Ng Kam Woh kamwoh

Installing Apache Superset on Windows 10

⚠️ WARN: This doc might be outdated. Use with caution. Only tested with Python v3.7

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	from __future__ import print_function, absolute_import

	__all__ = ['accuracy']

	def accuracy(output, target, topk=(1,)):
	"""Computes the precision@k for the specified values of k"""
	maxk = max(topk)
	batch_size = target.size(0)

	_, pred = output.topk(maxk, 1, True, True)

	#!/bin/bash
	#
	# script to extract ImageNet dataset
	# ILSVRC2012_img_train.tar (about 138 GB)
	# ILSVRC2012_img_val.tar (about 6.3 GB)
	# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
	#
	# https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
	#
	# train/

	#!/bin/bash

	wget https://upload.wikimedia.org/wikipedia/en/9/94/Snipping_Tool_10_Icon.png ~/Pictures/snipping.png

	cd ~/.local/share/applications/
	echo "[Desktop Entry]" > snipping-tools.desktop
	echo "Version=1.0" >> snipping-tools.desktop
	echo "Name=Snipping Tools" >> snipping-tools.desktop
	echo "Comment=A shortcut like Window Snipping Tools" >> snipping-tools.desktop
	echo "Exec=/usr/bin/gnome-screenshot -a -i" >> snipping-tools.desktop

	"""
	Create train, valid, test iterators for CIFAR-10 [1].
	Easily extended to MNIST, CIFAR-100 and Imagenet.

	[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
	"""

	import torch
	import numpy as np

	# Create a simple TF Graph
	# By Omid Alemi - Jan 2017
	# Works with TF <r1.0

	import tensorflow as tf

	I = tf.placeholder(tf.float32, shape=[None,3], name='I') # input
	W = tf.Variable(tf.zeros_initializer(shape=[3,2]), dtype=tf.float32, name='W') # weights
	b = tf.Variable(tf.zeros_initializer(shape=[2]), dtype=tf.float32, name='b') # biases
	O = tf.nn.relu(tf.matmul(I, W) + b, name='O') # activation / output

	import multiprocessing
	from multiprocessing.dummy import Pool as ThreadPool

	import numpy as np


	def my_multipro(items, func, max_cpus=12):
	"""Do an embarrassingly parallel task using multiprocessing.

	Use this for CPU bound tasks.

	# Remove anything linked to nvidia
	sudo apt-get remove --purge nvidia*
	sudo apt-get autoremove

	# Search for your driver
	apt search nvidia

	# Select one driver (the last one is a decent choice)
	sudo apt install nvidia-370