diff options
Diffstat (limited to 'FSF-2020/calculus-of-several-variables/multivariable-functions-and-paritial-derivatives/Scalar Functions/scalar_function_neural_nets.py')
| -rw-r--r-- | FSF-2020/calculus-of-several-variables/multivariable-functions-and-paritial-derivatives/Scalar Functions/scalar_function_neural_nets.py | 177 |
1 files changed, 0 insertions, 177 deletions
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)
-
-
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