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

I construct it using BoolVal : []bool{fasle}, with shape dim size = 1, but it has errors like behind:

The second input must be a scalar, but it has shape [1]

I don't know how to solve it.

The current examples are in the form of scripts. To make easier and more interactive for users of the library it would help to have notebooks demonstrating these examples. For now the notebooks would go under examples folder under branch 2.0 where porting to Python 3+ is happening.

In Place205, input images are zero-centered by mean pixel (rather than mean image) subtraction with the following values explained in readme.txt file when downloaded here:

mu = np.array([105.487823486, 113.741088867, 116.060394287])

The following function with places365CNN_mean.binaryproto:

def load_mean_file(mean_file):
      

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!

Thanks for sharing the examples. They are very useful. But I have one question, for mtcnn
https://github.com/OAID/Tengine/blob/master/examples/mtcnn/mtcnn.cpp

line 295: if(*(confidence_data + 1) > conf_r_threshold_)

should it be conf_o_threshold_?

e.g. some short methods may contain a description in the return tag, but not a description of the method itself. (to avoid redundancy).

Doing this would extract more methods, but they may be of lower quality if incorrectly parsed or automatically generated. I'd expect a description such as @return bool STARTDESCRIPTION true if this is a float to be extracted (I'm not familiar with how the dat