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System information

• Have I written custom code (as opposed to using a stock
  example script provided in TensorFlow): Yes

In Keras documentation, glorot_uniform says that the initializer is using Glorot Uniform from this paper. However, the Keras implementation is totally different from the equation on the paper. Also, there are some arguments such as mode ='fan_avg' is the default. It should be same as the referenced paper. 'fan_sum'. Golort uniform is shown

https://github.com/aymericdamien/TensorFlow-Examples/blob/9e1bb504f5d0f209d000997ce2ad95bb891798ab/examples/3_NeuralNetworks/bidirectional_rnn.py#L81

I think "outputs [-1]" and "outputs [0]" are equivalent (reversed) in this line of code, but the former (89%) works better than the latter (86%). Why?

Many models have identical implementations of prune_heads it would be nice to store that implementation as a method on PretrainedModel and reduce the redundancy.

• 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。

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

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

Hello All;

Hope your are doing well;

In Mask RCNN I would like change the color of the mask to be White with the Alpha = 1, I change it frome the right place in vizualize.py, but anything change the mask color still red or blue or another color, why the changes on vizualize.py dont have effect ?

Currently we only have build instructions, mentions of copying binaries around, and no mention of NuGet.

Dears,

if somebody has instructions , my target is to recognize a face using tensor flow but I didn't find a full instructions which scripts to use to do the following :
1- crop faces
2- Train images .
3- face recognition.

if examples or sample commands that would be appreciated!

I have Ubuntu Linux with python and tensor flow environment ready.

appreciate your steps to go forward

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

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.

Example scripts contains some dependencies not listed for Horovod, and in some cases require datasets without explaining how to obtain them. We should provide a README file along with a set of packages (requirements.txt) for successfully running the examples.

load_csv line can be since there's columns_to_ignore param's supported

data, labels = load_csv('titanic_dataset.csv', target_column=0, columns_to_ignore=[2, 7], categorical_labels=True, n_classes=2)

and we don't need to do that in preprocess()

def preprocess(passengers):
    for i in range(len(passengers)):
        passengers[i][1] = 1. if passengers[i][1] == 'female' else 0.

/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

"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.

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