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# Dat Nguyen datlife

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Last active Nov 1, 2018
View move_forward.py
 """Move Forward Start from the first element in the array (A), index is i. Move forward by A[i] steps max. The algorithm is to return true/false, to indicate whether we can move from the first element to the last element in the array A = [2, 0, 1, 2, 0, 3] --> True # Assumption: ------------- * A[i] > 0 and there are [1 ... N] possible steps A[i] can move if A[i] = N.
Last active Jul 9, 2018
Compute mean and standard deviation (std) per channel give a list of images
View compute_mean_std_per_channel.py
 """Give a list of image filenames, this script compute the mean and std over all the images. Requires: * OpenCV * tqdm In this example, I use `Stanford Dogs Datasets" (~20k images) Example Outputs: (8 cores CPU i7 4970K) (env) dat@desktop:****/StanfordDogs\$ python compute_mean_std.py
Last active Apr 7, 2018
Fresh Ubuntu 16.04.04, Jetson TX2
Last active Mar 14, 2018
Comparisons: How to efficiently iterate in pandas data frames by row.
View test_bench.py
 """ Problem: -------- We would like to explore which method perform row iteration in the most efficient way. Create a data frammes with 3 columns and 100,000 rows Results: -------- vector: Iterated over 100000 rows in 0.029180 | Sample at idx [0]: (1, 100000) zip: Iterated over 100000 rows in 0.073447 | Sample at idx [0]: [1, 100000]
Created Feb 20, 2018
Convert list of images into a MP4 H264
View gist:4819a1774de689916dcaa8fca2c42c86
 ffmpeg -framerate 25 -i img%05d.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p output.mp4
Created Feb 2, 2018
Export pre-trained TF Object Detection API model to Tensorflow Serving
View export_tfserving.py
 """ Thiss script would convert a pre-trained TF model to a servable version for TF Serving. A pre-trained model can be downloaded here https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo Requirements: * A directory contains pretrained model (can be download above). * Edit three arguments `frozen_graph`, `model_name`, `base_dir` accordingly
Last active Jan 25, 2021
Training Keras model with tf.data
View mnist_tfdata.py
 """An example of how to use tf.Dataset in Keras Model""" import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after _EPOCHS = 5 _NUM_CLASSES = 10 _BATCH_SIZE = 128 def training_pipeline(): # ############# # Load Dataset
Last active Jun 29, 2018 — forked from Chaser324/GitHub-Forking.md
GitHub Standard Fork & Pull Request Workflow
View contribution_pipeline.md

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

## Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j