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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
  • Updated Jun 28, 2020
  • Python
TCHERAKAmine
TCHERAKAmine commented Apr 5, 2020

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 ?

pranavsharma
pranavsharma commented Feb 27, 2020

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

autokeras
gabrieldemarmiesse
gabrieldemarmiesse commented Jan 20, 2020

Adding types on the public API surface would allow us to do some runtime type checking later on and would allow user's IDE to have more info for static analysis.

The functions/signatures to type are the ones listed here https://github.com/keras-team/autokeras/blob/master/autokeras/__init__.py

For the context, see #856 where I add some type information on a ImageClassifier.

This issue can

bersbersbers
bersbersbers commented Sep 11, 2019

Platform (like ubuntu 16.04/win10): Windows 10
Python version: 3.7.4, mmdnn==0.2.5

Running scripts: mmconvert -f caffe -df keras -om test

I know that this command is not supposed to run without passing an input file, but the error message is incorrect and should be improved:

mmconvert: error: argument --srcFramework/-f: invalid choice: 'None' (choose from 'caffe', 'caffe2', 'cn

yiheng
yiheng commented Jul 11, 2018

Spark 2.3 officially support run on kubernetes. While our guide of "Run on Kubernetes" is still based on a special version of Spark 2.2, which is out of date. We need to:

  1. update that document to Spark 2.3
  2. release the corresponding docker images.

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