Further SimplestNetworkExample practice

nn/part3.py

@@ -1778,7 +1778,6 @@ class OrganizeDataIntoMiniBatches(Scene):
         "random_seed" : 0,
     }
     def construct(self):
-        self.frame_duration = 1./24
         self.seed_random_libraries()
         self.add_examples()
         self.shuffle_examples()
@@ -1998,6 +1997,12 @@ class EOCWrapper(Scene):
         self.dither()
 
 class SimplestNetworkExample(PreviewLearning):
+    CONFIG = {
+        "random_seed" : 6,
+        "z_color" : GREEN,
+        "cost_color" : RED,
+        "desired_output_color" : YELLOW,
+    }
     def construct(self):
         self.force_skipping()
 
@@ -2023,8 +2028,8 @@ class SimplestNetworkExample(PreviewLearning):
         self.show_previous_weight_and_bias()
 
     def seed_random_libraries(self):
-        np.random.seed(0)
-        random.seed(0)
+        np.random.seed(self.random_seed)
+        random.seed(self.random_seed)
 
     def collapse_ordinary_network(self):
         network_mob = self.network_mob
@@ -2174,7 +2179,6 @@ class SimplestNetworkExample(PreviewLearning):
         rect = SurroundingRectangle(neuron)
         words = TextMobject("Desired \\\\ output")
         words.next_to(rect, UP)
-        VGroup(words, rect).highlight(YELLOW)
 
         y_label = TexMobject("y")
         y_label.next_to(neuron, DOWN, LARGE_BUFF)
@@ -2184,6 +2188,7 @@ class SimplestNetworkExample(PreviewLearning):
             color = WHITE,
             buff = SMALL_BUFF
         )
+        VGroup(words, rect, y_label).highlight(self.desired_output_color)
 
         self.play(*map(FadeIn, [neuron, decimal]))
         self.play(
@@ -2206,13 +2211,108 @@ class SimplestNetworkExample(PreviewLearning):
         )
 
     def show_cost(self):
-        pass
+        pre_a = self.a_labels[0].copy()
+        pre_y = self.y_label.copy()
+
+        cost_equation = TexMobject(
+            "C_0", "(", "\\dots", ")", "=",
+            "(", "a^{(L)}", "-", "y", ")", "^2"
+        )
+        cost_equation.to_corner(UP+RIGHT)
+        C0, a, y = [
+            cost_equation.get_part_by_tex(tex)
+            for tex in "C_0", "a^{(L)}", "y"
+        ]
+        y.highlight(YELLOW)
+
+        cost_word = TextMobject("Cost")
+        cost_word.next_to(C0[0], LEFT, LARGE_BUFF)
+        cost_arrow = Arrow(
+            cost_word, C0,
+            buff = SMALL_BUFF
+        )
+        VGroup(C0, cost_word, cost_arrow).highlight(self.cost_color)
+
+        self.play(
+            ReplacementTransform(pre_a, a),
+            ReplacementTransform(pre_y, y),
+        )
+        self.play(LaggedStart(
+            FadeIn, VGroup(*filter(
+                lambda m : m not in [a, y],
+                cost_equation
+            ))
+        ))
+        self.dither()
+        self.play(
+            Write(cost_word, run_time = 1),
+            GrowArrow(cost_arrow)
+        )
+        self.play(C0.shift, MED_SMALL_BUFF*UP, rate_func = wiggle)
+        self.dither()
+
+        self.set_variables_as_attrs(
+            cost_equation, cost_word, cost_arrow
+        )
 
     def show_activation_formula(self):
-        pass
+        neuron = self.network_mob.layers[-1].neurons[0]
+        edge = self.network_mob.edge_groups[-1][0]
+        pre_aL, pre_aLm1 = self.a_labels.copy()
+
+        formula = TexMobject(
+            "a^{(L)}", "=", "\\sigma", "(",
+            "w^{(L)}", "a^{(L-1)}", "+", "b^{(L)}", ")"
+        )
+        formula.next_to(neuron, UP, MED_LARGE_BUFF, RIGHT)
+        aL, equals, sigma, lp, wL, aLm1, plus, bL, rp = formula
+        wL.highlight(edge.get_color())
+        weight_label = wL.copy()
+        bL.highlight(MAROON_B)
+        bias_label = bL.copy()
+        sigma_group = VGroup(sigma, lp, rp)
+        sigma_group.save_state()
+        sigma_group.set_fill(opacity = 0)
+        sigma_group.shift(DOWN)
+
+        self.play(
+            ReplacementTransform(pre_aL, aL),
+            Write(equals)
+        )
+        self.play(ReplacementTransform(
+            edge.copy(), wL
+        ))
+        self.dither()
+        self.play(ReplacementTransform(pre_aLm1, aLm1))
+        self.dither()
+        self.play(Write(VGroup(plus, bL), run_time = 1))
+        self.dither()
+        self.play(sigma_group.restore)
+        self.dither()
+
+        weighted_sum_terms = VGroup(wL, aLm1, bL)
+        self.set_variables_as_attrs(
+            formula, weighted_sum_terms
+        )
 
     def introduce_z(self):
-        pass
+        terms = self.weighted_sum_terms
+        brace = Brace(terms, UP, buff = SMALL_BUFF)
+        z_label = TexMobject("z^{(L)}")
+        z_label.next_to(brace, UP, buff = SMALL_BUFF)
+        z_label.highlight(self.z_color)
+        rect = SurroundingRectangle(terms)
+        rect.highlight(GREEN)
+
+        self.play(ShowCreation(rect))
+        self.play(
+            GrowFromCenter(brace),
+            Write(z_label),
+        )
+        self.play(FadeOut(rect))
+        self.dither()
+
+        self.set_variables_as_attrs(z_label, z_brace = brace)
 
     def break_into_computational_graph(self):
         pass