From 06791eb8867f3e8418f8030fb0f5f121fdcc4a5d Mon Sep 17 00:00:00 2001 From: LeeSinLiang Date: Sun, 16 Apr 2023 17:17:17 +0800 Subject: [PATCH] initial commit --- .gitignore | 2 + applications.py | 300 ++++++++ assets/brain.svg | 7 + assets/cat.png | Bin 0 -> 325849 bytes assets/like.svg | 21 + assets/liked.svg | 21 + assets/orange2.0.svg | 7 + assets/soundwavee.svg | 119 ++++ assets/svg_images/Bubbles_speech.svg | 11 + assets/svg_images/finger.svg | 79 +++ assets/svg_images/mail.svg | 46 ++ assets/user.svg | 44 ++ assets/vector_images/Bubbles_speech.svg | 11 + bias_update.py | 96 +++ chooose.py | 73 ++ data/activation1.csv | 13 + data/activation2.csv | 11 + data/activation3.csv | 9 + data/activation4.csv | 7 + data/my_submission.csv | 419 +++++++++++ data/test.csv | 419 +++++++++++ data/train.csv | 892 ++++++++++++++++++++++++ data/weights.pkl | Bin 0 -> 3508 bytes data/without_training/activation1.csv | 13 + data/without_training/activation2.csv | 11 + data/without_training/activation3.csv | 9 + data/without_training/activation4.csv | 7 + data/without_training/y.csv | 2 + data/without_training/y_hat.csv | 2 + endscreen.py | 72 ++ explanationall.py | 796 +++++++++++++++++++++ graph_scene.py | 60 ++ nn.py | 407 +++++++++++ requirements.txt | 4 + 34 files changed, 3990 insertions(+) create mode 100644 applications.py create mode 100644 assets/brain.svg create mode 100644 assets/cat.png create mode 100644 assets/like.svg create mode 100644 assets/liked.svg create mode 100644 assets/orange2.0.svg create mode 100644 assets/soundwavee.svg create mode 100644 assets/svg_images/Bubbles_speech.svg create mode 100644 assets/svg_images/finger.svg create mode 100644 assets/svg_images/mail.svg create mode 100644 assets/user.svg create mode 100644 assets/vector_images/Bubbles_speech.svg create mode 100644 bias_update.py create mode 100644 chooose.py create mode 100644 data/activation1.csv create mode 100644 data/activation2.csv create mode 100644 data/activation3.csv create mode 100644 data/activation4.csv create mode 100644 data/my_submission.csv create mode 100644 data/test.csv create mode 100644 data/train.csv create mode 100644 data/weights.pkl create mode 100644 data/without_training/activation1.csv create mode 100644 data/without_training/activation2.csv create mode 100644 data/without_training/activation3.csv create mode 100644 data/without_training/activation4.csv create mode 100644 data/without_training/y.csv create mode 100644 data/without_training/y_hat.csv create mode 100644 endscreen.py create mode 100644 explanationall.py create mode 100644 graph_scene.py create mode 100644 nn.py create mode 100644 requirements.txt diff --git a/.gitignore b/.gitignore index b6e4761..eaa7b9e 100644 --- a/.gitignore +++ b/.gitignore @@ -127,3 +127,5 @@ dmypy.json # Pyre type checker .pyre/ + +audio/ diff --git a/applications.py b/applications.py new file mode 100644 index 0000000..f0ead11 --- /dev/null +++ b/applications.py @@ -0,0 +1,300 @@ +# Manim Cairo +from manimlib import * +# ManimGL +# from manimlib import * +import sys +import os.path +from manimlib.mobject.svg.drawings import SpeechBubble +from manimlib.mobject import geometry +# import six,colour,nncc2gl +from nn import * + +class Application(NeuralNetwork): + def construct(self): + NeuralNetwork.construct(self) + self.play( + *[FadeOut(mob)for mob in self.mobjects] + # All mobjects in the screen are saved in self.mobjects + ) + NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2] + NeuralNetwork.add_neurons(self,False) + NeuralNetwork.add_edges(self,False) + NeuralNetwork.group1(self) + self.grp.scale(0.5) + print('...') + self.clear() + lineoguidance = Line([-3, 0, 0], [3, 0, 0]).rotate(90*DEGREES) + self.wait(6) + self.v = self.virtual_assistant() + self.wait(12.5) + grp=self.grp + NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 2] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + self.grp.scale(0.5) + v_copy = self.v.copy().move_to(lineoguidance.get_end()).scale(0.18) + lineoguidance.rotate(60*DEGREES) + self.email = self.mail().move_to(lineoguidance.get_end()).scale(0.25) + lineoguidance.rotate(60*DEGREES) + self.youtube = self.yt_logo().move_to(lineoguidance.get_end()).scale(0.25) + lineoguidance.rotate(60*DEGREES) + self.tc = self.textclassification().move_to(lineoguidance.get_end()).scale(0.2) + lineoguidance.rotate(60*DEGREES) + self.stock_prediction = self.stock_prediction().move_to( + lineoguidance.get_end()).scale(0.15) + lineoguidance.rotate(60*DEGREES) + self.credit_card = self.credit_card().move_to(lineoguidance.get_end()).scale(0.25) + # self.rotating_applications() + # sun = Circle() + + + self.play(Transform(self.v, v_copy), FadeIn(self.credit_card), FadeIn( + self.email), FadeIn(self.tc), FadeIn(self.stock_prediction), FadeIn(self.youtube),ReplacementTransform(grp,self.grp)) + self.path = ArcBetweenPoints(start=self.email.get_center(),end=self.youtube.get_center(),radius=3,angle=TAU/6) + self.path1 = ArcBetweenPoints(start=self.youtube.get_center(),end=self.tc.get_center(),radius=3,angle=TAU/6) + self.path2 = ArcBetweenPoints(start=self.tc.get_center(),end=self.stock_prediction.get_center(),radius=3,angle=TAU/6) + self.path3 = ArcBetweenPoints(start=self.stock_prediction.get_center(),end=self.credit_card.get_center(),radius=3,angle=TAU/6) + self.path4 = ArcBetweenPoints(start=self.credit_card.get_center(),end=self.v.get_center(),radius=3,angle=TAU/6) + self.path5 = ArcBetweenPoints(start=self.v.get_center(),end=self.email.get_center(),radius=3,angle=TAU/6) + # path5 = ArcBetweenPoints(start=credit_card.get_center(),end=tc.get_center(),radius=3,angle=TAU/6) + + self.rotating_applications() + self.wait(12) + + # self.embed() + + def alt_text(self): + self.ttc = Text('Text Classification').scale(0.5) + self.tem = Text('Spam Filters').scale(0.5) + self.tsp = Text('Stock Prediction').scale(0.5) + self.cc = Text('Fraud Detection').scale(0.5) + self.yt = Text('Video Recommendation').scale(0.5) + self.tv = Text('Virtual Assistant').scale(0.5) + + always(self.ttc.next_to, self.cc_copy, RIGHT) + always(self.tem.next_to, self.email_copy, RIGHT) + always(self.tsp.next_to , self.stock_prediction_copy, RIGHT) + always(self.yt.next_to, self.youtube_copy, RIGHT) + always(self.cc.next_to, self.cc_copy, RIGHT) + # always(self.tv.next_to, self.v, RIGHT) + + def rotating_applications(self): + + self.cc_copy = self.credit_card.copy().move_to(3*UP).scale(2) + self.tc_copy = self.tc.copy().move_to(3*UP).scale(2) + self.email_copy = self.email.copy().move_to(3*UP).scale(2) + self.stock_prediction_copy = self.stock_prediction.copy().move_to(3*UP).scale(2) + self.youtube_copy = self.youtube.copy().move_to(3*UP).scale(2) + # cc_copy = self.credit_card.copy().move_to(3*UP).scale(2) + self.alt_text() + + self.play(MoveAlongPath(self.email, self.path), + MoveAlongPath(self.youtube, self.path1), + MoveAlongPath(self.tc, self.path2), + MoveAlongPath(self.stock_prediction, self.path3), + MoveAlongPath(self.credit_card, self.path4), + MoveAlongPath(self.v, self.path5), + Transform(self.credit_card,self.cc_copy)) + self.play(FadeIn(self.cc)) + self.play(ApplyMethod(self.credit_card.scale,0.5), + MoveAlongPath(self.email, self.path1), + MoveAlongPath(self.youtube, self.path2), + MoveAlongPath(self.tc, self.path3), + MoveAlongPath(self.stock_prediction, self.path4), + MoveAlongPath(self.credit_card, self.path5), + MoveAlongPath(self.v,self.path), + Transform(self.stock_prediction,self.stock_prediction_copy), + ReplacementTransform(self.cc,self.tsp)) + self.wait() + self.play(ApplyMethod(self.stock_prediction.scale,0.5), + MoveAlongPath(self.email, self.path2), + MoveAlongPath(self.youtube, self.path3), + MoveAlongPath(self.tc, self.path4), + MoveAlongPath(self.stock_prediction, self.path5), + MoveAlongPath(self.credit_card, self.path), + MoveAlongPath(self.v, self.path1), + Transform(self.tc,self.tc_copy), + ReplacementTransform(self.tsp,self.ttc)) + self.wait() + self.play(ApplyMethod(self.tc.scale,0.5), + MoveAlongPath(self.email, self.path3), + MoveAlongPath(self.youtube, self.path4), + MoveAlongPath(self.tc, self.path5), + MoveAlongPath(self.stock_prediction, self.path), + MoveAlongPath(self.credit_card, self.path1), + MoveAlongPath(self.v, self.path2), + Transform(self.youtube,self.youtube_copy), + ReplacementTransform(self.ttc,self.yt)) + self.wait() + self.play(ApplyMethod(self.youtube.scale,0.5), + MoveAlongPath(self.email, self.path4), + MoveAlongPath(self.youtube, self.path5), + MoveAlongPath(self.tc, self.path), + MoveAlongPath(self.stock_prediction, self.path1), + MoveAlongPath(self.credit_card, self.path2), + MoveAlongPath(self.v, self.path3), + Transform(self.email, self.email_copy), + ReplacementTransform(self.yt,self.tem)) + self.wait() + + + def full_rotate_without_zoom(self,va,v): + lineoguidance = Line([-3, 0, 0], [3, 0, 0]).rotate(60*DEGREES) + credit_card = self.credit_card().move_to(lineoguidance.get_end()).scale(0.25) + lineoguidance.rotate(60*DEGREES) + tc = self.textclassification().move_to(lineoguidance.get_end()).scale(0.2) + lineoguidance.rotate(120*DEGREES) + email = self.mail().move_to(lineoguidance.get_end()).scale(0.25) + stock_prediction = self.stock_prediction().move_to(ORIGIN+3*LEFT).scale(0.1) + lineoguidance.rotate(60*DEGREES) + youtube = self.yt_logo().move_to(lineoguidance.get_end()).scale(0.25) + lineoguidance.rotate(60*DEGREES) + c = Circle() + c1 = Circle() + c2 = Circle() + c3 = Circle() + c4 = Circle() + c.move_to(credit_card.get_center()) + c1.move_to(email.get_center()) + c2.move_to(tc.get_center()) + c3.move_to(stock_prediction.get_center()) + c4.move_to(youtube.get_center()) + group1 = VGroup(credit_card, c) + group2 = VGroup(email, c1) + group3 = VGroup(tc, c2) + group4 = VGroup(stock_prediction, c3) + group5 = VGroup(youtube, c4) + path1 = Circle(radius=3).rotate(60*DEGREES) + path2 = Circle(radius=3).rotate(120*DEGREES) + path3 = Circle(radius=3).rotate(180*DEGREES) + path4 = Circle(radius=3).rotate(240*DEGREES) + path5 = Circle(radius=3).rotate(300*DEGREES) + self.add(group1, group2, group3, group4, group5) + + #path = ArcBetweenPoints(start=tc.get_center(),end=stock_prediction.get_center(),radius=3,angle=TAU/6) + # infogroup1 = VGroup(v,infocircle1) + self.play(MoveAlongPath(v.infogroup1, v.path), MoveAlongPath(group1, path1), MoveAlongPath(group2, path2), MoveAlongPath( + group3, path3), MoveAlongPath(group4, path4), MoveAlongPath(group5, path5), run_time=4, rate_func=linear) + # self.remove(v) + Arc() + # v = self.textclassification() + # self.add(v) + # credit_card = self.credit_card() + # self.play(ShowCreation(credit_card)) + return None + + + + def virtual_assistant(self): + r = RoundedRectangle(height=5, width=3).shift(4*RIGHT) + self.grp.scale(1.5).next_to(r,LEFT).shift(0.5*LEFT) + person = SVGMobject('assets/user(3).svg').to_edge(LEFT) + person.set_stroke(width=0.2) + bubble = SpeechBubble(height=1.5, width=2.5) + bubble.add_content(Text('Hey Google', color=WHITE)) + # bubble.set_fill(opacity=0) + bubble.set_stroke(WHITE, 1) + # bubble.pin_to(self) + message = Rectangle(height=1, width=2.5).shift(RIGHT) + message.move_to(r.get_center()+DOWN) + t = Text('Hi, How can I help?').scale( + 0.25).move_to(message.get_center()) + g = Text('G').next_to(r.get_corner(RIGHT+UP), LEFT+DOWN) + self.play(ShowCreation(r)) + self.play(Write(person)) + VGroup(bubble, bubble.content).next_to( + person.get_corner(RIGHT+UP), RIGHT) + self.play(ShowCreation(bubble), Write(bubble.content)) + self.wait(2) + self.play(ShowCreation(message), Write(t), Write(g)) + self.wait(10) + self.play(Write(self.grp)) + self.wait() + v = VGroup(r, person, bubble, bubble.content, message, t, g) + return v + + def mail(self): + return SVGMobject('assets/mail.svg') + + def applications_example(self): + example1 = Text('Text Recongition') + example2 = Text('Image Recongition') + example3 = Text('Speech Recongition') + example4 = Text('Text Recongition') + + def credit_card(self): + card = RoundedRectangle(height=3, width=5) + upper_line = Rectangle(height=0.2, width=5).set_fill(WHITE, 1) + upper_line.shift(0.7*UP) + line1 = Line(stroke_width=10).set_fill(WHITE, 1) + line2 = Line(stroke_width=10).set_fill(WHITE, 1) + line3 = Line(stroke_width=10).set_fill(WHITE, 1) + line4 = Line(stroke_width=10).set_fill(WHITE, 1) + line1.set_length(0.8) + line2.set_length(0.8) + line3.set_length(0.8) + line4.set_length(0.8) + line_bottom = Line(stroke_width=10) + line1.shift(1.8*LEFT) + line2.shift(1.8*RIGHT) + line3.shift(0.6*RIGHT) + line4.shift(0.6*LEFT) + line_bottom.move_to(card.get_center()+0.15*BOTTOM+1.2*LEFT) + credit_card = VGroup(card, upper_line, line1, + line2, line3, line4, line_bottom) + return credit_card + + def yt_logo(self): + t = Triangle().set_fill(WHITE, 1) + t.rotate(270*DEGREES, about_point=np.array([1, 1, 0])) + t.move_to(ORIGIN) + r = RoundedRectangle(height=3, width=5) + r.set_fill(RED, 1) + r.set_stroke(RED,1) + t.set_fill(WHITE,1) + real_yt_logo = VGroup(r,t) + return real_yt_logo + + # self.embed() + + def textclassification(self): + box = Rectangle(height=5).shift(LEFT) + t = Text('Article').scale(0.6).shift(LEFT) + box.surround(t) + pointer1 = CurvedArrow(ORIGIN,UP+2*RIGHT,angle=-TAU/4) + pointer2 = Arrow(ORIGIN,2*RIGHT) + pointer3 = CurvedArrow(ORIGIN,DOWN+2*RIGHT) + t1 = Text('Science').scale(0.6).shift(UP+3.5*RIGHT) + t2 = Text('Buisness').scale(0.6).shift(3.5*RIGHT) + t3 = Text('Entertainment').scale(0.6).shift(DOWN+4*RIGHT) + tc = VGroup(box,t,pointer1,pointer2,pointer3,t1,t2,t3) + return tc + + def stock_prediction(self): + #random values here + x = [1, 3, 5, 7, 9, 11] + y = [3, 6, 1, 7, 4, 9] + axes = Axes((0,12),(0,12)) + dot_collection = VGroup() + for time, dat in zip(x,y): + dot = Dot().move_to(axes.coords_to_point(time , dat)) + dot_collection.add(dot) + line_collection = VGroup() + for i in range(5): + line = Line(dot_collection[i].get_center(),dot_collection[i+1].get_center()) + line_collection.add(line) + + return VGroup(axes,dot_collection,line_collection) + + def title(self): + return + +''' +1. Text classificaon / +2. Email spam filters / +3. Speech Recognition / +4. Recommender System / +5. Fraud Detection / +6. 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0000000..21f8b6d --- /dev/null +++ b/assets/liked.svg @@ -0,0 +1,21 @@ + + + + + + + + + diff --git a/assets/orange2.0.svg b/assets/orange2.0.svg new file mode 100644 index 0000000..f2499b9 --- /dev/null +++ b/assets/orange2.0.svg @@ -0,0 +1,7 @@ + + + + + Svg Vector Icons : http://www.onlinewebfonts.com/icon + + \ No newline at end of file diff --git a/assets/soundwavee.svg b/assets/soundwavee.svg new file mode 100644 index 0000000..d1dacce --- /dev/null +++ b/assets/soundwavee.svg @@ -0,0 +1,119 @@ + + + + +Created by potrace 1.16, written by Peter Selinger 2001-2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/assets/svg_images/Bubbles_speech.svg b/assets/svg_images/Bubbles_speech.svg new file mode 100644 index 0000000..d2b659c --- /dev/null +++ b/assets/svg_images/Bubbles_speech.svg @@ -0,0 +1,11 @@ + + + + + + \ No newline at end of file diff --git a/assets/svg_images/finger.svg b/assets/svg_images/finger.svg new file mode 100644 index 0000000..38b05f6 --- /dev/null +++ b/assets/svg_images/finger.svg @@ -0,0 +1,79 @@ + + + + + + + + + + + + + image/svg+xml + + + + + Openclipart + + + + + + + + + + + \ No newline at end of file diff --git a/assets/svg_images/mail.svg b/assets/svg_images/mail.svg new file mode 100644 index 0000000..1d24d48 --- /dev/null +++ b/assets/svg_images/mail.svg @@ -0,0 +1,46 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/assets/user.svg b/assets/user.svg new file mode 100644 index 0000000..f7496a3 --- /dev/null +++ b/assets/user.svg @@ -0,0 +1,44 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/assets/vector_images/Bubbles_speech.svg b/assets/vector_images/Bubbles_speech.svg new file mode 100644 index 0000000..d2b659c --- /dev/null +++ b/assets/vector_images/Bubbles_speech.svg @@ -0,0 +1,11 @@ + + + + + + \ No newline at end of file diff --git a/bias_update.py b/bias_update.py new file mode 100644 index 0000000..4aebd93 --- /dev/null +++ b/bias_update.py @@ -0,0 +1,96 @@ +from manimlib import * +from nn import NeuralNetwork +from applications import Application +from chooose import Choose + + +class Bias(Choose): + def construct(self): + axes = Axes((-10, 10), (0, 1.2)).scale(0.5) + axes.add_coordinate_labels( + x_values=[-10, -5, 0, 5, 10], y_values=[], font_size=15) + sigmoid_w0 = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-0.5*x)), + use_smoothing=False, + color=RED, + ) + sigmoid_w1 = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-1*x)), + use_smoothing=False, + color=GREEN, + ) + sigmoid_w2 = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-5*x)), + use_smoothing=False, + color=BLUE, + ) + non_linear_label = Tex( + "\\sigma (0.5*x)", color=RED).move_to([4.9, 2, 0]) + non_linear_label_1 = Tex( + "\\sigma (1*x)", color=GREEN).move_to([4.8, 1, 0]) + non_linear_label_2 = Tex( + "\\sigma (5*x)", color=BLUE).move_to([4.8, 0, 0]) + + lbl_3 = axes.x_axis.get_number_mobject(3, font_size=15) + lbl_negative_3 = axes.x_axis.get_number_mobject(-3, font_size=15) + sigma = Tex('\sigma').scale(2) + self.wait(3) + self.play(Write(sigma)) + self.play(sigma.animate.shift(1.5*LEFT),run_time=2) + self.play(sigma.animate.shift(3*RIGHT),run_time=2) + self.wait(2) + self.play(FadeOut(sigma)) + self.play(ShowCreation(axes),run_time=2) + self.play(ShowCreation(sigmoid_w1)) + self.wait() + self.play(Write(non_linear_label_1)) + self.wait() + self.play(ReplacementTransform(sigmoid_w1.copy(), + sigmoid_w0), Write(non_linear_label),run_time=2) + self.wait() + self.play(ReplacementTransform(sigmoid_w0.copy(), + sigmoid_w2), Write(non_linear_label_2),run_time=2) + self.wait() + sigmoid_w2_copy = sigmoid_w2.copy() + self.play(ReplacementTransform(sigmoid_w1.copy(),sigmoid_w2_copy,run_time=3),ZoomInThenZoomOut(non_linear_label_2)) + self.wait(5.5) + self.play(IndicateThenFadeOut(lbl_3, scale_factor=1.5), + FadeOut(sigmoid_w2_copy)) + self.wait(2.5) + self.play(IndicateThenFadeOut(lbl_negative_3, scale_factor=1.5)) + # self.play(FadeOut(lbl_3)) + self.wait() + self.play(ReplacementTransform(sigmoid_w2, sigmoid_w1.copy()), ReplacementTransform( + sigmoid_w0, sigmoid_w1.copy()), FadeOut(VGroup(non_linear_label, non_linear_label_2))) + self.wait(2) + + sigmoid_graph_3 = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-(-6+1*x))), + use_smoothing=False, + color=RED, + ) + # v_line = always_redraw(lambda: axes.get_v_line(dot.get_bottom())) + dot = Dot().move_to(axes.i2gp(3, sigmoid_graph_3)) + line = Line(dot, axes.c2p(3, 0)) + sigmoid_graph2 = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-(6+1*x))), + use_smoothing=False, + color=BLUE, + ) + dot1 = Dot().move_to(axes.i2gp(-3, sigmoid_graph2)) + line1 = axes.get_v_line(dot1.get_bottom()) + + bias_label = Tex("\\sigma (1*x+(-6*1))", + color=RED).move_to([4.9, 2, 0]) + # bias_label_1 = Tex("\\sigma (1*x)", color=GREEN).move_to([4.8,1,0]) + bias_label_2 = Tex("\\sigma (1*x+(6*1))", + color=BLUE).move_to([4.8, 0, 0]) + + # line = axes.get_graph(lambda x: 0.5, color=YELLOW) + self.play(ReplacementTransform(sigmoid_w1.copy(), + sigmoid_graph2), Write(bias_label_2)) + self.play(GrowFromPoint(dot1, dot1.get_center()), ShowCreation(line1)) + self.play(ReplacementTransform(sigmoid_w1.copy(), + sigmoid_graph_3), Write(bias_label)) + self.play(ShowCreation(VGroup(dot, line))) + # self.embed() diff --git a/chooose.py b/chooose.py new file mode 100644 index 0000000..7af6c8d --- /dev/null +++ b/chooose.py @@ -0,0 +1,73 @@ +from numpy import exp +from manimlib import * +from nn import * +from applications import * + + +class Choose(Application): + + def construct(self): + NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 4] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + self.grp.scale(1.2).to_edge(RIGHT).shift(LEFT) + title = TexText('Activation Function').move_to([0, 2.5, 0]) + hidden_layer = TexText('Hidden Layer').move_to([-3, 1, 0]) + output_layer = TexText('Output Layer').move_to([3, 1, 0]) + b_classification = TexText( + 'Binary\\\Classification').move_to([1.5, -1, 0]) + m_classification = TexText( + 'Multiclass\\\Classification').move_to([4.5, -1, 0]) + ReLU = TexText('ReLU\\\Activation').move_to([-3, -1, 0]) + r = Rectangle().surround(ReLU) + sigmoid = TexText('Sigmoid/Tanh\\\Activation').move_to([1.5, -3, 0]) + softmax = TexText('Softmax\\\Activation').move_to([4.5, -3, 0]) + line = Arrow(title, hidden_layer) + line1 = Arrow(title, output_layer) + line2 = Arrow(output_layer, b_classification) + line3 = Arrow(output_layer, m_classification) + arrow = Arrow(hidden_layer, ReLU) + arrow1 = Arrow(b_classification, sigmoid) + arrow2 = Arrow(m_classification, softmax) + self.wait(2) + self.play(Write(title)) + self.wait(2) + self.play(ShowCreation(line), ShowCreation(line1)) + self.wait(9) + self.play(Write(ReLU)) + self.wait() + self.play(ShowCreation(arrow)) + self.play(Write(hidden_layer)) + self.play(Write(b_classification)) + self.wait() + self.play(ShowCreation(arrow1)) + self.play(Write(sigmoid)) + self.wait() + self.play(Write(output_layer)) + self.play(ShowCreation(line2)) + self.play(ShowCreation(line3)) + self.wait() + self.play(Write(softmax)) + self.wait() + self.play(Write(m_classification)) + self.play(ShowCreation(arrow2)) + self.wait() + + def label(self, l, group, layer=0, edges=False, bias=False): + if bias is True: + for x in range(len(NeuralNetwork.arguments['layer_sizes'])-1): + label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8) + label.move_to(self.bias_layer[x]) + group.add(label) + else: + for x in range(NeuralNetwork.arguments['layer_sizes'][layer]): + label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8) + + label.move_to(self.grp.edges[layer][x]).shift( + 0.4*UP) if edges is True else label.move_to(self.grp.layers[layer][0][x]) + group.add(label) + + return group + +#TODO softmax function diff --git a/data/activation1.csv b/data/activation1.csv new file mode 100644 index 0000000..be67031 --- /dev/null +++ b/data/activation1.csv @@ -0,0 +1,13 @@ 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diff --git a/data/activation2.csv b/data/activation2.csv new file mode 100644 index 0000000..d40c633 --- /dev/null +++ b/data/activation2.csv @@ -0,0 +1,11 @@ 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diff --git a/data/activation4.csv b/data/activation4.csv new file mode 100644 index 0000000..cb8449a --- /dev/null +++ b/data/activation4.csv @@ -0,0 +1,7 @@ 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diff --git a/data/without_training/activation2.csv b/data/without_training/activation2.csv new file mode 100644 index 0000000..6c389c7 --- /dev/null +++ b/data/without_training/activation2.csv @@ -0,0 +1,11 @@ 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diff --git a/data/without_training/activation3.csv b/data/without_training/activation3.csv new file mode 100644 index 0000000..d384f68 --- /dev/null +++ b/data/without_training/activation3.csv @@ -0,0 +1,9 @@ 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diff --git a/endscreen.py b/endscreen.py new file mode 100644 index 0000000..613d89b --- /dev/null +++ b/endscreen.py @@ -0,0 +1,72 @@ +from manimlib import * +from nn import * +# from backward_teaser import * +from graph_scene import * +from bias_update import * +class EndScreen(Bias,graph_scene): + def construct(self): + self.title = TexText('Thanks for watching!').move_to([-4,3,0]) + NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6,1] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + self.grp.scale(1.2) + subscribe = TexText('SUBSCRIBE!').scale(0.8) + subs_button = RoundedRectangle(height=1,width=3).set_fill(RED,1).set_stroke(RED,1) + like = SVGMobject('assets/like.svg').scale(0.3).next_to(subs_button,LEFT) + like.flip(LEFT) + subs = VGroup(subs_button,subscribe,like).scale(0.8) + subs.to_edge(LEFT) + subscribed = TexText('SUBSCRIBED').scale(0.8) + subbed_button = RoundedRectangle( + height=1, width=3).set_fill(GREY, 1).set_stroke(GREY, 1) + liked = SVGMobject( + 'assets/liked.svg').scale(0.3).next_to(subbed_button,LEFT).set_color(BLUE) + liked.flip(LEFT) + subbed = VGroup(subbed_button, subscribed,liked).scale(0.8).to_edge(RIGHT) + + x = [[random.uniform(0, 1) for x1 in range(6)], [ + random.uniform(0, 1) for x1 in range(6)]] + self.play(Transform(self.title,TexText('Thanks for watching!').to_edge(UP+LEFT))) + self.wait(7) + comments = TexText('Let me know in the comment section!') + self.play(Write(comments)) + self.play(FadeOut(comments)) + self.play(GrowFromCenter(self.grp)) + self.play(Write(subs)) + dot = self.grp.layers[0].copy() + # dot.set_stroke(BLACK,1) + S = TexText('S').move_to(self.grp.layers[0][0][0]).scale(0.5) + U = TexText('U').move_to(self.grp.layers[0][0][1]).scale(0.5) + B = TexText('B').move_to(self.grp.layers[0][0][2]).scale(0.5) + S_END = TexText('S').move_to(self.grp.layers[0][0][3]).scale(0.5) + subs_word = VGroup(S,U,B,S_END) + self.play(Transform(subs,subs_word)) + self.play(*[ApplyMethod(self.grp.layers[1][0][x1].set_fill,WHITE, float(x[0][x1])) for x1 in range(6)], *[ApplyMethod(self.grp.layers[2][0][x1].set_fill, WHITE, float(x[1][x1])) for x1 in range(6)]) + self.grp.layers[3][0].animate.set_fill(WHITE,1) + self.play(ApplyMethod(self.grp.layers[3][0][0].set_fill,WHITE,1)) + self.play(Write(subbed)) + self.wait(2) + grp_fade = VGroup() + for mob in self.mobjects: + grp_fade.add(mob) if mob is not self.title else None + self.play( + FadeOut(grp_fade) + ) + subscribe = TexText('Subscribe!').move_to([-4,-1,0]) + series = TexText('Neural Network\\\ Crash Course Series').move_to([3,-1.5,0]) + self.play(Write(VGroup(subscribe,series))) + self.wait(13) + + + + + + # self.embed() + # self.wait(30) + + def dot(self): + dot_layer = VGroup(*[Dot().move_to(self.grp.layers[0][0][i].get_center()) + for i in range(NeuralNetwork.arguments['layer_sizes'][0])]) + dot_layer.set_fill(WHITE, 0) + return dot_layer diff --git a/explanationall.py b/explanationall.py new file mode 100644 index 0000000..7fabb23 --- /dev/null +++ b/explanationall.py @@ -0,0 +1,796 @@ +# Manim Cairo +from manimlib import * +# ManimGL +# from manimlib import * +import sys +import matplotlib.pyplot as plt +import os.path +from manimlib.mobject.svg.drawings import SpeechBubble +from manimlib.mobject import geometry +# import six,colour,nncc2gl +from nn import * +from applications import * +from bias_update import * +from backward_teaser import * +from graph1 import * +from endscreen import * +class Explanationall(EndScreen): + def construct(self): + # self.title = TexText('Test').to_edge(UP+LEFT) + NeuralNetwork.construct(self) + self.fade_all_except_title() + Application.construct(self) + self.fade_all_except_title() + self.wait(2) + NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2] + NeuralNetwork.add_neurons(self,False) + NeuralNetwork.add_edges(self,False) + NeuralNetwork.group1(self) + self.grp.scale(1.5) + self.edges=self.grp.edges.copy().set_color(YELLOW) + self.first_part() + self.play(self.camera.frame.animate.scale(10).move_to(ORIGIN),FadeIn(self.grp.edges),FadeOut(VGroup(self.decimal))) + self.play(Indicate(self.grp.layers[0],color=WHITE),run_time=3) + self.wait(3) + # self.play(ApplyMethod(self.camera.frame.move_to,ORIGIN)) + # self.add(self.grp.scale(1.5)) + + self.edge_transform_1 = VGroup() + self.connections(0, 0, 6,wait=4, highlight=True) + self.play(Indicate(self.grp.layers[2],color=WHITE)) + self.wait(2) + self.play(FadeOut(self.grp)) + self.weights() + # self.wait() + + # self.shallow_nn_representation() + # self.grp.scale(1.5) + NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2] + NeuralNetwork.add_neurons(self,False) + NeuralNetwork.add_edges(self,False) + NeuralNetwork.group1(self) + self.grp.scale(1.5) + bias=TexText('What is bias?').to_corner(UP+LEFT) + input_label = self.label("x",VGroup()) + self.play(Write(VGroup(self.grp)),Transform(self.title,bias)) + + + self.bias_layer = VGroup(*[ + Circle( + radius=NeuralNetwork.arguments["neuron_radius"], + stroke_color=NeuralNetwork.arguments["neuron_color"], + stroke_width=NeuralNetwork.arguments["neuron_width"], + fill_color=NeuralNetwork.arguments["neuron_fill_color"], + fill_opacity=NeuralNetwork.arguments["neuron_fill_opacity"], + ).scale(1.5).next_to(self.grp.layers[x][0][0],UP) + for x in range(len(NeuralNetwork.arguments['layer_sizes'])-1) + + ] + ) + bias_label = self.label("b",VGroup(),bias=True) + self.play(GrowFromPoint(self.bias_layer,self.bias_layer.get_center()),Write(bias_label)) + bias_edges = VGroup() + x=0 + for l1, l2 in zip(self.grp.layers[:-1], self.grp.layers[1:]): + edge_group = VGroup() + + y=0 + for n2 in (l2.neurons): + # print(x,y) + if y < self.arguments['layer_sizes'][x+1] and x is not len(self.arguments['layer_sizes'])-1:edge_group.add(self.get_edge(self.bias_layer[x], n2, False)) + else:break + y+=1 + + + # edge_group = self.set_bias(x,edge_group) if x is not len(NeuralNetwork.arguments['layer_sizes'])-2 else None + self.play(Write(edge_group), + run_time=0.5) + bias_edges.add(edge_group) + x+=1 + self.wait(3) + self.play(self.bias_layer.animate.set_fill(WHITE,1)) + self.wait(5) + input_grp = VGroup() + for x in range(NeuralNetwork.arguments['layer_sizes'][0]): + label = Tex('0') + label.move_to(self.grp.layers[0][0][x]) + input_grp.add(label) + self.play(Write(input_grp)) + self.wait(10.5) + + grp = VGroup(self.grp,self.bias_layer,bias_edges,bias_label) + self.play(grp.animate.shift(DOWN),input_grp.animate.shift(DOWN)) + input_label.shift(DOWN) + v = VGroup(input_label,bias_label[0]).copy() + forward_propagation = Tex("{{w_0\cdot x_1}}+{{w_1\cdot x_2}}+{{...}}+{{w_3\cdot x_4+b_0}}").shift(2.5*UP) + z_forward_propagation = Tex( + '\sigma({{w_0\cdot x_1}}+{{w_1\cdot x_2}}+{{...}}+{{w_3\cdot x_4+b_0}})').shift(2.5*UP) + self.play(ReplacementTransform(input_grp, input_label)) + self.wait() + without_z = Tex("\sum_{i=1}^nw_ix_i+b").shift(3*UP) + z = Tex('z = ').next_to(without_z,LEFT) + equal = Tex("=").next_to(without_z,RIGHT) + inf = Tex("\infty").next_to(equal,RIGHT) + negativeinf = Tex("-\infty").next_to(equal,RIGHT) + how = TexText('How?').next_to(inf,RIGHT).shift(RIGHT) + self.play(ReplacementTransform(v,forward_propagation)) + self.wait(2) + self.play(Transform(forward_propagation,z_forward_propagation)) + self.wait(5) + self.play(ReplacementTransform(forward_propagation,without_z)) + self.wait(7) + self.play(Write(z)) + self.wait(2) + self.play(FadeIn(VGroup(equal,negativeinf))) + self.wait() + self.play(ReplacementTransform(negativeinf,inf)) + self.wait(5) + # #TODO: activation function: z=wx+b, the values can go up to -inf, +inf, it knows no bounds. so how does the neural network determine which neuron is activated? Thats where activation functions comes in. + self.play(FadeOut(VGroup(inf,self.grp,without_z,z,equal,input_label,bias_edges,bias_label,self.bias_layer))) + # self.embed() + self.wait() + af = TexText('What is activation function?').to_corner(UP+LEFT) + a = TexText('Activation Function').shift(3*LEFT) + arrow = Arrow([0,0,0],[1,0,0]).next_to(a,RIGHT) + ans = TexText('Output of a neuron').next_to(arrow,RIGHT) + self.play(Write(a), Transform(self.title, af)) + self.play(Write(arrow)) + self.play(Write(ans)) + v = VGroup(a, arrow, ans) + self.play(v.animate.shift(2.5*UP)) + self.wait() + circle = Circle(radius=1.5,color=BLUE).shift(DOWN) + r_v = ValueTracker(0) + restricted_value = DecimalNumber(0, num_decimal_places=3, include_sign=False, unit=None) + restricted_value.add_updater(lambda d: d.set_value(r_v.get_value())) + restricted_value.move_to(circle.get_center()) + limit = TexText('Limit: -1 to 1').move_to([-2.5,1,0]) + + self.play(ShowCreation(circle),FadeIn(restricted_value),Write(limit)) + self.play(r_v.set_value, -1,run_time=2.5) + self.wait() + self.play(r_v.set_value, 1,run_time=2.5) + # self.wait(2) + print('--') + self.play(FadeOut(VGroup(circle,restricted_value,limit))) + self.remove(r_v) + + axes = Axes((-5,5),(0,1)).scale(0.5) + sigmoid_graph = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-x)), + use_smoothing=False, + color=YELLOW, + ) + self.play(Write(axes, lag_ratio=0.01, run_time=1)) + non_linear_label = axes.get_graph_label(sigmoid_graph, Text("Non Linear Function").scale(0.4)) + self.play( + ShowCreation(sigmoid_graph), + FadeIn(non_linear_label, RIGHT), + ) + axes1 = Axes(x_range=[-1, 5], + y_range=[-1, 5],width=10).scale(0.4).to_edge(UP+RIGHT) + linear_graph = axes1.get_graph(lambda x: x, + x_range=[-1, 4], color=YELLOW) + linear_label = axes1.get_graph_label( + linear_graph, Text("Linear Function").scale(0.3)) + self.wait(10) + self.play(FadeOut(VGroup(sigmoid_graph,non_linear_label,axes,v))) + grp.shift(0.8*UP+0.5*LEFT) + cat = ImageMobject('assets/cat.png').next_to(self.grp.layers[0],LEFT).scale(0.5) + self.play(Write(self.grp), + FadeIn(cat), + Write(axes1, lag_ratio=0.01, run_time=1), + ShowCreation(linear_graph), + FadeIn(linear_label, RIGHT),run_time=3 + ) + soundwave = SVGMobject( + 'assets/soundwavee.svg').scale(0.5).next_to(self.grp.layers[0], LEFT) + + cross = VGroup(Line([3,2,0],[-3,-2,0],color=RED),Line([-3,2,0],[3,-2,0],color=RED)).move_to(self.grp.get_center()) + self.play(FadeTransform(cat,self.dot()),ShowCreation(cross),run_time=2) + self.wait(5) + self.play(FadeIn(soundwave)) + self.play(FadeTransform( + soundwave, self.dot()), ShowCreation(cross),run_time=2) + self.wait(5) + self.play( + *[FadeOut(mob)for mob in self.mobjects] + # All mobjects in the screen are saved in self.mobjects + ) + self.title = TexText('Types of Activation Function').to_edge(UP+LEFT) + self.play(Write(self.title)) + graph_scene.construct(self) + self.wait() + self.fade_all_except_title() + axes = Axes((-5, 5), (0, 1.2), + y_axis_config={ + "decimal_number_config": { + "num_decimal_places": 1, + } + }, + width=10).scale(0.6).shift(0.5*UP) + sigmoid_graph = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-x)), + use_smoothing=False, + color=YELLOW, + ) + axes1 = Axes((-3, 5), (0, 5), + y_axis_config={ + "decimal_number_config": { + "num_decimal_places": 1, + } + }, + width=10).scale(0.6).shift(0.5*DOWN) + relu_graph = axes1.get_graph( + lambda x: max(0, x), + use_smoothing=False, + color=YELLOW, + ) + axes2 = Axes((-5, 5), (-1.2, 1.2), + y_axis_config={ + "decimal_number_config": { + "num_decimal_places": 1, + } + }, + width=10).scale(0.6).shift(0.5*DOWN) + tanh_graph = axes2.get_graph( + lambda x: (math.exp(x) - math.exp(-x)) / (math.exp(x) + math.exp(-x)), + use_smoothing=False, + color=YELLOW, + ) + + self.play(Write(axes, lag_ratio=0.01, run_time=1)) + non_linear_label = axes.get_graph_label(sigmoid_graph, Text("Sigmoid(Logistic) Function").scale(0.4)) + sigmoid_formula = axes.get_graph_label( + sigmoid_graph, Tex("\\frac{1}{1+e^{-z}}").scale(1.3),direction=RIGHT).shift(2*DOWN+0.5*RIGHT) + # sigmoid_formula = Tex("\\frac{1}{1+e^{-x}}").next_to(axes,DOWN).scale(1.3) + lbl = axes.y_axis.get_number_mobject(0.5,font_size=24) + tip = Line([-0.1,0.5,0],[0.1,0.5,0]) + axes.add_coordinate_labels(x_values=[-5,5],y_values=[]) + self.play( + ShowCreation(sigmoid_graph), + FadeIn(non_linear_label, RIGHT), + Write(sigmoid_formula), + Write(lbl), + Write(tip), + ) + self.wait(6) + zero = axes.x_axis.get_number_mobject(0,font_size=24) + one = axes.y_axis.get_number_mobject(1,font_size=24) + self.play(Write(zero)) + self.play(Write(one)) + self.wait(4) + v_grp = VGroup(sigmoid_graph,non_linear_label,sigmoid_formula,lbl, tip,axes) + v_grp1 = v_grp.copy().shift(4*LEFT).scale(0.8) + self.wait() + NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2] + NeuralNetwork.add_neurons(self,False) + NeuralNetwork.add_edges(self,False) + NeuralNetwork.group1(self) + self.grp.shift(3*RIGHT) + self.play(Transform(v_grp,v_grp1),FadeOut(VGroup(zero,one))) + self.play(Write(self.grp)) + explain_binary_classification = TexText('(Classify a set of elements into 2 groups)').next_to(self.grp,DOWN).scale(0.8) + binary_classification = TexText('Binary Classification').next_to(self.grp,UP).scale(0.8) + cat_label = TexText('Cat').next_to(self.grp.layers[3][0][0]).scale(0.8) + dog_label = TexText('Dog').next_to(self.grp.layers[3][0][1]).scale(0.8) + cat = ImageMobject('assets/cat.png').next_to(self.grp.layers[0],LEFT).scale(0.4).shift(RIGHT*0.8) + self.play(Write(binary_classification)) + self.play(Indicate(self.grp.layers[3]), Write( + VGroup(cat_label, dog_label)), FadeIn(cat)) + self.wait() + self.play(Write(explain_binary_classification)) + self.wait() + x = [[random.uniform(0, 1) for x1 in range(6)], [ + random.uniform(0, 1) for x1 in range(6)]] + self.play(FadeTransform(cat,self.dot())) + self.play(*[ApplyMethod(self.grp.layers[1][0][x1].set_fill, + WHITE, float(x[0][x1])) for x1 in range(6)]) + self.wait() + self.play(*[ApplyMethod(self.grp.layers[2][0][x2].set_fill, + WHITE, float(x[1][x2])) for x2 in range(6)]) + self.wait() + self.play(ApplyMethod(self.grp.layers[3][0][0].set_fill, WHITE, 1)) + self.wait() + relu_label = axes1.get_graph_label(relu_graph, Text("ReLU Function").scale(0.4)) + relu_formula = axes1.get_graph_label( + relu_graph, Tex("max(0,z)").scale(1.3),direction=RIGHT).shift(2.5*DOWN+RIGHT) + relu_title = TexText('Rectified Linear Unit').to_edge(UP).shift(DOWN) + relu_description = TexText('Simple, Effective, Less Computation').next_to(axes1,DOWN) + self.fade_all_except_title() + self.play(Write(axes1, lag_ratio=0.01, run_time=1)) + self.play( + ShowCreation(relu_graph), + Write(relu_title), + # Write(lbl), + # Write(tip), + ) + self.wait() + self.play(FadeIn(relu_label, RIGHT)) + self.wait(2) + self.play(Write(relu_formula)) + self.play(Indicate(relu_formula[0][0:3])) + self.play(Indicate(relu_formula[0][4],scale_factor=1.5)) + self.play(Indicate(relu_formula[0][6]),scale_factor=1.5) + self.wait(2) + self.play(Indicate(relu_formula[0][6]), scale_factor=1.5) + self.wait(2.5) + self.play(Indicate(relu_formula[0][4], scale_factor=1.5)) + self.wait(7) + self.play(Write(relu_description),run_time=3) + self.wait(2) + tanh_label = axes2.get_graph_label( + tanh_graph, Text("Tanh Function").scale(0.4)) + tanh_formula = axes2.get_graph_label( + tanh_graph, Tex("\\frac{(e^z-e^{-z})}{(e^z+e^{-z})}").scale(0.8), direction=RIGHT).shift(1.5*DOWN+0.2*RIGHT) + tanh_title = TexText('Hyperbolic Tangent Function').to_edge(UP).shift(DOWN) + tanh_description = TexText( + 'Used in Binary Classification\\\Performs better than Sigmoid Function').next_to(self.grp, DOWN).scale(0.8) + # self.embed() + NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 4] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + self.grp.shift(3*RIGHT) + relu = VGroup(axes1,relu_graph,relu_formula) + tanh = VGroup(axes2,tanh_graph,tanh_formula) + # self.embed() + self.play(ReplacementTransform(relu,tanh),FadeOut(VGroup(relu_description,relu_title,relu_label))) + self.play(Write(tanh_title)) + self.wait() + self.play(Write(tanh_label)) + self.wait(4) + one = axes2.y_axis.get_number_mobject(1,font_size=24) + negative_one = axes2.y_axis.get_number_mobject(-1,font_size=24) + self.play(Write(negative_one)) + self.play(Write(one)) + self.wait(2) + dot = Dot(color=BLUE) + dot.move_to(axes2.i2gp(0, tanh_graph)) + self.play(GrowFromPoint(dot,dot.get_center(), scale=0.5)) + + x_tracker = ValueTracker(0) + f_always( + dot.move_to, + lambda: axes2.i2gp(x_tracker.get_value(), tanh_graph) + ) + + self.play(x_tracker.animate.set_value(4), run_time=3) + self.play(x_tracker.animate.set_value(-4), run_time=5) + self.wait(2) + # tanh_grp = VGroup(axes2, tanh_graph, tanh_label, + # tanh_formula, tanh_title) + tanh.add(tanh_label) + tanh_grp_copy = tanh.copy().shift(4*LEFT).scale(0.8) + self.play(Transform(tanh,tanh_grp_copy),FadeOut(VGroup(one,negative_one,dot))) + self.remove(x_tracker) + self.play(Write(self.grp)) + self.play(Write(tanh_description)) + self.wait() + self.play(Indicate(self.grp.layers[3])) + + self.wait(5) + self.fade_all_except_title() + NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 4] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + self.grp.scale(1.2).to_edge(RIGHT).shift(LEFT) + question = TexText('Which should you use?').move_to([3,3,0]) + # self.add(question) + softmax = TexText('Softmax Function').to_edge(UP).shift(DOWN) + softmax_formula = Tex('\\text{Softmax}(x)=\\frac{e^z}{\sum e^z}') + self.play(Write(softmax)) + self.play(Write(softmax_formula)) + self.play(softmax_formula.animate.shift(3.5*LEFT)) + self.play(Write(self.grp)) + + softmax_group = self.label('a',VGroup(),3) + self.play(Write(softmax_group)) + softmax_sum = Tex('\sum_{a=0} = 1').next_to(self.grp.layers[2],UP) + softmax_group_copy = softmax_group.copy() + self.wait(5) + self.play(Transform(softmax_group_copy,softmax_sum)) + softmax_application = TexText('Used in Multiclass Classification').next_to(self.grp,DOWN) + self.wait(2) + ans_layer = VGroup(Tex('0.1').scale(0.5).move_to(self.grp.layers[3][0][0]),Tex('0.5').scale(0.5).move_to(self.grp.layers[3][0][1]),Tex('0.2').scale(0.5).move_to(self.grp.layers[3][0][2]),Tex('0.1').scale(0.5).move_to(self.grp.layers[3][0][3])).set_color(BLUE) + self.play(*[Transform(softmax_group[i],ans_layer[i]) for i in range(len(ans_layer))]) + self.wait(5) + self.play(Write(softmax_application)) + self.wait(6) + self.fade_all_except_title() + self.play(Transform(self.title,TexText('How to choose Activation Function').to_edge(UP+LEFT))) + Choose.construct(self) + self.fade_all_except_title() + self.play(Transform(self.title,TexText('Bias: Further Explanation').to_edge(UP+LEFT))) + Bias.construct(self) + self.fade_all_except_title() + EndScreen.construct(self) + self.embed() + + def first_part(self): + #asset, first part + self.title = TexText('What is Neural Network?').to_corner(UP+LEFT) + brain = SVGMobject('assets/brain.svg') + brain.flip(LEFT) + algorithm = TexText('Algorithm').next_to(brain,LEFT) + question = Tex('?').next_to(brain,RIGHT) + ans = TexText("It's an orange.").next_to(self.grp.layers[3][0][0],RIGHT) + question2nd = TexText('What is a neuron?').to_corner(UP+LEFT) + arrow = Arrow([-2,2.5,0],[2,2.5,0]) + self.play(Write(self.title)) + self.wait() + self.play(Write(VGroup(algorithm))) + self.play(DrawBorderThenFill(brain),Write(question)) + self.wait() + self.play(VGroup(algorithm,question,brain).animate.to_edge(LEFT)) + self.wait(2) + self.subset() + # self.embed() + # self.wait() + self.fade_all_except_title() + + x = [[random.uniform(0, 1) for x1 in range(6)], [ + random.uniform(0, 1) for x1 in range(6)]] + + org = SVGMobject('assets/orange2.0.svg') + org.set_color(ORANGE).to_edge(LEFT).flip(LEFT) + + self.hid_layer = Brace(VGroup(self.grp.layers[1],self.grp.layers[2]),UP) + self.in_layer_text = TexText('Input Layer').next_to(self.grp.layers[0],1.5*DOWN) + self.hid_layer_text = self.hid_layer.get_text('Hidden Layer').scale(0.8) + self.out_layer_text = TexText('Output Layer').next_to(self.grp.layers[3],1.5*DOWN) + t = Tex('248').move_to(self.grp.layers[0][0][0]).scale(0.5) + t1 = Tex('148').move_to(self.grp.layers[0][0][1]).scale(0.5) + t2 = Tex('6').move_to(self.grp.layers[0][0][2]).scale(0.5) + t3 = Tex('1').move_to(self.grp.layers[0][0][3]).scale(0.5) + self.texts = VGroup(t,t1,t2,t3) + self.add(self.grp) + org_transform = org.copy() + self.play(DrawBorderThenFill(org)) + self.play(ReplacementTransform(org_transform,self.texts)) + self.wait() + self.play(ShowCreation(arrow)) + self.play(*[ApplyMethod(self.grp.layers[1][0][x1].set_fill, + WHITE, float(x[0][x1])) for x1 in range(6)]) + self.play(*[ApplyMethod(self.grp.layers[2][0][x2].set_fill, + WHITE, float(x[1][x2])) for x2 in range(6)]) + self.wait(4) + self.play(ApplyMethod(self.grp.layers[3][0][0].set_fill,WHITE,1)) + self.wait() + self.play(Write(ans)) + self.play(FadeOut(org),FadeOut(arrow),FadeOut(ans),FadeOut(self.texts)) + self.wait(3) + + #Layer + shade_area = Rectangle(height=FRAME_HEIGHT,width=FRAME_WIDTH-6).to_edge(RIGHT) + shade = BackgroundRectangle(shade_area) + shade_area_hid1 = Rectangle(height=FRAME_HEIGHT,width=5).to_edge(LEFT) + shade_area_hid2 = Rectangle(height=FRAME_HEIGHT,width=3.5).to_edge(RIGHT) + shade_area_out = Rectangle(height=FRAME_HEIGHT,width=FRAME_WIDTH-6).to_edge(LEFT) + shade_hid1 = BackgroundRectangle(shade_area_hid1) + shade_hid2 = BackgroundRectangle(shade_area_hid2) + shade_out = BackgroundRectangle(shade_area_out) + org_transform = org.copy() + + self.play(Write(self.in_layer_text),ShowCreation(shade)) + self.wait(4) + self.play(DrawBorderThenFill(org)) + self.bring_to_back(org) + self.play(ReplacementTransform(org_transform,self.texts)) + self.play(FadeOut(self.texts)) + self.wait(3) + self.play(ShowCreationThenDestruction(self.edges[0])) + self.wait(2) + self.play( GrowFromCenter(self.hid_layer), Write(self.hid_layer_text),ReplacementTransform(shade.copy(),shade_hid2),ReplacementTransform(shade,shade_hid1)) + self.bring_to_back(self.hid_layer) + self.bring_to_back(self.hid_layer_text) + self.wait(2) + self.play(ShowCreationThenDestruction(self.edges[1])) + self.wait(3) + self.play(ShowCreationThenDestruction(self.edges[2])) + self.wait(2) + shade_out_copy = shade_out.copy() + self.play(Write( + self.out_layer_text), ReplacementTransform(shade_hid2, shade_out),ReplacementTransform(shade_hid1, shade_out_copy)) + self.play(Write(ans),FadeOut(shade_out_copy)) + self.wait(2) + grp=self.grp + NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6,6,6, 1] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + self.grp.scale(0.7).to_corner(UP+RIGHT) + + # # #explanation: speech recongition example + self.remove(self.in_layer_text,org,self.hid_layer_text,self.hid_layer) + self.play(FadeOut(VGroup(self.out_layer_text, shade_out, ans))) + grp_fade = VGroup() + print('---') + for mob in self.mobjects: + grp_fade.add(mob) if mob is not self.title else None + print(mob) + # self.embed() + # self.fade_all_except_grp(grp) + # self.add(grp) + self.wait() + self.play(ReplacementTransform(grp, self.grp)) + + explanation = VGroup() + soundwave = SVGMobject('assets/soundwavee.svg').scale(0.5).to_edge(LEFT) + arrow = Arrow([0,0,0],[1,0,0]).next_to(soundwave,RIGHT) + explanation1 = TexText('Frequency/Pitch of Sound').next_to(arrow,RIGHT) + arrow1 = Arrow([0, 0, 0], [1, 0, 0]).next_to(explanation1, RIGHT) + explanation2 = TexText('Phonemes').next_to(arrow1,RIGHT) + explanation20 = TexText('Code').next_to(explanation2,UP) + explanation21 = TexText('C o d e').next_to(explanation2,UP) + arrow2 = Arrow([0,0,0],[0,-1,0]).next_to(explanation2,DOWN) + explanation3 = TexText('Word').next_to(arrow2,DOWN) + arrow3 = Arrow([0, 0, 0], [-1, 0, 0]).next_to(explanation3, LEFT) + explanation4 = TexText('Phrases').next_to(arrow3,LEFT) + arrow4 = Arrow([0, 0, 0], [-1, 0, 0]).next_to(explanation4, LEFT) + output = TexText('Hello World').next_to(arrow4,LEFT) + explanation.add(arrow,explanation1,arrow1,explanation2,arrow2,explanation3,arrow3,explanation4,arrow4) + self.wait(3) + self.play(DrawBorderThenFill(soundwave),Indicate(self.grp.layers[0],color=YELLOW, run_time=2)) + self.wait(3) + self.play(Write(VGroup(arrow,explanation1)),Indicate(self.grp.layers[1],color=YELLOW, run_time=2)) + self.wait(3) + self.play(Write(VGroup(arrow1,explanation2)),Indicate(self.grp.layers[2],color=YELLOW, run_time=2)) + self.wait(9) + self.play(Write(explanation20)) + self.wait(3) + self.play(TransformMatchingTex(explanation20,explanation21,path_arc = PI/2)) + self.wait(4) + self.play(Write(VGroup(arrow2, explanation3)), Indicate( + self.grp.layers[3], color=YELLOW, run_time=2)) + self.wait(2) + self.play(Write(VGroup(arrow3, explanation4)),Indicate(self.grp.layers[4],color=YELLOW, run_time=2)) + self.wait(3.5) + self.play(Write(VGroup(arrow4,output)),Indicate(self.grp.layers[5],color=YELLOW, run_time=2)) + self.wait(8) + hp = TexText('Based on Human Perspective').to_edge(LEFT).shift(2*UP).to_edge(LEFT) + not_exactly = TexText("Not Exactly What's Happening").next_to(hp,DOWN).to_edge(LEFT) + self.play(Write(hp)) + self.wait(9) + self.play(Write(not_exactly)) + self.wait(3) + self.play(FadeOut(VGroup(explanation,output,explanation21))) + self.play(ReplacementTransform(soundwave.copy(),explanation),run_time=2) + self.wait(2) + self.play(Indicate(self.grp.layers[1]),run_time=0.5) + self.play(Indicate(self.grp.layers[2]),run_time=0.5) + self.play(Indicate(self.grp.layers[3]),run_time=0.5) + self.play(Indicate(self.grp.layers[4]), run_time=0.5) + self.play(ReplacementTransform(explanation.copy(),output)) + self.play(Indicate(self.grp.layers[5], color=YELLOW, run_time=2)) + self.wait(1.5) + + grp = self.grp + NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 2] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + self.grp.scale(1.5) + self.fade_all_except_grp(grp) + self.play(ReplacementTransform(grp, self.grp)) + + # self.embed() + self.wait() + self.play(Transform(self.title, question2nd)) + self.wait(12) + self.decimal = DecimalNumber( + 0, num_decimal_places=3, include_sign=False, unit=None) + self.decimal.add_updater(lambda d: d.set_value( + float(self.grp.layers[1][0][3].get_fill_opacities()[0]))) + self.decimal.scale(0.2).move_to( + self.grp.layers[1][0][3].set_fill(WHITE, 0)) + self.play(self.camera.frame.scale, 0.1, + self.camera.frame.move_to, self.grp.layers[1][0][3]) + self.play(FadeOut(self.grp.edges)) + self.bring_to_front(self.decimal) + self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 0)) + self.wait() + self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 1)) + self.wait() + self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 0)) + self.wait() + self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 1)) + self.wait() + + + + + def shallow_nn_representation(self): + NeuralNetwork.arguments['layer_sizes'] = [3,1,1] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False) + NeuralNetwork.group1(self) + l = "x" + + self.grp.scale(3) + self.grp.layers[1][0][0].scale(2) + input_group = VGroup() + + for x in range(NeuralNetwork.arguments['layer_sizes'][0]): + label = Tex(f"{l}_"+"{"+f"{x+1}"+"}") + label.move_to(self.grp.layers[0][0][x]) + input_group.add(label) + y_hat = Tex('\hat{y}') + y_hat.move_to(self.grp.layers[2][0][0]) + sigma = Tex('\sigma').shift(0.5*DOWN) + line = Line(self.grp.edges[0][1],self.grp.edges[1][0]) + log_reg_formula = Tex('w^Tx+b').scale(0.8).shift(0.4*UP) + self.play(Write(self.grp),Write(input_group), Write( + log_reg_formula), Write(line), Write(sigma),Write(y_hat)) + return + + def weights(self): + NeuralNetwork.arguments['layer_sizes'] = [3, 1, 1] + NeuralNetwork.add_neurons(self, False) + NeuralNetwork.add_edges(self, False, False) + NeuralNetwork.group1(self) + l = "x" + weight = TexText('What is weight?').to_corner(UP+LEFT) + self.grp.scale(3) + self.grp.layers[1][0][0].scale(2) + input_group = VGroup() + input_label = TexText('Input').next_to(self.grp.layers[0][0][0], UP) + y_hat_label = TexText('Predicted Output').next_to( + self.grp.layers[2][0][0], UP) + for x in range(NeuralNetwork.arguments['layer_sizes'][0]): + label = Tex(f"{l}_"+"{"+f"{x+1}"+"}") + label.move_to(self.grp.layers[0][0][x]) + input_group.add(label) + y_hat = Tex('\hat{y}') + y_hat.move_to(self.grp.layers[2][0][0]) + shallow_nn = VGroup(self.grp, input_group, y_hat) + self.play(Write(shallow_nn), + Write(input_label), Write(y_hat_label), + Transform(self.title,weight)) + self.bring_to_back(self.grp.layers[0]) + self.play(ZoomInThenZoomOut(self.grp.layers[0], color=WHITE),ZoomInThenZoomOut(input_group)) + self.wait() + self.play(ZoomInThenZoomOut(self.grp.layers[1],color=WHITE)) + dashed_line_group = VGroup(*[DashedLine(self.grp.edges[0][x].get_center(), [0, 2.5, 0]) for x in range( + NeuralNetwork.arguments['layer_sizes'][0])], VGroup(DashedLine(self.grp.edges[1][0].get_center(), [0, 2.5, 0]))) + w_layer_group = VGroup() + l = "w" + synapse = TexText('Edges (Connections)').next_to( + dashed_line_group[0].get_end(), UP) + for x in range(NeuralNetwork.arguments['layer_sizes'][0]): + label = Tex(f"{l}_"+"{"+f"{x+1}"+"}") + label.move_to(self.grp.edges[0][x]).shift(0.4*UP) + w_layer_group.add(label) + self.wait() + self.play(Write(dashed_line_group)) + self.play(Write(synapse)) + self.wait() + self.play(FadeOut(synapse), FadeOut(dashed_line_group)) + self.wait() + self.play(Write(w_layer_group)) + self.wait(1.5) + self.play(ZoomInThenZoomOut(w_layer_group[0])) + self.wait(4) + self.play(ZoomInThenZoomOut(self.grp.edges[0])) + self.wait(7) + arrow = Arrow([0, 0, 0], [0, 1, 0]).next_to(w_layer_group[0], RIGHT) + arrow3 = Arrow([0, 1, 0], [0, 0, 0]).next_to(w_layer_group[0], RIGHT) + + # zero = Tex('0').next_to(w_layer_group[0], RIGHT) + v = ValueTracker(0) + x1 = DecimalNumber(0, num_decimal_places=1, include_sign=False, unit=None).move_to( + self.layers[0][0][0].get_center()).scale(0.5) + z = DecimalNumber(0, num_decimal_places=2, + include_sign=False, unit=None) + x1.add_updater(lambda m: m.set_value(v.get_value())) + z.add_updater(lambda d: d.set_value( + float(self.grp.layers[1][0][0].get_fill_opacities()[0]))) + + self.grp.layers[1][0][0].set_fill(WHITE, 0) + self.play(ZoomInThenZoomOut(self.grp.layers[0][0][0])) + self.bring_to_back(self.grp.layers[0]) + self.wait() + self.play(ZoomInThenZoomOut(self.grp.layers[1][0][0],color=WHITE)) + self.wait(2) + self.play(Write(arrow), ReplacementTransform(input_group[0], x1)) + self.add(z) + + self.play(v.animate.set_value(7), + ApplyMethod(self.grp.layers[1][0][0].set_fill, WHITE, 0.8), run_time=2) + self.wait(2) + self.grp.layers[1][0][0].set_fill(WHITE, 0) + v.set_value(0) + self.play(Transform(arrow, arrow3)) + self.play(v.animate.set_value(7), + ApplyMethod(self.grp.layers[1][0][0].set_fill, WHITE, 0.4), run_time=2) + self.wait(3) + self.remove(x1, z,v) + self.play(FadeOut(VGroup(self.grp, arrow, w_layer_group, input_group,input_label,y_hat_label,y_hat))) + # self.play(ReplacementTransform(arrow,zero),FadeOut(arrow1)) + # self.grp.layers[1][0][0].set_fill(WHITE,0) + # self.play(ShowCreationThenFadeOut(arrow2),run_time=0.5) + # arrow2.flip(LEFT) + # self.play(ShowCreationThenFadeOut(arrow2),run_time=0.5) + # self.wait() + + # self.play(ReplacementTransform(zero,arrow3)) + # self.play(ApplyMethod(self.grp.layers[1][0][0].set_fill, WHITE, 0.2)) + + def nn_representation(self): + return + + def label(self,l,group,layer=0,edges=False,bias=False): + if bias is True: + for x in range(len(NeuralNetwork.arguments['layer_sizes'])-1): + label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8) + label.move_to(self.bias_layer[x]) + group.add(label) + else: + for x in range(NeuralNetwork.arguments['layer_sizes'][layer]): + label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8) + + label.move_to(self.grp.edges[layer][x]).shift( + 0.4*UP) if edges is True else label.move_to(self.grp.layers[layer][0][x]) + group.add(label) + + return group + + def connections(self,x,y,z,wait=1,highlight=False): + i=0 + while i != NeuralNetwork.arguments['layer_sizes'][0]: + self.edge_transform_1.add(self.edges[0][x]) + print(0,x) + i+=1 + x+=NeuralNetwork.arguments['layer_sizes'][1] + + + self.play(ShowCreationThenDestruction(self.edge_transform_1)) + self.wait(wait) + if highlight is True: + self.play(Indicate(self.grp.layers[1][0][0],color=WHITE)) + self.wait(2) + self.play(*[ShowCreationThenDestruction(self.edges[1][i]) for i in range(y,z)]) + + def dot(self): + dot_layer = VGroup(*[Dot().move_to(self.grp.layers[0][0][i].get_center()) for i in range(NeuralNetwork.arguments['layer_sizes'][0])]) + dot_layer.set_fill(WHITE,0) + return dot_layer + + def fade_all_except_title(self): + grp_fade = VGroup() + print('---') + for mob in self.mobjects: + grp_fade.add(mob) if mob is not self.title else None + print(mob) + self.play( + FadeOut(grp_fade) + ) + + def subset(self): + nn = TexText('Neural Network') + dl = TexText('Deep Learning').shift(1.3*UP) + ml = TexText('Machine Learning').shift(2.3*UP) + c = Circle(stroke_color=RED).stretch(2, dim=0) + c1 = Circle(radius=1.5, stroke_color=GREEN).stretch( + 2, dim=0).shift(0.5*UP) + c2 = Circle(radius=2, stroke_color=BLUE).stretch(2, dim=0).shift(UP) + subset = VGroup(nn,dl,ml,c,c1,c2).scale(0.8).to_edge(RIGHT).shift(DOWN) + self.play(Write(VGroup(c, nn))) + self.wait() + self.play(Write(VGroup(c1, dl))) + self.wait() + self.play(Write(VGroup(c2, ml))) + + def fade_all_except_grp(self,grp): + grp_fade = VGroup() + for mob in self.mobjects: + grp_fade.add(mob) if mob is not grp else None + self.play( + FadeOut(grp_fade) + ) + +# color detection +# edge detection +# group detection +#TODO diff --git a/graph_scene.py b/graph_scene.py new file mode 100644 index 0000000..84672fc --- /dev/null +++ b/graph_scene.py @@ -0,0 +1,60 @@ +from manimlib import * + +class graph_scene(Scene): + def construct(self): + + axes = Axes((-5, 5), (0, 1), + y_axis_config={ + "decimal_number_config": { + "num_decimal_places": 1, + } + }, + width=10).scale(0.4).shift(0.5*UP) + sigmoid_graph = axes.get_graph( + lambda x: 1.0 / (1.0 + math.exp(-x)), + use_smoothing=False, + color=YELLOW, + ) + axes1 = Axes((-3, 5), (0, 5), + y_axis_config={ + "decimal_number_config": { + "num_decimal_places": 1, + } + }, + width=10).scale(0.4).shift(0.5*DOWN) + relu_graph = axes1.get_graph( + lambda x: max(0, x), + use_smoothing=False, + color=YELLOW, + ) + axes2 = Axes((-5, 5), (-1, 1), + y_axis_config={ + "decimal_number_config": { + "num_decimal_places": 1, + } + }, + width=10).scale(0.4).shift(0.5*DOWN) + tanh_graph = axes2.get_graph( + lambda x: (math.exp(x) - math.exp(-x)) / + (math.exp(x) + math.exp(-x)), + use_smoothing=False, + color=YELLOW, + ) + + sigmoid_name = TexText('Sigmoid Function').next_to(axes,DOWN) + relu_name = TexText('ReLU Function').next_to(axes1,DOWN) + tanh_name = TexText('Tanh Function').next_to(axes2,DOWN) + softmax_formula = Tex('\\text{Softmax}(x)=\\frac{e^z}{\sum e^z}') + softmax_name = TexText('Softmax Function').next_to(softmax_formula,DOWN) + + sigmoid_grp = VGroup(axes,sigmoid_graph,sigmoid_name).shift(1.2*UP+3*LEFT) + relu_grp = VGroup(axes1,relu_graph,relu_name).shift(2.2*UP+3*RIGHT) + tanh_grp = VGroup(axes2,tanh_graph,tanh_name).shift(1.5*DOWN+3*LEFT) + softmax_grp = VGroup(softmax_name,softmax_formula).shift(2*DOWN+3*RIGHT) + self.play(Write(sigmoid_grp)) + self.wait() + self.play(Write(relu_grp)) + self.wait() + self.play(Write(tanh_grp)) + self.wait() + self.play(Write(softmax_grp)) diff --git a/nn.py b/nn.py new file mode 100644 index 0000000..916c2ac --- /dev/null +++ b/nn.py @@ -0,0 +1,407 @@ +from manimlib import * +import random +import pandas as pd +from sklearn.preprocessing import MinMaxScaler +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score +red1 = "#FF0000" +blue1 = "#0000ff" +light_grey1 = "#6b6b78" +green1 = "#00FF00" +violet1 = "#FF00FF" + + +class NeuralNetwork(Scene): + arguments = { + "network_size": 1, + "network_position": ORIGIN, + "layer_sizes": [6, 12, 10, 8, 6, 2], + "layer_buff": 1.25*LARGE_BUFF, + # "layer_buff": LARGE_BUFF, + "neuron_radius": 0.15, + "neuron_color": WHITE, + "neuron_width": 2, + "neuron_fill_color": BLACK, + "neuron_fill_opacity": 1, + "neuron_buff": MED_SMALL_BUFF, + "edge_color": YELLOW_B, + "edge_width": 2, + "edge_opacity": 1, + "layer_label_color": WHITE, + "layer_label_size": 0.5, + "neuron_label_color": WHITE, + } + + # def __init__(self): + # self.nPclass = 0 + # self.nSex = 0 + # self.nAge = 0 + # self.nSibSp = 0 + # self.Fare = 0 + # self.Embarked = 0 + # self.Age_was_missing = 0 + # def setup(self): + # MovingCameraScene.setup(self) + + def construct(self): + # self.camera_frame.save_state() + + self.add_neurons() + self.add_edges() + # self.label_stuff() + self.group1() + # self.preload() + # self.grp.n = self.n + # self.grp.output_label = self.output_label + # self.grp.add(self.n) + # self.grp.add(self.output_label) + self.intro() + self.grp.n = self.n + self.grp.add(self.n) + + # self.content() + # grp = VGroup() + # grp.layers = self.layers + # grp.edges = self.edge_groups + # grp.n = self.n + # grp.output_label = self.output_label + # grp.add(self.layers) + # grp.add(self.edge_groups) + # grp.add(self.n) + # grp.add(self.output_label) + # grp.shift(LEFT) + # self.play(ApplyMethod(grp.scale,0.72)) + # self.label_layers() + # box = Rectangle(height=30, color=YELLOW, stroke_width=4) + # box.surround(self.n) + # self.play(ShowCreation(box)) + # grp1 = VGroup() + # grp1.add(self.layers[1][0]) + # grp1.add(self.layers[2][0]) + # grp1.add(self.layers[3][0]) + # grp1.add(self.layers[4][0]) + # grp1.add(self.output_label) + # bland = Rectangle(height=20,fill_color=BLACK, fill_opacity=0.4, stroke_opacity=0) + # bland.surround(grp1) + # self.play(ShowCreation(bland)) + return self.grp + + def group1(self): + grp = VGroup() + grp.layers = self.layers + grp.edges = self.edge_groups + grp.add(self.layers) + grp.add(self.edge_groups) + self.grp = grp + return grp + + def intro(self): + grp1 = self.grp.copy().scale(0.8).shift(3*RIGHT) + # self.play(FadeIn(self.grp)) + # self.play(ShowCreation(grp1)) + self.play(Transform(self.grp, grp1)) + neural = TexText("Neural") + title = VGroup( + neural, + TexText("Network Crash"), + TexText("Course") + ).arrange(DOWN, aligned_edge=LEFT).shift(3.5*LEFT).scale(1.1) + line1 = Line(title.get_corner(UP+LEFT), neural.get_corner(UP+RIGHT), + color=YELLOW_E, stroke_width=5).next_to(title, UP).shift(LEFT) + line = Line(title.get_corner(DOWN+LEFT), title.get_corner(DOWN+RIGHT), + color=YELLOW_E, stroke_width=5).next_to(title, DOWN) + self.play(Write(title), ShowCreation(line), ShowCreation(line1)) + ml = Text('Machine Learning:', font="consolas", font_size=24) + self.quote = VGroup( + ml, + Text("Programming with examples,", font="consolas", + font_size=24, t2c={"examples": YELLOW}), + Text("not instructions.", font="consolas", + font_size=24, t2c={"instructions": YELLOW}), + Tex('-\\ Kyle\\ Mcdonald') + ).arrange(DOWN, aligned_edge=LEFT).to_edge(LEFT).shift(0.5*RIGHT) + + # mob1.align_to(mob2, alignment_vect = RIGHT) + self.introgrp = VGroup(title, line, line1) + title.add(line1) + title.add(line) + self.title = title + self.animation(self.grp) + + def add_neurons(self, animation=True): + self.loop = False + layers = VGroup(*[self.get_layer(size) + for size in NeuralNetwork.arguments["layer_sizes"]]) + layers.arrange(RIGHT, buff=NeuralNetwork.arguments["layer_buff"]) + layers.scale(NeuralNetwork.arguments["network_size"]) + # self.layers is layers, but we can use it throughout every method in our class + # without having to redefine layers each time + self.layers = layers + layers.shift(NeuralNetwork.arguments["network_position"]) + self.play(FadeInFromPoint(layers, ORIGIN), + run_time=2) if animation is True else None + # self.add(layers) + + def get_layer(self, size): + layer = VGroup() + n_neurons = size + neurons = VGroup(*[ + Circle( + radius=NeuralNetwork.arguments["neuron_radius"], + stroke_color=NeuralNetwork.arguments["neuron_color"], + stroke_width=NeuralNetwork.arguments["neuron_width"], + fill_color=NeuralNetwork.arguments["neuron_fill_color"], + fill_opacity=NeuralNetwork.arguments["neuron_fill_opacity"], + ) + for i in range(n_neurons) + ]) + neurons.arrange(DOWN, buff=NeuralNetwork.arguments["neuron_buff"]) + layer.neurons = neurons + layer.add(neurons) + if size == 6 and self.loop == False: + self.neurons1 = neurons + self.loop = True + return layer + + def edge_security(self): + self.edge_groups = VGroup() + for l1, l2 in zip(self.layers[:-1], self.layers[1:]): + edge_group = VGroup() + for n1, n2 in it.product(l1.neurons, l2.neurons): + edge = self.get_edge(n1, n2) + edge_group.add(edge) + self.edge_groups.add(edge_group) + + def add_edges(self, animation=True, color=True): + self.edge_groups = VGroup() + for l1, l2 in zip(self.layers[:-1], self.layers[1:]): + edge_group = VGroup() + for n1, n2 in it.product(l1.neurons, l2.neurons): + # print(x,y) + edge = self.get_edge(n1, n2, color) + edge_group.add(edge) + + # edge_group = self.set_bias(x,edge_group) if x is not len(NeuralNetwork.arguments['layer_sizes'])-2 else None + self.play(Write(edge_group), + run_time=0.5) if animation is True else None + self.edge_groups.add(edge_group) + + def get_edge(self, neuron1, neuron2, color): + colors = [red1, blue1, blue1] + r = random.randint(0, len(colors)-1) + # if y % self.arguments['layer_sizes'][x+1] == 0 and x is not len(self.arguments['layer_sizes'])-2: + # print(x,y) + # color = GREY + # else: + # color = colors[r] + # print(y%NeuralNetwork.arguments['layer_sizes'][x+1] != 0,x,y) + return Line( + neuron1.get_center(), + neuron2.get_center(), + # buff=1.25*NeuralNetwork.arguments["neuron_radius"], + buff=NeuralNetwork.arguments["neuron_radius"], + + stroke_color=GREY if color is False else colors[r], + stroke_width=NeuralNetwork.arguments["edge_width"], + stroke_opacity=NeuralNetwork.arguments["edge_opacity"] + ) + + def set_bias(self, x): + x1 = 0 + # return None + for i in range(self.arguments['layer_sizes'][x]): + print(x, x1) + self.edge_groups[x][x1].set_color(GREY) + x1 += self.arguments['layer_sizes'][x+1] + # self.edge_groups[0][0].set_color(GREY) + + def animation(self, grop, backward_teaser=False, L=1): + input_data1 = self.read_data('test.csv', False) + # print(input_data) + output = self.read_data('my_submission.csv', False) + + # output_data = self.read_data('ans.csv',False) + # for i in range(len(input_data1)): + # y=None + if backward_teaser is True: + y = np.array(pd.read_csv( + 'data/without_training/output_real.csv').T.reset_index().drop('index', axis=1)[0]) + y_hat = np.array(pd.read_csv( + 'data/without_training/output.csv').T.reset_index().drop('index', axis=1)[0]) + p = np.zeros((1, y_hat.shape[0])) + for i in range(0, y_hat.shape[0]): + if y_hat[i] > 0.5: + p[0, i] = 1 + else: + p[0, i] = 0 + print(y[0]) + self.ans = Tex(str(y[1])).next_to( + self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT).shift(RIGHT) + + self.activate = False + ans_output = False + # always(self.ans.next_to, + # self.grp.layers[len(self.arguments['layer_sizes'])-1], 2*RIGHT) + for i in range(L): + self.input_data(input_data1.T[i], True, backward_teaser=True, y=y, i=i+1) if backward_teaser is True else self.input_data( + input_data1.T[i], True) + + # self.wait() + for i1 in range(4): + df = self.read_data('activation'+str(i1+1)+'.csv', + True) if backward_teaser is False else self.read_data('without_training/activation'+str(i1+1)+'.csv', True) + # for x in range(len(df.T)): + # print(df[x][i]) + self.play(*[ApplyMethod(grop.layers[i1+1][0][x].set_fill, + WHITE, float(df[x][i])) for x in range(len(df.T))]) + if i1 == 1 and backward_teaser is False: + print("DEY") + self.play(TransformMatchingParts( + self.title, self.quote, transform_mismatches=True)) + + print(output['Survived'][i], 6) + self.output_data(output['Survived'][i], len(self.arguments['layer_sizes'])-1) if backward_teaser is False else self.output_data( + int(list(p[0])[i+1]), len(self.arguments['layer_sizes'])-1, y[i+1]) + if backward_teaser is True: + # self.wait() + self.grp.layers.set_fill(WHITE, 0) + self.play(FadeOut(self.fade_out)) + if backward_teaser is True and i == 1: + accuracy = TexText('Accuracy: 38\%').move_to([4, 3, 0]) + self.play(Write(accuracy)) if ans_output is False else None + ans_output = True + self.wait() + weights_random = TexText('Weights are initialized randomly!').next_to( + self.grp.layers[2], UP).to_edge(LEFT).shift(RIGHT+0.5*DOWN) + grp = self.grp.copy() + grp.shift(DOWN).scale(0.8) + n = self.n.copy() + for i in range(6): + n[i].move_to(self.grp.layers[0][0][i].get_center()) + self.play(self.grp.animate.shift(DOWN).scale(0.8), accuracy.animate.shift(2.5*DOWN), self.ans.animate.next_to( + grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT).shift(RIGHT), Transform(self.n, n)) + self.play(Write(weights_random)) + self.play( + Write(TexText('= Guessing Game!').next_to(weights_random, RIGHT))) + #self.play( self.camera_frame.scale,0.05,self.camera_frame.move_to,self.layers[0][0][0]) + #self.wait() + + def preload(self): + input_data1 = self.read_data('test.csv', False) + self.input_data(input_data1.T[0], False) + print(1) + + def label_layers(self): + + braces = Brace(self.layers[0], LEFT) + self.t = braces.get_text("Input Layer").scale(0.8).shift(0.5*RIGHT) + # t = TexText("Input Layer", tex_to_color_map={'Input': WHITE}).next_to(braces,LEFT).scale(0.8) + self.play(GrowFromCenter(braces), Write(self.t)) + self.wait() + grp = VGroup() + # for i in range(4): + # grp.add(self.layers[i+1][0][0].get_center()) + line = Line(self.grp.layers[1][0][0].get_center( + ), self.grp.layers[4][0][0].get_center()) + + braces1 = Brace(line, direction=line.copy().rotate( + PI / 2).get_unit_vector()) + t1 = braces1.get_text("Hidden Layer").scale(0.8).shift(0.5*RIGHT) + + self.play(GrowFromCenter(braces1), Write(t1)) + braces2 = Brace(self.grp.output_label, RIGHT) + t2 = braces2.get_text("Output Layer").scale(0.8).shift(0.5*LEFT) + self.play(GrowFromCenter(braces2), Write(t2)) + + def read_data(self, fil, activation): + foo = pd.read_csv('data/'+fil) + if activation == True: + scaler = MinMaxScaler() + d = scaler.fit_transform(foo.T) + scaled_df = pd.DataFrame(d) + scaled_df, scaled_df_selected = train_test_split( + scaled_df, test_size=0.1, random_state=42) + + else: + + scaled_df, scaled_df_selected = train_test_split( + foo, test_size=0.1, random_state=42) + + return scaled_df_selected.reset_index().drop('index', axis=1) + + def input_data(self, a, animate, backward_teaser=False, y=None, i=None): + + n = VGroup() + for s in a: + t = Tex(str(round(int(s), 2))) + n.add(t) + n.arrange(DOWN, buff=0.3) + n.next_to(self.grp.layers, LEFT) + ans = Tex(str(y[i])).next_to( + self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT).shift(RIGHT) if backward_teaser is True else None + print(ans, 'ans') if backward_teaser is True else None + n1 = n.copy() + n1.scale(0.2) + for i in range(6): + n1[i].move_to(self.neurons1[i].get_center()) + if animate is True: + try: + self.play(FadeOut(self.n)) + except: + pass + print('hello') + if backward_teaser is True: + self.play(Transform(n, n1), Write(self.ans)) if self.activate is False else self.play( + Transform(n, n1), Transform(self.ans, ans)) + self.activate = True + else: + self.play(Transform(n, n1)) + self.n = n + else: + self.n = n1 + + def label_stuff(self): + a = [str(random.randint(1, 10)) for i in range(2)] + n = VGroup() + survived = Tex('Survive').scale(0.8) + not_survived = Tex('Not Survive').scale(0.8) + n.add(survived) + n.add(not_survived) + n.arrange(DOWN, buff=MED_SMALL_BUFF) + n.next_to(self.layers, RIGHT) + self.play(Write(n)) + title = TexText("Neural Network of Titanic Dataset", + tex_to_color_map={'Neural': WHITE}).to_edge(UP) + self.play(Write(title)) + self.output_label = n + + def output_data(self, a, layer, y=None): + print(a, layer, 'output') + if self.arguments['layer_sizes'][-1] != 1 and y is None: + self.layers[layer][0][0].set_fill(WHITE, 0) + self.layers[layer][0][1].set_fill(WHITE, 0) + self.play(ApplyMethod(self.layers[layer][0][a].set_fill, WHITE, 1)) + elif self.arguments['layer_sizes'][-1] is 1 and y is not None: + not_equal = Tex('!=', color=RED).next_to( + self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT) + equal = Tex('==', color=GREEN).next_to( + self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT) + self.play(ApplyMethod(self.layers[layer][0][0].set_fill, WHITE, a)) + self.play(Write(not_equal)) if a != y else self.play( + Write(equal)) + self.wait() + self.fade_out = not_equal if a != y else equal + + def content(self): + content = VGroup( + TexText("Course Overview:"), + TexText("1. What is Neural Network?"), + TexText("2. Neuron"), + TexText('3. Weights,'), + TexText('Bias and Connections').shift(RIGHT), + TexText("4. Layers") + ).arrange(DOWN, aligned_edge=LEFT).to_edge(UP+LEFT) + self.grp.add(self.t) + + self.play(ReplacementTransform(self.t, content)) + self.grp.remove(self.t) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..f96ebfe --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +manimgl +scikit-learn +numpy +pandas