Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template

System information

• TensorFlow version (you are using): 2.1
• Are you willing to contribute it (Yes/No): No

Please implement the latest examples in the examples/ folder to the docs/mkdocs.yml to see theses examples on the homepage. Perhaps a subsectioning for that many examples is necessary. See Homepage

Thank you!

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb

trainable_variables = weights.values() + biases.values() doesn't work.
Also if I write trainable_variables = list(weights.values()) + list(biases.values()), I have to turn on tf.enable_eager_execution(), but the training result is wrong, accuracy is ar

• 原文链接：A Beginner’s Guide to Simulating Dynamical Systems with Python
• Markdown文件：文件地址
• PR 地址：
• 文章分类：算法，AI
• 注意：文件位置在 TODO1 文件夹中，不是之前的 TODO 了。

Please provide a barebones "pick up and go" GPT-2 colab notebook for text generation, just like gpt-2-simple does

When you look at the variables in the pretrained base uncased BERT the varibles look like list 1. When you do the training from scratch, 2 additional variables per layer are introduced, with suffixes adam_m and adam_v. It would be nice for someone to explain what these variables are? and what is their significance to the process of training?
If one were to manually initialize variables from a pri

Considering the MNIST dataset, wich has 5923 instances of the 0 class in the training set, I'm alittle confused about the following code for detemining the relative errors of the SGD classification model:

row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums

(https://github.com/ageron/handson-ml/blob/master/03_classification.ipynb // In: 67)

Since using `axi

Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

This has a very good explanation!!

Does any of you donne this without mask, I mean a new branch but with the mask remove just bouding boxes?

This is starting to become a frequent question. If the scorer has not been downloaded via git lfs, the error message ValueError: Scorer initialization failed with error code 1 will appear. This is not a very helpful error message and I feel that a better message that actually describes the problem and how to solve it would be better and prevent this question being asked so many times.

This al

if name == 'main':
main(parse_arguments(sys.argv[1:]))
Traceback (most recent call last):

File "", line 2, in
main(parse_arguments(sys.argv[1:]))

File "", line 16, in parse_arguments
return parser.parse_args(argv)

File "C:\Users\DELL\Anaconda3\lib\argparse.py", line 1730, in parse_args

Looping the process of writing images into the .tfrecords-file works fine, but how do I read multiple images from a .tfrecords-file?

Is there any simple solution? would be great if added to the code.

I've just started learning a bit about Tensorflow, and I tried out the Fashion MNIST tutorial. However, I keep getting a list index out of range error. I've copied the code from the official tutorial and also tried using tf.reset_default_graph() as suggested in some other posts, but neither have worked.

Here is the notebook with the error:
https://github.com/fsiraj/Tensorflow-Tutorials/blob/ma

I've ran into this issue for a couple hours and I ended up editing the dist library adding two new functions called fetchVideo and bufferToVideo that works pretty much like the fetchImage and bufferToImage functions.

I'll leave it here to help somebody else with the same issue and in case someone wants to include it on future releases.

face-api.js

...
exports.fetchVideo = fetc

Environment:

1. Framework: PyTorch
2. Framework version: 1.3.1
3. Horovod version: 0.19.0
4. MPI version: 4.0.2
5. CUDA version: N/A
6. NCCL version: N/A
7. Python version: 3.7.5
8. OS and version: Mac OS 10.15.2
9. GCC version: 9.2.0

Checklist:

1. Did you search issues to find if somebody asked this question before? Yes
2. If your question is about hang, did you read [this d

"scipy.misc.imsave" was removed in scipy version 1.3
--> pip install scipy==1.2.1

https://docs.scipy.org/doc/scipy/reference/release.1.3.0.html#scipy-interpolate-changes

and functions from scipy.misc (bytescale, fromimage, imfilter, imread, imresize, imrotate, imsave, imshow, toimage) have been removed.

/kind feature

Persona: Infrastructure Engineer
Control Plane Walkthrough:

• Install Kubeflow with (istio) KfDefs
• Install Kubeflow with dex - pending
• Configure / Get Istio IngressGateway endpoint
• Create profiles (w/ kubectl) #4725
• Create notebooks from user-namespace (w/ kubectl)
• Login (port-forward) into notebook as user
• Create TFJob (w/ kubec

There are some minor warnings while building docs. It can cause future issues, so I'll fix them.
(in my environment, there are 11 warnings running the build)

/Users/hanbyulkim/Desktop/dev/sonnet/sonnet/src/axis_norm.py:docstring of sonnet.LayerNorm.init:5: WARNING: Unexpected indentation.
/Users/hanbyulkim/Desktop/dev/sonnet/sonnet/src/optimizers/momentum.py:docstring of sonnet.optimizers

Tutorial 19 worked fine with TensorFlow v. 1.4.0 and Keras v. 2.0.8-tf. But I have just upgraded to TensorFlow 1.9 and Keras 2.1.6-tf and now I get an error when trying to load the model with the best hyper-parameters. I think it is a bug in Keras because many other people have experienced variations of the same error-message, see e.g. tensorflow/tensorflow#20073

We wi

Several parts of the op sec like the main op description, attributes, input and output descriptions become part of the binary that consumes ONNX e.g. onnxruntime causing an increase in its size due to strings that take no part in the execution of the model or its verification.

Setting __ONNX_NO_DOC_STRINGS doesn't really help here since (1) it's not used in the SetDoc(string) overload (s