From d6f6012145d9b00b1a4b9c569e7dcffb92eb9d56 Mon Sep 17 00:00:00 2001 From: nishanpoojary Date: Tue, 26 May 2020 12:17:16 +0530 Subject: Delete scalar_function_neural_nets.py --- .../scalar_function_neural_nets.py | 177 --------------------- 1 file changed, 177 deletions(-) delete mode 100644 FSF-2020/calculus-of-several-variables/multivariable-functions-and-paritial-derivatives/Scalar Functions/scalar_function_neural_nets.py diff --git a/FSF-2020/calculus-of-several-variables/multivariable-functions-and-paritial-derivatives/Scalar Functions/scalar_function_neural_nets.py b/FSF-2020/calculus-of-several-variables/multivariable-functions-and-paritial-derivatives/Scalar Functions/scalar_function_neural_nets.py deleted file mode 100644 index eb6bf45..0000000 --- a/FSF-2020/calculus-of-several-variables/multivariable-functions-and-paritial-derivatives/Scalar Functions/scalar_function_neural_nets.py +++ /dev/null @@ -1,177 +0,0 @@ -from manimlib.imports import * - -class SigmoidFunc(GraphScene): - CONFIG = { - "x_min": -4, - "x_max": 4, - "y_min": -1, - "y_max": 1, - "graph_origin": ORIGIN + 0.8*DOWN, - "x_labeled_nums": list(range(-4, 5)), - "y_labeled_nums": list(range(-1, 2)), - "y_axis_height": 4.5, - } - def construct(self): - XTD = self.x_axis_width/(self.x_max- self.x_min) - YTD = self.y_axis_height/(self.y_max- self.y_min) - - topic = TextMobject("Sigmoid Function") - topic.move_to(3.2*UP) - topic.set_color_by_gradient(RED, ORANGE, YELLOW, GREEN, BLUE, PURPLE) - - self.setup_axes(animate = True) - sigmoid_func = self.get_graph(lambda x : (1/(1 + np.exp(-x))), x_min = -4, x_max = 4) - sigmoid_lab = self.get_graph_label(sigmoid_func, label = r"\frac{1}{1 + e^{-z}}") - - - - - self.play(ShowCreation(sigmoid_func),Write(sigmoid_lab)) - self.play(Write(topic)) - self.wait(2) - self.play(FadeOut(sigmoid_func), FadeOut(sigmoid_lab)) - self.wait(1) - - - -class NeuralNet(GraphScene): - def construct(self): - - sigmoid_exp = TextMobject(r"g(z) = g($\theta^T$ X) = $\frac{1}{1 + e^{-z}}$") - sigmoid_exp.move_to(3*UP + 4*LEFT) - sigmoid_exp.scale(0.8) - sigmoid_exp.set_color(BLUE) - sigmoid_exp1 = TextMobject(r"Predict: 'y = 1'",r"When g(z) $\geq$ 0.5, z $\geq$ 0, $\theta^T$ X $\geq$ 0") - sigmoid_exp2 = TextMobject(r"Predict: 'y = 0'", r"When g(z) $\leq$ 0.5, z $\leq$ 0, $\theta^T$ X $\leq$ 0") - sigmoid_exp1.scale(0.5) - sigmoid_exp2.scale(0.5) - sigmoid_exp1.set_color(PURPLE) - sigmoid_exp2.set_color(PURPLE) - - sigmoid_exp1[0].next_to(sigmoid_exp, 1.5*DOWN) - sigmoid_exp1[1].next_to(sigmoid_exp1[0], DOWN) - sigmoid_exp2[0].next_to(sigmoid_exp1[1], 1.5*DOWN) - sigmoid_exp2[1].next_to(sigmoid_exp2[0], DOWN) - - - self.play(Write(sigmoid_exp)) - self.play(Write(sigmoid_exp1[0]), Write(sigmoid_exp1[1])) - self.play(Write(sigmoid_exp2[0]), Write(sigmoid_exp2[1])) - self.wait(2) - - - neuron1 = Circle() - neuron1.set_fill(YELLOW_A, opacity = 0.5) - - neuron2 = Circle() - neuron2.set_fill(ORANGE, opacity = 0.5) - - neuron3 = Circle() - neuron3.set_fill(GREEN_E, opacity = 0.5) - - neuron1.move_to(2*UP+RIGHT) - neuron2.move_to(2*DOWN+RIGHT) - neuron3.move_to(4*RIGHT) - - arrow1 = Arrow(neuron1.get_right(),neuron3.get_left(),buff=0.1) - arrow1.set_color(RED) - arrow2 = Arrow(neuron2.get_right(),neuron3.get_left(),buff=0.1) - arrow2.set_color(RED) - - arrow3 = Arrow(neuron3.get_right(),7*RIGHT,buff=0.1) - arrow3.set_color(RED) - - - sign1 = TextMobject("+1") - sign1.move_to(2*UP+RIGHT) - sign1.scale(2) - sign2 = TextMobject(r"$x_1$") - sign2.move_to(2*DOWN+RIGHT) - sign2.scale(2) - sign3 = TextMobject(r"$h_{\theta}(x)$") - sign3.move_to(6*RIGHT+0.4*DOWN) - sign3.scale(0.7) - sign4 = TextMobject(r"$= g(10 - 20x_1)$") - sign4.next_to(sign3,DOWN) - sign4.scale(0.5) - sign5 = TextMobject(r"$= g(10 - 20x_1)$") - sign5.next_to(sign3,DOWN) - sign5.scale(0.5) - sign6 = TextMobject(r"$= g(10 - 20x_1)$") - sign6.next_to(sign3,DOWN) - sign6.scale(0.5) - - - weight1 = TextMobject("10") - weight1.next_to(arrow1,UP) - weight2 = TextMobject("-20") - weight2.next_to(arrow2,DOWN) - - gate = TextMobject("NOT GATE") - gate.set_color_by_gradient(RED, ORANGE, YELLOW, GREEN, BLUE, PURPLE) - gate.scale(1.5) - gate.move_to(3*RIGHT+3.5*UP) - - - - truth_table = TextMobject(r"\begin{displaymath}\begin{array}{|c|c|} x & y\\ \hline 1 & 0 \\0 & 1 \\\end{array}\end{displaymath}") - truth_table.next_to(sigmoid_exp2[1], 3*DOWN) - - values = TextMobject("1", "0") - values.scale(2) - - sign4_trans1 = TextMobject(r"$= g(10 - 20(1))$") - sign4_trans2 = TextMobject(r"$= g(10 - 20(0))$") - sign4_trans1.next_to(sign3,DOWN) - sign4_trans2.next_to(sign3,DOWN) - sign4_trans1.scale(0.5) - sign4_trans2.scale(0.5) - - - - output1 = TextMobject("y = 0") - output2 = TextMobject("y = 1") - output1.next_to(sign4,DOWN) - output2.next_to(sign4,DOWN) - output1.scale(1.5) - output2.scale(1.5) - - - - self.play(ShowCreation(neuron1),ShowCreation(neuron2)) - self.play(ShowCreation(neuron3)) - self.play(ShowCreation(sign1),ShowCreation(sign2)) - self.wait(1) - - self.play(GrowArrow(arrow1)) - self.play(GrowArrow(arrow2)) - self.play(ShowCreation(weight1),ShowCreation(weight2)) - - - - self.play(GrowArrow(arrow3)) - self.play(Write(sign3),Write(sign4)) - - self.play(Write(gate)) - self.play(ShowCreation(truth_table)) - - self.play(ApplyMethod(values[0].move_to, 2*DOWN+RIGHT)) - self.play(FadeOut(values[0])) - self.play(Transform(sign4,sign4_trans1)) - self.play(Write(output1)) - self.wait(1) - self.play(FadeOut(output1)) - self.play(Transform(sign4, sign5)) - - - self.play(ApplyMethod(values[1].move_to, 2*DOWN+RIGHT)) - self.play(FadeOut(values[1])) - self.play(Transform(sign4,sign4_trans2)) - self.play(Write(output2)) - self.wait(1) - self.play(FadeOut(output2)) - self.play(Transform(sign4, sign6)) - - self.wait(2) - - -- cgit