
-        z_formula.highlight_by_tex_to_color_map({
+            "z^{(L)}_j", "=", 
-            "z^" : self.z_color,
+            "\\cdots", "", "+" 
-            "w^" : self.w_label.get_color()
+            "w^{(L)}_{jk}", "a^{(L-1)}_k", "+", 
-        })
+            "\\cdots", "", "", "",
-        w_part = z_formula.get_part_by_tex("w^")
+        )
-        aLm1_part = z_formula.get_part_by_tex("a^{(L-1)}")
+        for expression in z_formula, compact_z_formula:
+            expression.to_corner(UP+RIGHT)
+            expression.highlight_by_tex_to_color_map({
+                "z^" : self.z_color,
+                "w^" : self.w_label.get_color(),
+                "b^" : MAROON_B,
+            })
+        w_part = z_formula.get_parts_by_tex("w^")[1]
+        aLm1_part = z_formula.get_parts_by_tex("a^{(L-1)}")[1]
 
         a_formula = TexMobject(
             "a^{(L)}_j", "=", "\\sigma(", "z^{(L)}_j", ")"
         )
         a_formula.highlight_by_tex("z^", self.z_color)
         a_formula.next_to(z_formula, DOWN, MED_LARGE_BUFF)
+        a_formula.align_to(self.cost_equation, LEFT)
         aL_part = a_formula[0]
 
         to_fade = VGroup(
@@ -3973,10 +3983,16 @@ class GeneralFormulas(SimplestNetworkExample):
             self.chosen_neurons[1].label.copy(),
             aL_part
         ))
-        self.play(Write(VGroup(*a_formula[1:])))
+        self.play(
+            Write(VGroup(*a_formula[1:3] + [a_formula[-1]])),
+            ReplacementTransform(
+                z_formula[0].copy(),
+                a_formula.get_part_by_tex("z^")
+            )
+        )
         self.dither()
 
-        self.set_variables_as_attrs(z_formula, a_formula)
+        self.set_variables_as_attrs(z_formula, compact_z_formula, a_formula)
 
     def show_weight_chain_rule(self):
         chain_rule = self.get_chain_rule(
@@ -3996,6 +4012,9 @@ class GeneralFormulas(SimplestNetworkExample):
         ])
         rects.gradient_highlight(GREEN, WHITE, RED)
 
+        self.play(Transform(
+            self.z_formula, self.compact_z_formula
+        ))
         self.play(Write(chain_rule))
         self.dither()
         self.play(LaggedStart(
@@ -4151,7 +4170,6 @@ class GeneralFormulas(SimplestNetworkExample):
                 )
             ]
 
-        self.revert_to_original_skipping_status()
         self.play(ShowCreation(deriv_rect))
         self.play(LaggedStart(
             ShowCreationThenDestruction, 
@@ -4225,6 +4243,70 @@ class ThatsALotToThinkAbout(TeacherStudentsScene):
         self.change_student_modes(*["thinking"]*3)
         self.dither(4)
 
+class LayersOfComplexity(Scene):
+    def construct(self):
+        chain_rule_equations = self.get_chain_rule_equations()
+        chain_rule_equations.to_corner(UP+RIGHT)
+
+        brace = Brace(chain_rule_equations, LEFT)
+        arrow = Vector(LEFT, color = RED)
+        arrow.next_to(brace, LEFT)
+        gradient = TexMobject("\\nabla C")
+        gradient.scale(2)
+        gradient.highlight(RED)
+        gradient.next_to(arrow, LEFT)
+
+        self.play(LaggedStart(FadeIn, chain_rule_equations))
+        self.play(GrowFromCenter(brace))
+        self.play(GrowArrow(arrow))
+        self.play(Write(gradient))
+        self.dither()
+
+
+    def get_chain_rule_equations(self):
+        w_deriv = TexMobject(
+            "{\\partial C", "\\over", "\\partial w^{(l)}_{jk}}",
+            "=",
+            "a^{(l-1)}_k",
+            "\\sigma'(z^{(l)}_j)",
+            "{\\partial C", "\\over", "\\partial a^{(l)}_j}",
+        )
+        lil_rect = SurroundingRectangle(
+            VGroup(*w_deriv[-3:]),
+            buff = 0.5*SMALL_BUFF
+        )
+        a_deriv = TexMobject(
+            "\\sum_{j = 0}^{n_{l+1} - 1}",
+            "w^{(l+1)}_{jk}",
+            "\\sigma'(z^{(l+1)}_j)",
+            "{\\partial C", "\\over", "\\partial a^{(l+1)}_j}",
+        )
+        or_word = TextMobject("or")
+        last_a_deriv = TexMobject("2(a^{(L)}_j - y_j)")
+
+        a_deriv.next_to(w_deriv, DOWN, LARGE_BUFF)
+        or_word.next_to(a_deriv, DOWN)
+        last_a_deriv.next_to(or_word, DOWN, MED_LARGE_BUFF)
+
+        big_rect = SurroundingRectangle(VGroup(a_deriv, last_a_deriv))
+        arrow = Arrow(
+            lil_rect.get_corner(DOWN+LEFT), 
+            big_rect.get_top(),
+        )
+
+        result = VGroup(
+            w_deriv, lil_rect, arrow,
+            big_rect, a_deriv, or_word, last_a_deriv
+        )
+        for expression in w_deriv, a_deriv, last_a_deriv:
+            expression.highlight_by_tex_to_color_map({
+                "C" : RED,
+                "z^" : GREEN,
+                "w^" : BLUE,
+                "b^" : MAROON_B,
+            })
+        return result
+
 class SponsorFrame(PiCreatureScene):
     def construct(self):
         morty = self.pi_creature
@@ -4363,11 +4445,16 @@ class Thumbnail(PreviewLearning):
         },
         "stroke_width_exp" : 1,
         "max_stroke_width" : 5,
+        "title" : "Backpropagation",
+        "network_scale_val" : 0.8,
     }
     def construct(self):
         self.color_network_edges()
         network_mob = self.network_mob
-        network_mob.scale(0.8, about_point = network_mob.get_bottom())
+        network_mob.scale(
+            self.network_scale_val, 
+            about_point = network_mob.get_bottom()
+        )
         network_mob.activate_layers(np.random.random(self.layer_sizes[0]))
 
         for edge in it.chain(*network_mob.edge_groups):
@@ -4377,7 +4464,7 @@ class Thumbnail(PreviewLearning):
                 tip_length = 0.1,
                 color = edge.get_color()
             )
-            self.add(arrow.tip)
+            network_mob.add(arrow.tip)
 
         arrow = Vector(
             3*LEFT, 
@@ -4387,14 +4474,29 @@ class Thumbnail(PreviewLearning):
         )
         arrow.next_to(network_mob.edge_groups[1], UP, MED_LARGE_BUFF)
 
-        self.add(arrow)
+        network_mob.add(arrow)
+        self.add(network_mob)
 
-        title = TextMobject("Backpropagation")
+        title = TextMobject(self.title)
         title.scale(2)
         title.to_edge(UP)
         self.add(title)
 
-
+class SupplementThumbnail(Thumbnail):
+    CONFIG = {
+        "title" : "Backpropagation \\\\ calculus",
+        "network_scale_val" : 0.7,
+    }
+    def construct(self):
+        Thumbnail.construct(self)
+        self.network_mob.to_edge(DOWN, buff = MED_SMALL_BUFF)
+
+        for layer in self.network_mob.layers:
+            for neuron in layer.neurons:
+                partial = TexMobject("\\partial")
+                partial.move_to(neuron)
+                self.remove(neuron)
+                self.add(partial)