Install OpenCV, PyQt4, Anaconda.

That means I have to install these three separately?

conda install -c https://conda.anaconda.org/menpo opencv3

Install opencv with anaconda again?

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.

According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li

when i run the example which using timit to build a lstm+ctc system,i meet a mistake,the error as follows：

can you give a suggest how to deal with it,thank very much.

For further development of the project, it would be great to have unit tests to ensure the project code does not break with further changes/improvements. The tests go under tests folder in 2.0 branch where Python 3+ porting is happening.

flake8 testing of https://github.com/CSAILVision/places365 on Python 3.7.1

$ flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics

./train_placesCNN.py:66:14: E999 SyntaxError: invalid syntax
    print args
             ^
./docker/run_scene.py:36:10: E999 SyntaxError: invalid syntax
			print i, labels[k]
         ^
2     E999 Sy

In augmentation, elastic_transform, it only applies a random transform on one input image array. I would think to be used for training, the image and mask pair should be transform in the same way. However, this single-input-image, single-output-image method makes it very inconvenient. Could we deform a list of images (np.arrays) using the same transformation in this method ? Thanks!

Modify 2_one-pixel-attack-imagenet.ipynb to download a sample subset of ImageNet files with appropriate labels. The files should be preprocessed as to be ready for the attack.