If odd and even numbers in an integer sequence are to be sorted separately, as follows: a = [2, 3, 5, 4, 22, 45, 99, 16]#结果：[3, 5, 45, 99, 2, 4, 16, 22] b = sorted(a, key=lambda x: (x+100000) if x%2==0 else x) print(b) The key here is the sorting condition. We can assign different mappings to lambda functions. For example,
1.ArryList and HashMap.
(1) are all thread asynchronous.
(2) High efficiency but low safety (generally applicable to stand-alone version).
(3)HashMap has key values, which can solve the redundancy problem.
2.Vetor and HashTable.
(1) belong to thread synchronization.
(2) Low efficiency but high security (generally applicable to network version).
(3)HashTable has key values, which can solve the redundancy problem.
1. Install torch. The end result is that th command can be used. Official website: http://torch.ch/docs/getting-started.html 2, linux install hdf5, install hdf5 for the system reference link: https://jingyan.baidu.com/article/cbcede077e1ebe02f40b4d1e.html Official Download Link: https://support.hdfgroup.org/HDF5/release/obtainsrc518.html 3. Installing lua http://www.runoob.com/lua/lua-environment.html 4, install torch-hdf5. https://blog.csdn.net/lsh894609937/article/details/70477311 Reference Content: sudo apt-get install libhdf5-serial-dev hdf5-tools git clone https://github.com/deepmind/torch-hdf5 cd torch-hdf5 luarocks make hdf5-0-0.rockspec LIBHDF5_LIBDIR=
Deep learning is a relatively young research direction, and it has been found in fields from machine vision to speech recognition and natural language recognition.To tell the truth, Brother Meow only knew about this subject before, but he did not know exactly what it was and how to use it.In fact, I don't know anything about it now, but I feel more and more that in-depth study is a special specialty we need in the future.
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Such as the topic, when training in CAFFE (Convolutional Architecture for Fast Feature Embedding), caffe meeting this special number, loss will always be this number.
Although there are many trick on the Internet, there is little detailed analysis. Therefore, it is necessary to study the source code of CAFFE (Convolutional Architecture for Fast Feature Embedding).
Softmax's formula is
Where x is the output of the previous layer of softmax