Hey there, Wanted to check with you first, currently in requirements-cuda.txt we find tensorflow-gpu=2.4.0 which if I am not mistaken is legacy, and tensorflow=2.4.0 would just work as well, this is since tensorflow > 2. I've wasted some time trying to set this up on linux, and this was one of the things that I had to change to make it work (among others), actually I used tensorflow without an specific version (installed 2.5.0) and so far so good, so it might be able to be bumped. Let me know if you wnat a PR. Cheers.
Minkiu Updated
안녕하세요! 어제까지 야학 공부를 끝낸 학생입니다. 1주 전에 그림<=>사진 웹 프로그램을 만들기 위해 질문드렸고 도움이 되는 자료를 많이 주셨는데 활용을 못해서 1주간 열심히 시도해봤지만 실패하여 염치없이 다시 질문하게 되었습니다..ㅠ # 해결하고자 하는 문제 https://www.tensorflow.org/tutorials/generative/style_transfer 해당 사이트의 파이썬 코드를 teachable machine에 tensorflow와 tensorflow.js가 있듯이 자바스크립트 코드로 변환해보려 시도를 했는데 잘 안되어 질문드립니다. https://www.tensorflow.org/js/tutorials/conversion/import_keras?hl=ko https://www.youtube.com/watch?v=yWBM2-Rx47M 우선 해당 사이트와 유튜브를 참고하며 파이썬을 자바스크립트로 바꾸는 작업을 1주일동안 찾아가며 해보는 중인데 정말 해결이 안돼서 어렵네요 ㅠ 답도 다 나와있는데 말이죠... # 코드 혹은 오류 <해당 방법으로는 model.json 생성에 성공했지만 경로 입력시 오류가 발생했습니다.> model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(4, input_dim=2, activation='tanh')) model.add(tf.keras.layers.Dense(1, activation='sigmoid')) model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy']) tfjs.converters.save_keras_model(model,'models') tensorflowjs_converter --input_format keras \C:\Users\User\Desktop\picturetophoto\model.json \C:\Users\User\Desktop\picturetophoto 에러 : ValueError: Nonexistent path to HDF5 file: \C:\Users\User\Desktop\picturetophoto\models\model.json <.h5를 생성하는 해당방법엔 해당오류가 있었습니다> model = tf.keras.models.Sequential() model.save('C:\Users\User\Desktop\picturetophoto') 에러 : File "c:\Users\User\Desktop\picturetophoto\model.py", line 74 -3: truncated \UXXXXXXXX escape model.save('C:\Users\User\Desktop\picturetophoto') ^ SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape 이렇게 2가지 방향으로 변환을 시키려 시도했는데 둘 다 잘 안되어 질문드립니다. 되도록 json을 로드하는 방법이 아닌 자바스크립트 코드로 바꿔 수정이 가능하도록 하고 싶은데 혹시 방법이 있을까요??
donhaklee Updated ML / DL
I had three (albeit small) PRs in tensorflow/tensorflow. All of them were reverted due to some internal checks, which were not available to the end user back then. For more info, see #49201. Should I reopen these PRs here? Links to PRs: #48610, #48000, #48491
AdityaKane2001 Updated
As a developer, I want to be able to see the expected outputs in the tutorial. As a manager, I want you to present your TensorFlow skills. Tutorial: https://github.com/aamini/introtodeeplearning/blob/master/lab1/Part1_TensorFlow.ipynb AC: complete tensorflow tutorial present tutorial output. Story Point: 1
kadamQ Updated
As a manager, I want the developer to understand how modern data processing and deep learning works. As a developer, I want to receive working implementations of deep feed forward, sequence processing, and image recognition networks. As a manager, I want to be able to quantify the performance of the networks. As a developer, I want to be able to run the models with parameters from a configuration file. As a manager, I want to be able to look at plots visualizing the output of the model. AC: complete tensorflow tutorial read CSV into pandas dataframe log model runs and solutions plot outputs print best model parameters save run data into directory with separate names Story Point: 5
kadamQ Updated
Tensorflow on Windows is unusable, without users manually hacking the produced libraries. tensorflow/tensorflow#23542 https://stackoverflow.com/questions/62954634/tensorflow-2-3-unresolved-external-symbols-in-machine-generated-files-when-build This is confusing since "ok" installed packets fail during link phase because missing exported symbols. Regardless of "who should fix it/whose fault it is" fact is that having a broken package in vcpkg is bad for user perspective of vcpkg quality. As I presume that it is unlikely somebody will write a magic fix for this since it does not seem trivial so I think proper thing is to remove the tensorflow packet for Windows. If it matters broken version I tried is 2.4#2, but I presume all of them are broken. note: build(installing tensorflow_cc with vcpkg) works fine, I get .lib and .dll (in both Release and Debug variants), but if you actually try to actually write some TF code that uses those libs then you get LNK2019. as for repro: install tensorflow_cc and try to actually use to build some toy TF project. Some of missing symbols are: LNK2019: unresolved external symbol "public: __cdecl tensorflow::SessionOptions::SessionOptions(void)” LNK2019: unresolved external symbol "class tensorflow::Status __cdecl tensorflow::NewSession(struct tensorflow::SessionOptions const &,class tensorflow::Session * *)"
As a manager, I want the developer to understand of how modern data processing and deep learning works. As a developer, I want to receive working implementations of deep feed forward, sequence processing and image recognition networks. As a manager, I want to be able to quantify the performance of the networks. As a developer, I want to be able to run the models with parameters from a configuration file. As a manager, I want to be able to look at plots visualizing the output of the model. AC: complete tensorflow tutorial read CSV into pandas dataframe log model runs and solutions plot outputs print best model parameters save run data into directory with separate names Story Point: 5
Benczus Updated
System information macOS 11.5.1 (Big Sur) Macbook Air M1 2020 Hi! Would you help with a tutorial for installing Tensorflow and tensorflow_addons in this system?
rosas-github Updated
Source Source doc in Mandarin: 開啟 TensorFlow 自動混合精度運算與執行效能分析 To-do Before translating Step1. [GitHub] Create a new branch for every doc ( branch name: use [xxx-xxxx] No. doc_title) Step2. Download [.md] of the source doc in Mandarin Step3. Commit & push the [.md] in Step 2 to GitHub (directory: https://github.com/twcc/TWCC-Docs-en/tree/main/CCS/3.%20Tutorials/No. doc_title) Start translating Step4. [.md] title: change zh to en [.md] tags: change ZH to EN [.md] header: change{%hackmd @docsharedstyle/twccheader-zh %} to {%hackmd @docsharedstyle/twccheader-en %} [.md] content: translate Mandarin content into English & update screenshots change -zh ending in URLs to -en insert ==**Need to update:exclamation:**== if the content need to be updated by Viga Wrap up Step5. [GitHub] commit & push changes to the directory (in Step 3) of the branch (in Step 1) every work day Step6. [GitHub] Once the translation is done, create a pull request base: master, compare: your branch reviewer: Viga
VigaWei Updated must have
Source Source doc in Mandarin: 使用 TensorFlow 訓練 MNIST 手寫數字辨識模型 To-do Before translating Step1. [GitHub] Create a new branch for every doc ( branch name: use [xxx-xxxx] No. doc_title) Step2. Download [.md] of the source doc in Mandarin Step3. Commit & push the [.md] in Step 2 to GitHub (directory: https://github.com/twcc/TWCC-Docs-en/tree/main/CCS/3.%20Tutorials/No. doc_title) Start translating Step4. [.md] title: change zh to en [.md] tags: change ZH to EN [.md] header: change{%hackmd @docsharedstyle/twccheader-zh %} to {%hackmd @docsharedstyle/twccheader-en %} [.md] content: translate Mandarin content into English & update screenshots change -zh ending in URLs to -en insert ==**Need to update:exclamation:**== if the content need to be updated by Viga Wrap up Step5. [GitHub] commit & push changes to the directory (in Step 3) of the branch (in Step 1) every work day Step6. [GitHub] Once the translation is done, create a pull request base: master, compare: your branch reviewer: Viga
VigaWei Updated
You have to prepare a content file for this topic Interested ones can comment below this or you can text me in slack TensorFlow Basics: Tensor Shape Type Graph Sessions Operators Variables
As a manager, I want the developer to understand of how modern data processing and deep learning works. As a developer, I want to receive working implementations of deep feed forward, sequence processing and image recognition networks. As a manager, I want to be able to quantify the performance of the networks. As a developer, I want to be able to run the models with parameters from a configuration file. As a manager, I want to be able to look at plots visualizing the output of the model. AC: complete tensorflow tutorial read CSV into pandas dataframe log model runs and solutions plot outputs print best model parameters save run data into directory with separate names Story Point: 5
kadamQ Updated
Figure out whether any of these "best practices" should be implemented in the image file or not. https://community.intel.com/t5/Intel-oneAPI-AI-Analytics/Tensorflow-2-optimization-documentation-Python/m-p/1205148 https://github.com/IntelAI/models/blob/master/docs/general/tensorflow/GeneralBestPractices.md most important here is OMP_NUM_THREADS, but also: https://www.tensorflow.org/api_docs/python/tf/config/threading/set_inter_op_parallelism_threads https://able.bio/rhett/how-to-set-and-get-environment-variables-in-python--274rgt5 installation: https://software.intel.com/content/www/us/en/develop/articles/intel-optimization-for-tensorflow-installation-guide.html
myoung3 Updated
I am able to get my AMD Radeon Pro 5700 XT gpu to run on an iMac 27" with Big Sur tensorflow with 2.5. If I add tensorflow-text, I get this exception: % python Python 3.8.2 (default, Apr 8 2021, 23:19:18) [Clang 12.0.5 (clang-1205.0.22.9)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/tensorflow/__init__.py", line 449, in <module> _ll.load_library(_plugin_dir) File "/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/framework/load_library.py", line 154, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 6): Symbol not found: _TF_AssignUpdateVariable Referenced from: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: flat namespace >>> Here's what was added with pip install tensorflow-text in the Python 3.8.2 virtual environment: pip install tensorflow_text Collecting tensorflow_text Using cached tensorflow_text-2.5.0-cp38-cp38-macosx_10_9_x86_64.whl (3.6 MB) Collecting tensorflow<2.6,>=2.5.0 Using cached tensorflow-2.5.0-cp38-cp38-macosx_10_11_x86_64.whl (195.7 MB) Collecting tensorflow-hub>=0.8.0 Using cached tensorflow_hub-0.12.0-py2.py3-none-any.whl (108 kB) Requirement already satisfied: tensorflow-estimator<2.6.0,>=2.5.0rc0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (2.5.0) Requirement already satisfied: numpy~=1.19.2 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.19.5) Requirement already satisfied: tensorboard~=2.5 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (2.5.0) Requirement already satisfied: termcolor~=1.1.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.1.0) Requirement already satisfied: opt-einsum~=3.3.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (3.3.0) Requirement already satisfied: protobuf>=3.9.2 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (3.17.3) Requirement already satisfied: flatbuffers~=1.12.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.12) Requirement already satisfied: google-pasta~=0.2 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (0.2.0) Requirement already satisfied: grpcio~=1.34.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.34.1) Requirement already satisfied: astunparse~=1.6.3 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.6.3) Requirement already satisfied: six~=1.15.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.15.0) Requirement already satisfied: keras-preprocessing~=1.1.2 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.1.2) Requirement already satisfied: typing-extensions~=3.7.4 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (3.7.4.3) Requirement already satisfied: gast==0.4.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (0.4.0) Requirement already satisfied: keras-nightly~=2.5.0.dev in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (2.5.0.dev2021032900) Requirement already satisfied: absl-py~=0.10 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (0.12.0) Requirement already satisfied: wrapt~=1.12.1 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (1.12.1) Requirement already satisfied: h5py~=3.1.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (3.1.0) Requirement already satisfied: wheel~=0.35 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorflow<2.6,>=2.5.0->tensorflow_text) (0.36.2) Requirement already satisfied: google-auth<2,>=1.6.3 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (1.32.1) Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (0.6.1) Requirement already satisfied: requests<3,>=2.21.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (2.26.0) Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (0.4.4) Requirement already satisfied: werkzeug>=0.11.15 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (2.0.1) Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (1.8.0) Requirement already satisfied: setuptools>=41.0.0 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (41.2.0) Requirement already satisfied: markdown>=2.6.8 in ./tensorflow-metal/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (3.3.4) Requirement already satisfied: pyasn1-modules>=0.2.1 in ./tensorflow-metal/lib/python3.8/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (0.2.8) Requirement already satisfied: cachetools<5.0,>=2.0.0 in ./tensorflow-metal/lib/python3.8/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (4.2.2) Requirement already satisfied: rsa<5,>=3.1.4 in ./tensorflow-metal/lib/python3.8/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (4.7.2) Requirement already satisfied: requests-oauthlib>=0.7.0 in ./tensorflow-metal/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (1.3.0) Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in ./tensorflow-metal/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (0.4.8) Requirement already satisfied: certifi>=2017.4.17 in ./tensorflow-metal/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (2021.5.30) Requirement already satisfied: idna<4,>=2.5 in ./tensorflow-metal/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (3.2) Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./tensorflow-metal/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (1.26.6) Requirement already satisfied: charset-normalizer~=2.0.0 in ./tensorflow-metal/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (2.0.1) Requirement already satisfied: oauthlib>=3.0.0 in ./tensorflow-metal/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow<2.6,>=2.5.0->tensorflow_text) (3.1.1) Installing collected packages: tensorflow-hub, tensorflow, tensorflow-text Successfully installed tensorflow-2.5.0 tensorflow-hub-0.12.0 tensorflow-text-2.5.0 Here's what the environment looked like before adding tensorflow-text: r@x86_64-apple-darwin13 ~ % pip list Package Version ------------------------ ------------------- absl-py 0.12.0 appnope 0.1.2 astunparse 1.6.3 attrs 21.2.0 backcall 0.2.0 cachetools 4.2.2 certifi 2021.5.30 charset-normalizer 2.0.1 Cython 0.29.23 debugpy 1.3.0 decorator 5.0.9 dill 0.3.4 flatbuffers 1.12 future 0.18.2 gast 0.4.0 google-auth 1.32.1 google-auth-oauthlib 0.4.4 google-pasta 0.2.0 googleapis-common-protos 1.53.0 grpcio 1.34.1 h5py 3.1.0 idna 3.2 importlib-resources 5.2.0 ipykernel 6.0.1 ipython 7.25.0 ipython-genutils 0.2.0 jedi 0.18.0 jupyter-client 6.1.12 jupyter-core 4.7.1 keras-nightly 2.5.0.dev2021032900 Keras-Preprocessing 1.1.2 Markdown 3.3.4 matplotlib-inline 0.1.2 numpy 1.19.5 oauthlib 3.1.1 opt-einsum 3.3.0 parso 0.8.2 pexpect 4.8.0 pickleshare 0.7.5 pip 21.1.3 promise 2.3 prompt-toolkit 3.0.19 protobuf 3.17.3 ptyprocess 0.7.0 pyasn1 0.4.8 pyasn1-modules 0.2.8 Pygments 2.9.0 python-dateutil 2.8.1 pyzmq 22.1.0 requests 2.26.0 requests-oauthlib 1.3.0 rsa 4.7.2 setuptools 41.2.0 six 1.15.0 tensorboard 2.5.0 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.0 tensorflow-datasets 4.3.0 tensorflow-estimator 2.5.0 tensorflow-macos 2.5.0 tensorflow-metadata 1.1.0 tensorflow-metal 0.1.1 termcolor 1.1.0 tornado 6.1 tqdm 4.61.2 traitlets 5.0.5 typing-extensions 3.7.4.3 urllib3 1.26.6 wcwidth 0.2.5 Werkzeug 2.0.1 wheel 0.36.2 wrapt 1.12.1 zipp 3.5.0 Here's with the environment looks like after installing tensorflow-text: % pip list Package Version ------------------------ ------------------- absl-py 0.12.0 appnope 0.1.2 astunparse 1.6.3 attrs 21.2.0 backcall 0.2.0 cachetools 4.2.2 certifi 2021.5.30 charset-normalizer 2.0.1 Cython 0.29.23 debugpy 1.3.0 decorator 5.0.9 dill 0.3.4 flatbuffers 1.12 future 0.18.2 gast 0.4.0 google-auth 1.32.1 google-auth-oauthlib 0.4.4 google-pasta 0.2.0 googleapis-common-protos 1.53.0 grpcio 1.34.1 h5py 3.1.0 idna 3.2 importlib-resources 5.2.0 ipykernel 6.0.1 ipython 7.25.0 ipython-genutils 0.2.0 jedi 0.18.0 jupyter-client 6.1.12 jupyter-core 4.7.1 keras-nightly 2.5.0.dev2021032900 Keras-Preprocessing 1.1.2 Markdown 3.3.4 matplotlib-inline 0.1.2 numpy 1.19.5 oauthlib 3.1.1 opt-einsum 3.3.0 parso 0.8.2 pexpect 4.8.0 pickleshare 0.7.5 pip 21.1.3 promise 2.3 prompt-toolkit 3.0.19 protobuf 3.17.3 ptyprocess 0.7.0 pyasn1 0.4.8 pyasn1-modules 0.2.8 Pygments 2.9.0 python-dateutil 2.8.1 pyzmq 22.1.0 requests 2.26.0 requests-oauthlib 1.3.0 rsa 4.7.2 setuptools 41.2.0 six 1.15.0 tensorboard 2.5.0 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.0 tensorflow 2.5.0 tensorflow-datasets 4.3.0 tensorflow-estimator 2.5.0 tensorflow-hub 0.12.0 tensorflow-macos 2.5.0 tensorflow-metadata 1.1.0 tensorflow-metal 0.1.1 tensorflow-text 2.5.0 termcolor 1.1.0 tornado 6.1 tqdm 4.61.2 traitlets 5.0.5 typing-extensions 3.7.4.3 urllib3 1.26.6 wcwidth 0.2.5 Werkzeug 2.0.1 wheel 0.36.2 wrapt 1.12.1 zipp 3.5.0
dbl001 Updated duplicate
Hello, the documentation in this repo all suggest it tailors to TF1 support exclusively. Since there's no backwards compatibility for TF2, is there an ETA for TF2 support for AWS neuron? Thank you!
cabal-daniel Updated
Develop tensorflow examples and test gradients for tf api
Saran-nns Updated
As a developer, I want to be able to see the expected outputs in the tutorial. As a manager, I want you to present your TensorFlow skills. Tutorial: https://github.com/aamini/introtodeeplearning/blob/master/lab1/Part1_TensorFlow.ipynb AC: complete tensorflow tutorial present tutorial output. Story Point: 1
kadamQ Updated
As a manager, I want the developer to understand of how modern data processing and deep learning works. As a developer, I want to receive working implementations of deep feed forward, sequence processing and image recognition networks. As a manager, I want to be able to quantify the performance of the networks. As a developer, I want to be able to run the models with parameters from a configuration file. As a manager, I want to be able to look at plots visualizing the output of the model. AC: complete tensorflow tutorial read CSV into pandas dataframe log model runs and solutions plot outputs print best model parameters save run data into directory with separate names Story Point: 5
kadamQ Updated
As a manager, I want the developer to understand of how modern data processing and deep learning works. As a developer, I want to receive working implementations of deep feed forward, sequence processing and image recognition networks. As a manager, I want to be able to quantify the performance of the networks. As a developer, I want to be able to run the models with parameters from a configuration file. As a manager, I want to be able to look at plots visualizing the output of the model. AC: complete tensorflow tutorial read CSV into pandas dataframe log model runs and solutions plot outputs print best model parameters save run data into directory with separate names Story Point: 5
Benczus Updated
Hi, Was anyone successful in migrating the code to tensorflow 2 at all? and if so, can you share the code? Thanks!
YedidyahD Updated
Hi, Thanks for this great project. I have a few questions on using Webdataset for tensorflow. I referred to this github repository to set up the data and model trainer pipeline. But, I have used a small synthetic dataset unlike imagenet used in the repository. When I ran a tensorflow model using Webdataset as a datastore in Windows OS, I got this error "zmq.error.ZMQError: Protocol not supported". However, this error did not come when running in Linux. Is it because of the use of IPC protocol for establishing socket connections via zmq defined inside multi.py? Inside Linux, I was performing few tests in a multi-gpu environment by varying ZMQ workers and when it was set to a value greater than 1 (say: 2, 4), the program froze midway during an epoch training. Sometimes while the program did not freeze, it did not terminate after the training completion. Could anyone help me understand the exact reason of this behavior? Note: I have installed the github version of Webdataset.
retazo0018 Updated bug
As a manager, I want the developer to understand how modern data processing and deep learning works. As a developer, I want to receive working implementations of deep feed forward, sequence processing, and image recognition networks. As a manager, I want to be able to quantify the performance of the networks. As a developer, I want to be able to run the models with parameters from a configuration file. As a manager, I want to be able to look at plots visualizing the output of the model. AC: complete tensorflow tutorial read CSV into pandas dataframe log model runs and solutions plot outputs print best model parameters save run data into directory with separate names Story Point: 5
kadamQ Updated
Would it be possible to modiy it for Tensorflow Lite? This should be much faster on a RPi
Xento Updated
thanks for public the code, my question is what is the version of tensorflow is available, thanks
trra1988 Updated
Hello, I am using HF and i have built my model using TensorFlow. I am interested in pruning my model but I am unsure if this supports TensorFlow. Any clarification would be appreciated
Amokstakov Updated
As a manager, I want the developer to understand of how modern data processing and deep learning works. As a developer, I want to receive working implementations of deep feed forward, sequence processing and image recognition networks. As a manager, I want to be able to quantify the performance of the networks. As a developer, I want to be able to run the models with parameters from a configuration file. As a manager, I want to be able to look at plots visualizing the output of the model. AC: complete tensorflow tutorial read CSV into pandas dataframe log model runs and solutions plot outputs print best model parameters save run data into directory with separate names Story Point: 5
Benczus Updated
I have a car with the latest version of Donkey Car. It runs tensorflow 2.2 However, when I run the colab script, it is creating a model with tensorflow 2.5. I am able to create the model, mypilot.h5, download it, and transfer it to my car. However when I start the car and load the model: python3 manage.py drive --model ./models/mypilot.h5 I get the following error: "get_model_by_type" model Type is: linear Created KerasLinear loading model ./models/mypilot.h5 Loading model ./models/mypilot.h5 Traceback (most recent call last): File "manage.py", line 719, in <module> meta=args['--meta']) File "manage.py", line 418, in drive load_model(kl, model_path) File "manage.py", line 383, in load_model kl.load(model_path) File "/home/pi/projects/donkeycar/donkeycar/parts/keras.py", line 50, in load self.model = keras.models.load_model(model_path, compile=False) File "/home/pi/env/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py", line 184, in load_model return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile) File "/home/pi/env/lib/python3.7/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 178, in load_model_from_hdf5 custom_objects=custom_objects) File "/home/pi/env/lib/python3.7/site-packages/tensorflow/python/keras/saving/model_config.py", line 55, in model_from_config return deserialize(config, custom_objects=custom_objects) File "/home/pi/env/lib/python3.7/site-packages/tensorflow/python/keras/layers/serialization.py", line 109, in deserialize printable_module_name='layer') File "/home/pi/env/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 362, in deserialize_keras_object config, module_objects, custom_objects, printable_module_name) File "/home/pi/env/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 321, in class_and_config_for_serialized_keras_object raise ValueError('Unknown ' + printable_module_name + ': ' + class_name) ValueError: Unknown layer: Functional I have tried setting Google Colab to match tensorflow versions: %tensorflow_version 2.2 But I can't set 2.2.. I'm stuck with the latest version, tensorflow 2.5: %tensorflow_version` only switches the major version: 1.x or 2.x. You set: `2.2`. This will be interpreted as: `2.x`. TensorFlow 2.x selected. Any suggestions on how to either A) Set tensorflow v 2.2 on google colab, or B) allow my car to read a model created with tensorflow 2.5? Thanks!
ender18g Updated