Real-World Python Neural Nets Tutorial (Image Classification w/ CNN) | Tensorflow & Keras 

sup keith galli, i need help trying to fix a bug in this code. This is a game where there is a player and it has to dodge the enemies. But for some reason I still die if i haven't touced any of the enemies. How do you fix that bug? import turtle import random import time from random import randint wn = turtle.Screen() wn.bgcolor('black') wn.setup(600, 800) x = random.randint(-290, 290) y = random.randint(395, 500) x1 = -290 x2 = 290 y1 = 395 y2 = 1000 pm = 10 em = 5 ed = 44 s = 1 score = 0 p = turtle.Turtle() p.hideturtle() p.penup() p.speed(0) p.color('red') p.shape('circle') p.shapesize(stretch_wid = 2, stretch_len = 2) p.goto(0, -350) p.showturtle() enemy_colors = ['darkblue', 'cyan', 'green', 'yellow', 'purple', 'pink', 'brown', 'white', 'orange', 'lightgreen', 'blue'] random_color = random.choice(enemy_colors) e1 = turtle.Turtle() e1.hideturtle() e1.color(random_color) e1.penup() e1.speed(0) e1.shape('square') e1.shapesize(stretch_wid=2, stretch_len=2) e1.goto(x, y) e1.showturtle() e1.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e2 = turtle.Turtle() e2.hideturtle() e2.color(random_color) e2.penup() e2.speed(0) e2.shape('square') e2.shapesize(stretch_wid=2, stretch_len=2) e2.goto(x, y) e2.showturtle() e2.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e3 = turtle.Turtle() e3.hideturtle() e3.penup() e3.speed(0) e3.color(random_color) e3.shape('square') e3.shapesize(stretch_wid=2, stretch_len=2) e3.goto(x, y) e3.showturtle() e3.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e4 = turtle.Turtle() e4.hideturtle() e4.penup() e4.speed(0) e4.color(random_color) e4.shape('square') e4.shapesize(stretch_wid=2, stretch_len=2) e4.goto(x, y) e1.showturtle() e4.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e5 = turtle.Turtle() e5.hideturtle() e5.penup() e5.speed(0) e5.color(random_color) e5.shape('square') e5.shapesize(stretch_wid=2, stretch_len=2) e5.goto(x, y) e5.showturtle() e5.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e6 = turtle.Turtle() e6.hideturtle() e6.penup() e6.speed(0) e6.color(random_color) e6.shape('square') e6.shapesize(stretch_wid=2, stretch_len=2) e6.goto(x, y) e6.showturtle() e6.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e7 = turtle.Turtle() e7.hideturtle() e7.penup() e7.speed(0) e7.color(random_color) e7.shape('square') e7.shapesize(stretch_wid=2, stretch_len=2) e7.goto(x, y) e7.showturtle() e7.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e8 = turtle.Turtle() e8.hideturtle() e8.penup() e8.speed(0) e8.color(random_color) e8.shape('square') e8.shapesize(stretch_wid=2, stretch_len=2) e8.goto(x, y) e8.showturtle() e8.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e9 = turtle.Turtle() e9.hideturtle() e9.penup() e9.speed(0) e9.color(random_color) e9.shape('square') e9.shapesize(stretch_wid=2, stretch_len=2) e9.goto(x, y) e9.showturtle() e9.right(90) random_color = random.choice(enemy_colors) x = random.randint(x1, x2) y = random.randint(y1, y2) e10 = turtle.Turtle() e10.hideturtle() e10.penup() e10.speed(0) e10.color(random_color) e10.shape('square') e10.shapesize(stretch_wid=2, stretch_len=2) e10.goto(x, y) e10.showturtle() e10.right(90) pen = turtle.Turtle() pen.hideturtle() pen.penup() pen.goto(0, 360) pen.color('white') def move_right(): x = p.xcor() x += pm if x > 290: x = 290 p.setx(x) def move_left(): x = p.xcor() x -= pm if x < -290: x = -290 p.setx(x) def move_up(): y = p.ycor() y += pm if y > 390: y = 390 p.sety(y) def move_down(): y = p.ycor() y -= pm if y < -390: y = -390 p.sety(y) wn.listen() wn.onkeypress(move_left, 'Left') wn.onkeypress(move_right, 'Right') wn.onkeypress(move_up, 'Up') wn.onkeypress(move_down, 'Down') run = True while run: wn.update() em += 0.04 e1.fd(em) e2.fd(em) e3.fd(em) e4.fd(em) e5.fd(em) e6.fd(em) e7.fd(em) e8.fd(em) e9.fd(em) e10.fd(em) if e1.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e1.color(random_color) e1.goto(100000000000, 100000000000) e1.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e2.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e2.color(random_color) e2.goto(100000000000, 100000000000) e2.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e3.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e3.color(random_color) e3.goto(100000000000, 100000000000) e3.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e4.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e4.color(random_color) e4.goto(100000000000, 100000000000) e4.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e5.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e5.color(random_color) e5.goto(100000000000, 100000000000) e5.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e6.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e6.color(random_color) e6.goto(100000000000, 100000000000) e6.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e7.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e7.color(random_color) e7.goto(100000000000, 100000000000) e7.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e8.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e8.color(random_color) e8.goto(100000000000, 100000000000) e8.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e9.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e9.color(random_color) e9.goto(100000000000, 100000000000) e9.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if e10.ycor() < -395: x = random.randint(x1, x2) y = random.randint(y1, y2) random_color = random.choice(enemy_colors) e10.color(random_color) e10.goto(1000000000000, 100000000000) e10.goto(x, y) score += 1 pen.clear() pen.write('{}'.format(score), align='center', font=('courier', 24, 'normal')) if p.distance(e1) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e2) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e3) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e4) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e5) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e6) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e7) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e8) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e9) < ed: time.sleep(s) print('Score:', score) quit() if p.distance(e10) < ed: time.sleep(s) print('Score:', score) quit() wn.mainloop()

I have done andrew ng's machine learning course. It would be awesome if you could explain how to apply those concepts to python and start machine learning in python.

Another series of real "business" world data science tasks (using pandas and machine learning together!) would be awesome :D I'm from a non-IT background and the last series you posted actually helped me a lot at my work!!! Thanks a lot :)

Can you try to do OpenGL

Maybe a video on GANs would be cool?

Sorry for keeping you waiting! I appreciate your support 🙌

Thanks man! Big fan of your videos :)

You're welcome :). Would love to visit Morocco some day!

@@KeithGalli you're welcome, I will be happy to show you around.

You're welcome!

Glad that you enjoy them and happy to hear my English is easy enough to understand! :)

Sorry to hear that covid cases are increasing over there :/. Hope you stay safe & healthy as well. We all will get through this!

@@KeithGalli Yes Keith... We will fight & win ✌️

@@MrKrishnalovesyou Bht hi ache bhai, mjaa hi aagya

That is easily done using Streamlit, checkout this video. m.ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-aAQe8plGy1M.html

That is something that I want to do, but I don't have it on the immediate roadmap. Think it would require a decent amount of planning to do properly and want to focus on some other video topics before diving into PyTorch.

@@KeithGalli gonna keep waiting! appreciate your work)

Same here too eagerly waiting for a deep learning pytorch video examples from our man Keith Gaili

Ultimately we could have kept any of the color channels and the performance would remain roughly the same. Each of the color channels would produce a pretty similar grey scale image. No real reason I took red specifically other than I thought it made sense to just use index 0.

@@KeithGalli ok thanks, so far I have always seen converting the color image to rgb upfront so I was wondering.

I think you could pass it in as predict(np.array(test_images[0])) and it will work. Glad you have liked some of my videos! :)

@@KeithGalli I do this: print("Predict: ", np.round(best_model.predict(np.array(test_images[0])))) and the result is still the same as below "ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 300, 1]"

You should try this : best_model.predict(np.array(test_images))[your_number] and it'll work!!

@@azzmaniac4139 When I'm prediction the model based on how you told it's returning an ndarray instead of the label, any idea how to tackle it?

I have a similar video that is already filmed going over similar stuff. It got delayed by the sponsor so I'm not a exactly sure when I can post it

I personally like using an ImageGenerator & the flow from directory methods in Keras. Here is a link you can read more about that: datascience.stackexchange.com/questions/65979/what-is-the-correct-way-to-call-keras-flow-from-directory-method

To save some time.

@@joseph3036 Is there an ideal number for the total epochs one must run?

10 epochs have worked well for me so far, too many epochs will cause overfitting. stackoverflow.com/questions/47421951/keras-optimal-epoch-selection