lexfridman / mit-deep-learning Goto Github PK

can i translate this repo to Chinese?

Hi there,
In the part1 of deep_learning_basic lession, this cell i think you displayed the RMSE value in validation set. It should be the new 'rmse' variable instead.

X_test = scaler.transform(X_test)
mse, _, _ = model.evaluate(X_test, y_test)
rmse = np.sqrt(mse)
print('Root Mean Square Error on test set: {}'.format(round(rmse_final, 3)))

This will be great for learning. I googled and it seemed to require tensorboard, not sure how easy this is if run in google colab.

Hi. I tried to run DeepLearningBasics notebook on Colab.
Below are some of the issues I came across:

The code to download mnist_dream.mp4 video when using Colab doesn't work if ran as is:

mnist_dream_path = urllib.request.urlretrieve(os.path.join(deep_repo_url, 'mit_driveseg_sample.mp4')[0]

deep_repo_url is not declared anywhere and the video mentioned in the code is incorrect

I tried below two versions of code to download the video and both worked:

 !wget https://github.com/lexfridman/mit-deep-learning/tree/master/tutorial_deep_learning_basics/images/mnist_dream.mp4

import urllib
urllib.request.urlretrieve("https://github.com/lexfridman/mit-deep-learning/tree/master/tutorial_deep_learning_basics/images/mnist_dream.mp4","mnist_dream.mp4")

The code ret, img = cap.read() returns the tuple False, None. There seems to be some problem with VideoCapture() on Colab.

Since the img is not being returned as expected, the rest of the code to do inference on mnist_dream.mp4 frames isn't working.

I remember facing problems in the past when using OpenCV methods on Colab. I'm not aware of any fix or workaround for this issue atm.

Besides these issues on Colab, below are few things that can be modified:

The functiondef plot_history(history) works just fine without the parameter history since it's not being used anywhere inside the function

In the below code, rmse needs to be printed instead of rmse_final

test_features_norm = (test_features - train_mean) / train_std
mse, _, _ = model.evaluate(test_features_norm, test_labels)
rmse = np.sqrt(mse)
print('Root Mean Square Error on test set: {}'.format(round(rmse_final, 3)))

https://deeplearning.mit.edu/ can't be accessed — could you please take a look at that? This seems to be a DB connection issue.

Thank you for your work, Lex 🤝

The links in the tutorial redirect to somewhere else - a page which now doesn't probably exist.

path: tutorial_gans/tutorial_gans.ipynb

Some tf functions are deprecated

I added:
!pip install tensorflow==1.15

and there was no video, so i changed the last code cell to:
mp4 = open('gan.mp4','rb').read() data_url = "data:video/mp4;base64," + base64.b64encode(mp4).decode() HTML(""" <video width=400 controls autoplay loop> <source src="%s" type="video/mp4"> </video> """ % data_url)

The problem is from this: sep_color= (yn+1)/2.0; line, since it return's a array of list of list. And the pl.scatter() cannot accept array of list as parameter.

I unlisted the sep_color.

# Computing the colors for the points
from itertools import chain
sep_color = (yn+1)/2.0;
sep_color = list(chain(*sep_color))

The second part of the deep learning basics notebook mentions using CNNs for the MNIST dataset, but the code is just using dense layers. This may be misleading to students.

Hope you could give some insight why the void class or background class should be ignore during confusion matrix calculation since during training void class is included?