Abstract
Deep learning has gained tremendous traction from the developer and researcher communities. It plays an increasingly significant role in a number of application domains. Deep learning frameworks are proposed to help developers and researchers easily leverage deep learning technologies, and they attract a great number of discussions on popular platforms, i.e., Stack Overflow and GitHub. To understand and compare the insights from these two platforms, we mine the topics of interests from these two platforms. Specifically, we apply Latent Dirichlet Allocation (LDA) topic modeling techniques to derive the discussion topics related to three popular deep learning frameworks, namely, Tensorflow, PyTorch and Theano. Within each platform, we compare the topics across the three deep learning frameworks. Moreover, we make a comparison of topics between the two platforms. Our observations include 1) a wide range of topics that are discussed about the three deep learning frameworks on both platforms, and the most popular workflow stages are Model Training and Preliminary Preparation. 2) the topic distributions at the workflow level and topic category level on Tensorflow and PyTorch are always similar while the topic distribution pattern on Theano is quite different. In addition, the topic trends at the workflow level and topic category level of the three deep learning frameworks are quite different. 3) the topics at the workflow level show different trends across the two platforms. e.g., the trend of the Preliminary Preparation stage topic on Stack Overflow comes to be relatively stable after 2016, while the trend of it on GitHub shows a stronger upward trend after 2016. Besides, the Model Training stage topic still achieves the highest impact scores across two platforms. Based on the findings, we also discuss implications for practitioners and researchers.
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Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: A system for large-scale machine learning. In: OSDI, vol 16, pp 265–283
Allamanis M, Sutton C (2013) Why, when, and what: Analyzing stack overflow questions by topic, type, and code. In: Proceedings of the 10th working conference on mining software repositories, IEEE Press, pp 53–56
Azad S, Rigby P C, Guerrouj L (2017) Generating api call rules from version history and stack overflow posts. ACM Trans Softw Eng Methodol (TOSEM) 25(4):29
Bahrampour S, Ramakrishnan N, Schott L, Shah M (2015) Comparative study of deep learning software frameworks. arXiv:151106435
Bajaj K, Pattabiraman K, Mesbah A (2014) Mining questions asked by web developers. In: Proceedings of the 11th working conference on mining software repositories, ACM, pp 112–121
Baltes S, Dumani L, Treude C, Diehl S (2018) Sotorrent: Reconstructing and analyzing the evolution of stack overflow posts. arXiv:180307311
Barua A, Thomas S W, Hassan A E (2014) What are developers talking about? an analysis of topics and trends in stack overflow. Empir Softw Eng 19(3):619–654
Bergstra J, Breuleux O, Bastien F, Lamblin P, Pascanu R, Desjardins G, Turian J, Warde-Farley D, Bengio Y (2010) Theano: A cpu and gpu math compiler in python. In: Proc. 9th python in science conf, vol 1
Blei D M, Ng A Y, Jordan M I (2012) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In: 8th ACM international conference on web search and data mining, pp 399–408
Bovens L, Hartmann S (2010) Bayesian Epistemology. Clarendon
Cai R, Xu B, Yang X, Zhang Z, Li Z (2017) An encoder-decoder framework translating natural language to database queries. arXiv:171106061
Chen C, Gao S, Xing Z (2016) Mining analogical libraries in q&a discussions–incorporating relational and categorical knowledge into word embedding. In: 2016 IEEE 23rd international conference on software analysis, evolution, and Reengineering (SANER), IEEE, vol 1, pp 338–348
Chen T H, Thomas S W, Hemmati H, Nagappan M, Hassan A E (2017) An empirical study on the effect of testing on code quality using topic models: A case study on software development systems. IEEE Trans Reliab R 66(3):806–824
Collobert R, Kavukcuoglu K, Farabet C (2011) Torch7: A matlab-like environment for machine learning. In: BigLearn, NIPS workshop, EPFL-CONF-192376
De Lucia A, Di Penta M, Oliveto R, Panichella A, Panichella S (2014) Labeling source code with information retrieval methods: An empirical study. Empir Softw Eng 19(5):1383–1420
Ding W, Wang R, Mao F, Taylor G (2014) Theano-based large-scale visual recognition with multiple gpus. arXiv:14122302
Duan C, Cui L, Chen X, Wei F, Zhu C, Zhao T (2018) Attention-fused deep matching network for natural language inference. In: IJCAI, pp 4033–4040
Erickson B J, Korfiatis P, Akkus Z, Kline T, Philbrick K (2017) Toolkits and libraries for deep learning. J Digit Imaging 30(4):400–405
Hannun A, Case C, Casper J, Catanzaro B, Diamos G, Elsen E, Prenger R, Satheesh S, Sengupta S, Coates A et al (2014) Deep speech: Scaling up end-to-end speech recognition. arXiv:14125567
Hinton G E, Osindero S, Teh Y W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
Hoang CDV, Haffari G, Cohn T (2017) Towards decoding as continuous optimisation in neural machine translation. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 146–156
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Hoffman MD, Blei DM, Bach F (2010) Online learning for latent dirichlet allocation. In: International conference on neural information processing systems, pp 856–864
Ketkar N (2017) Introduction to pytorch. In: Deep learning with python, Springer, pp 195–208
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A P, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR, vol 2, p 4
Lee SR, Heo MJ, Lee CG, Kim M, Jeong G (2017) Applying deep learning based automatic bug triager to industrial projects. In: Proceedings of the 2017 11th joint meeting on foundations of software engineering, ACM, pp 926–931
Li H, Xing Z, Peng X, Zhao W (2013) What help do developers seek, when and how? In: 2013 20th Working Conference on Reverse Engineering (WCRE), IEEE, pp 142–151
Li H, Chen T H P, Shang W, Hassan A E (2018) Studying software logging using topic models. Empir Softw Eng 23(5):2655–2694
Li M, Andersen D G, Park J W, Smola A J, Ahmed A, Josifovski V, Long J, Shekita E J, Su B Y (2014) Scaling distributed machine learning with the parameter server. In: OSDI, vol 14, pp 583–598
Liu H, Xu Z, Zou Y (2018) Deep learning based feature envy detection. In: Proceedings of the 33rd ACM/IEEE international conference on automated software engineering, ACM, pp 385–396
Loper E, Bird S (2002) Nltk: The natural language toolkit. In: Proceedings of the ACL-02 workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics-Volume 1, Association for Computational Linguistics, pp 63–70
Lukins SK, Kraft NA, Etzkorn LH (2008) Source code retrieval for bug localization using latent dirichlet allocation. In: Working conference on reverse engineering, 2008. Wcre ’08, pp 155–164
Miller G A (1995) Wordnet: A lexical database for english. Commun ACM 38 (11):39–41
Mo W, Shen B, Chen Y, Zhu J (2015) Tbil: A tagging-based approach to identity linkage across software communities. In: Software Engineering Conference (APSEC) 2015 Asia-Pacific, IEEE, pp 56–63
Newman D, Lau J H, Grieser K, Baldwin T (2010) Automatic evaluation of topic coherence. In: Human language technologies: Conference of the North American chapter of the association of computational linguistics, Proceedings, June 2-4, 2010 Los Angeles, California, USA, pp 100–108
Nguyen AT, Nguyen TT, Al-Kofahi J, Nguyen HV, Nguyen TN (2011) A topic-based approach for narrowing the search space of buggy files from a bug report. In: Proceedings of the 2011 26th IEEE/ACM international conference on automated software engineering, IEEE Computer Society, pp 263–272
Panichella A, Dit B, Oliveto R, Di Penta M, Poshyvanyk D, De Lucia A (2013) How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms. In: Proceedings of the 2013 international conference on software engineering, IEEE Press, pp 522–531
Rosen C, Shihab E (2016) What are mobile developers asking about? a large scale study using stack overflow. Empir Softw Eng 21(3):1192–1223
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85–117
Schütze H, Manning CD, Raghavan P (2008) Introduction to information retrieval, vol 39. Cambridge University Press, Cambridge
Spencer D (2009) Card sorting: Designing usable categories. Rosenfeld Media
Thomas S (2012) Mining unstructured software repositories using ir models
Treude C, Robillard MP (2016) Augmenting api documentation with insights from stack overflow. In: 2016 IEEE/ACM 38th international conference on software engineering (ICSE), IEEE, pp 392–403
Treude C, Barzilay O, Storey MA (2011) How do programmers ask and answer questions on the web?: Nier track. In: 2011 33rd international conference on software engineering (ICSE), IEEE, pp 804–807
Vasilescu B, Filkov V, Serebrenik A (2013) Stackoverflow and github: Associations between software development and crowdsourced knowledge. In: 2013 international conference on social computing (SocialCom), IEEE, pp 188–195
Wan Y, Zhao Z, Yang M, Xu G, Ying H, Wu J, Yu PS (2018) Improving automatic source code summarization via deep reinforcement learning. In: Proceedings of the 33rd ACM/IEEE international conference on automated software engineering, ACM, pp 397–407
Wan Z, Lo D, Xia X, Cai L (2017) Bug characteristics in blockchain systems: A large-scale empirical study
Wan Z, Xia X, Hassan A E (2019) What do programmers discuss about blockchain? a case study on the use of balanced lda and the reference architecture of a domain to capture online discussions about blockchain platforms across stack exchange communities. IEEE Trans Softw Eng 2019:1–1
Wang S, Chen T H, Hassan A E (2018) Understanding the factors for fast answers in technical q&a websites. Empir Softw Eng 23(3):1552–1593
Weng R, Huang S, Zheng Z, Dai X, Chen J (2017) Neural machine translation with word predictions. arXiv:170801771
Yang X L, Lo D, Xia X, Wan Z Y, Sun J L (2016) What security questions do developers ask? a large-scale study of stack overflow posts. J Comput Sci Technol 31(5):910–924
Yao Z, Weld DS, Chen WP, Sun H (2018) Staqc: A systematically mined question-code dataset from stack overflow. arXiv:180309371
Ye D, Xing Z, Foo C Y, Li J, Kapre N (2016) Learning to extract api mentions from informal natural language discussions. In: 2016 IEEE international conference on software maintenance and evolution (ICSME), IEEE, pp 389–399
Yu L, Mishra A, Mishra D (2014) An empirical study of the dynamics of github repository and its impact on distributed software development. In: OTM confederated international conferences” on the move to meaningful internet systems”, Springer, pp 457–466
Zagalsky A, German D M, Storey M A, Teshima C G, Poo-Caamaño G (2018) How the r community creates and curates knowledge: an extended study of stack overflow and mailing lists. Empir Softw Eng 23(2):953–986
Zhang Y, Chen Y, Cheung S C, Xiong Y, Zhang L (2018) An empirical study on tensorflow program bugs
Acknowledgment
This research was partially supported by the National Key Research and Development Program of China (No. 2017YFB1400601), Key Research and Development Project of Zhejiang Province (No. 2017C01015), National Science Foundation of China (No. 61772461), Natural Science Foundation of Zhejiang Province (No. LR18F020003 and No.LY17F020014).
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Communicated by: Filippo Lanubile
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Appendices
Appendix
A LDA Topics and Keywords
Tables 7, 8, 9, 10, 11 and 12 illustrate the details of the discovered LDA-topics and their top keywords.
B LDA-topic Trends
We further make analyses on the development trends of LDA-topics, we first detemine the dominant topics of the posts/records by applying the dominant topic metric (1). Then, we calculate the topic trends of the LDA-topics using the impact metric (2). Figures 17, 18, 19, 20, 21 and 22 illustrate the top 3 LDA-topics with the largest increases or decreases over time for the six different corpora.
C Examples of LDA Topics on Different Workflow Stages
- Preliminary Preparation. :
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Out of the derived 75 LDA-topics, 29 LDA-topics belong to the preliminary preparation stage, which are: 1). File Operation for Tensorflow on Stack Overflow, 2). Keras for Tensorflow on Stack Overflow, 3). Installation Error for Tensorflow on Stack Overflow, 4). Runtime Error for Tensorflow on Stack Overflow, 5). Build Error for Tensorflow on Stack Overflow, 6). Code Error for PyTorch on Stack Overflow, 7). Installation Error for PyTorch on Stack Overflow, 8). File Operation for Theano on Stack Overflow, 9). Code Error for Theano on Stack Overflow, 10). File Operation for Tensorflow on GitHub, 11). Installation in Linux for Tensorflow on GitHub, 12). Version Problem for Tensorflow on GitHub, 13). Build Error for Tensorflow on GitHub, 14). Fixing Error for Tensorflow on GitHub, 15). File Operation for PyTorch on GitHub, 16). System Installation for PyTorch on GitHub, 17). Version Problem for PyTorch on GitHub, 18). Third Party for PyTorch on GitHub, 19). Build Error for PyTorch on GitHub, 20). Code Error for PyTorch on GitHub, 21). File Operation for Theano on GitHub, 22). Version Problem for Theano on GitHub, 23). Using Numpy for Theano on GitHub, 24). Import Error for Theano on GitHub, 25). Installation Error for Theano on GitHub, 26). Warnings for Theano on GitHub, 27). Errors on Windows for Theano on GitHub, 28). Compile Error for Theano on GitHub, 29). Clang Error for Theano on GitHub. Some example posts/records of these LDA-topics are shown as following:
Detect object from video stream using Keras .h5 file I am using keras and tensorflow to train a custom model using transfer learning. I was wondering, is there any tutorial which covers custom object detection from live video stream using keras .h5 file? Here is my sample code for training based on https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/5.3-using-a-pretrained-convnet.ipynb Dominant Topic: Tensorflow on Stack Overflow/Keras
Install PyTorch on Windows I am trying to install PyTorch on Windows8.1. I am using Python 3.6.4 and no GPU. I’ve tried already the Anaconda package provided by peterjc123 by running conda install -c peterjc123 pytorch_legacy cuda80 using a virtual environment. While the installation goes smooth (without errors), after import torch I get the following error. Can somebody help me to install it? Dominant Topic: PyTorch on Stack Overflow/Installation Error
strides argument, the layer received both the legacy keyword argument subsample and the Keras 2 keyword argument strides when I try to run this code with keras 2.1.3 I get this error https://github.com/marcellacornia/sam Dominant Topic: Theano on Stack Overflow/Code Error
cannot import name bayesflow Error Hi, I get the error I mentioned in the title. I did a search on Google and I usually found a solution to update dask. I updated Dask to version 0.17.2 but I still get the same error. I can not import BayesFlow. The Tensorflow version is 0.12.1. Thanks for the answers.
Code : from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(“/tmp/data/”)
OS : Ubuntu 16.04 LTS Tensorflow version is 0.12.1 Cuda : 8.0 CuDNN : 5.1 GPU : 4 GB GTX 1050Ti Dask : 0.17.2 Dominant Topic: Tensorflow on GitHub/Version Problem
Install hpp headers for CPP Extensions With the Cppzation of a few files in ‘TH’/‘THC’, the CPP extensions got broken whenever the user uses feature from ‘THC’ in their files, when pytorch is installed via ‘python setup.py install’.
This addresses issues such as “/home/me/.conda/envs/pytorch/lib/python3.6/site-packages/torch/lib/include/THC/THCDeviceTensorUtils.cuh:5:25: fatal error: THCTensor.hpp: No such file or directory” Dominant Topic: PyTorch on GitHub/File Operation
Theano cannot detect clang++ in Mac OS X I am using the dev version of theano under Mac OS X 10.11.3 with command line tools for Xcode 7. Running theano gives me the following warning:
‘WARNING (theano.configdefaults): Only clang++ is supported. With g++, we end up with strange g++/OSX bugs.’
I’ve also got g++ installed. It seems theano cannot detect the ‘clang++’.
Besides, I had strang NaN problems resulting from a simple calculation ‘T.dot(W, X)’ where ‘W’ and ‘X’ do not have nan value (checked with ‘np.any(np.isnan)’). I doubt this is because of I am not using ‘clang++’ Dominant Topic: Theano on GitHub/Clang Error
- Data Preparation. :
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We obtain 13 LDA-topics in the data preparation stage, that is: 1). Variable for Tensorflow on Stack Overflow, 2). Data Reading for Tensorflow on Stack Overflow, 3). Tensor Operation for Tensorflow on Stack Overflow, 4). Input Error for Tensorflow on Stack Overflow, 5). Input Size for PyTorch on Stack Overflow, 6). Tensor Error for PyTorch on Stack Overflow, 7). Value Type for Tensorflow on GitHub, 8). Variable Shape for Tensorflow on GitHub, 9). Tensor Operation for PyTorch on GitHub, 10). Tensor Fixing for PyTorch on GitHub, 11). Data Type for Theano on GitHub, 12). Data Shape for Theano on GitHub, 13). Object Error for Theano on GitHub. The following is the examples of posts/records of the LDA-topics.
Dataset API ‘flat_map’ method producing error for same code which works with ‘map’ method I am trying to create a pipeline to read multiple CSV files using TensorFlow Dataset API and Pandas. However, using the flat_map method is producing errors. However, if I am using map method I am able to build the code and run it in session. This is the code I am using. I already opened #17415 issue in TensorFlow Github repository. Dominant Topic: Tensorflow on Stack Overflow/Data Reading
Image Captioning Example input size of Decoder LSTM PyTorch I’m new to PyTorch, there is a doubt that am having in the Image Captioning example code. We first embed the captions and then concat the embeddings with the context feature from the EncoderCNN, but the concat increases the size from embed size how we can forward that to the lstm? As the input size of lstm is already defined as embed_size. Dominant Topic: PyTorch on Stack Overflow/Input Size
Feature request: tf.as_dtype(float) should work just as tf.as_dtype(‘float’) In NumPy, ‘np.dtype(float)’ works just the same as ‘np.dtype(“float”)’. In TensorFlow ‘tf.as_dtype(“float”)’ works but ‘tf.as_dtype(float)’ crashes with ‘TypeError: Cannot convert value <class ‘float’> to a TensorFlow DType.’. Is there a particular reason for this behaviour or was it just overlooked? (same error for other builtins such as ‘int’ and ‘complex’) Dominant Topic: Tensorflow on GitHub/Value Type
Explicitly specify the output ndim in reshape The whole code expects the shape to be of length 4, and the output to be 4D already. Fixes #5613. Dominant Topic: Theano on GitHub/Data Shape
- Model Setup. :
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We find 8 model setup LDA-topics in our dataset, namely: 1). Image Classification for Tensorflow on Stack Overflow, 2). Word Embedding for Tensorflow on Stack Overflow, 3). RNN LSTM for Tensorflow on Stack Overflow, 4). Network Layer for Tensorflow on Stack Overflow, 5). Model Saving for Tensorflow on Stack Overflow, 6). Image Training for PyTorch on Stack Overflow, 7). Network Layer for PyTorch on Stack Overflow, 8). Storage Error for Theano on GitHub. Here are the 3 examples of these LDA-topics.
single neuron layer after softmax (keras) I need to create a neural network (with keras) that has as last layer a single neuron that contains the index of the neuron with the maximum value prediction in the precedent softmax layer. For example my softmax layer gives as result this: [0.1, 0.1, 0.7, 0.0, 0.05, 0.05] And I want that the single neuron layer (after the softmax layer) gives as result 2 (considering a 0 based valutation). How can I do that? Dominant Topic: Tensorflow on Stack Overflow/Network Layer
In tensorflow deep and wide tutorial, what’s the embedding principle When I played tensorflow tutorial, one embedding trick is used in Wide and Deep tutorial like this. The tutorial shows how transfer sparse feature (usually one hot encoding) to embedding vector. I knew there are some approaches to create this embedding, such as word embedding, PCA or t-SNE or matrix factorization. But in this tutorial, they did not show how to create an embedding for the sparse vector. Or did the tutorial just use neural network to finish the embedding? Dominant Topic: Tensorflow on Stack Overflow/Word Embedding
Reading multiple images as custom dataset for PyTorch? I want to read in multiple images for the main_image set and blur_image set. For example, 5 main images and 5 blurred images. The goal is determine what values for the kernel in the convolutional layer convert the main images to the blurred images. The assumption is that the same kernel is used to blur each of the 5 original images to produce the 5 blurred images.
My code is available at: https://pastebin.com/PWf7rjd4 and https://pastebin.com/VxryDb7g
However, it seems to only be processing the first image, that is “1.png” for the main and blurred images. It is not processing images 2.png, 3.png, 4.png, and 5.png How can I fix this? Dominant Topic: PyTorch on Stack Overflow/Image Training
- Model Training. :
-
We discover 22 model training LDA-topics in our dataset, including: 1). Model Training for Tensorflow on Stack Overflow, 2). Loss Function for Tensorflow on Stack Overflow, 3). Batch for Tensorflow on Stack Overflow, 4). Performance for Tensorflow on Stack Overflow, 5). Training Accuracy for Tensorflow on Stack Overflow, 6). Model Training for PyTorch on Stack Overflow, 7). Gpu Training for PyTorch on Stack Overflow, 8). Loss Function for PyTorch on Stack Overflow, 9). Cuda Error for PyTorch on Stack Overflow, 10). Function Operation for Theano on Stack Overflow, 11). Model Training for Theano on Stack Overflow, 12). Gpu Error for Theano on Stack Overflow, 13). Model Training for Tensorflow on GitHub, 14). Gradient for Tensorflow on GitHub, 15). Performance for Tensorflow on GitHub, 16). Function Operation for PyTorch on GitHub, 17). Distributed Process for PyTorch on GitHub, 18). Theano Composite for Theano on GitHub, 19). Performance for Theano on GitHub, 20). Gpu Error for Theano on GitHub, 21). Cuda Error for Theano on GitHub, 22). Gpuarray Bug for Theano on GitHub. The following are the examples of posts/records in our dataset.
Try to define pearson correlation as loss function but got error I would like to use pearson correlation as the loss function in Keras with backend of tensorflow. The dimensions of the tensor is (Batch, Coils, Time). The correlation coefficients are to be calculated along time and across coils. For example, if the number of coils is 3, the averaged correlation coefficients will be calculated between coil #1 and #2, #1 and #3, and #2 and #3. Dominant Topic: Tensorflow on Stack Overflow/Loss Function
RNN is not training (PyTorch) I can’t get what I am doing wrong when training RNN. I am trying to train RNN for AND operation on sequences (to learn how it works on simple task). But my network is not learning, loss stays the same and it can’t event overfit the model. Can you please help me to find the problem? Dominant Topic: PyTorch on Stack Overflow/Model Training
Enabling GPU with theano generates Exception I have followed the steps from here to enable gpu with theano on an Ubuntu 16.04 machine. I installed cuda toolkit, cudnn, drivers but I am still not able to get it to work. Dominant Topic: Theano on Stack Overflow/Gpu Error
[Java] Support addition of gradient operations in a graph This calls the C-api ‘TF_AddGradients’ method through a new JNI binding for adding gradient nodes to a graph. It also includes an ‘AddGradients’ wrapper for invoking this operation smoothly while building a graph using the new Java Ops API. Dominant Topic: Tensorflow on GitHub/Gradient
Use customized python interpreter for distributed launch util For spawning sub-processes, I think it should be quite intuitive to use the interpreter of ‘launch.py’ rather than the default ‘python’. Dominant Topic: PyTorch on GitHub/Distributed Process
- Model Evaluation. :
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We find 2 model evaluation LDA-topics in our dataset, 1). Tensorboard for Tensorflow on Stack Overflow , 2). Code Graph for Theano on GitHub. The following example post is from these LDA-topics:
Tensorboard not creating network graph (Python) I really can’t understand why tensorboard is not showing the graph of my network. I have followed the tutorials on Tensorboard Website and other stuff in the web, none of these allowed to display the graph. I am embedding the part of my code related to the network. I’ve tried to remove all the other parts but I did not want to reduce to much otherwise it can create confusion. The only thing it displays on the graph sections is the global_step. Dominant Topic: Tensorflow on Stack Overflow/Tensorboard
- Model Tuning. :
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In our derived 75 LDA-topics, no LDA-topic is related to the model tuning stage.
- Model Prediction. :
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Only 1 LDA-topic is associated with the model prediction stage, which is 1). Test Error for Theano on GitHub.
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Han, J., Shihab, E., Wan, Z. et al. What do Programmers Discuss about Deep Learning Frameworks. Empir Software Eng 25, 2694–2747 (2020). https://doi.org/10.1007/s10664-020-09819-6
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DOI: https://doi.org/10.1007/s10664-020-09819-6