Juwencheng juwencheng

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Working from home

juwencheng / git-fancy-pancy.sh

Created April 13, 2016 09:32 — forked from sverweij/git-fancy-pancy.sh

Fancy command line git history

	# shamelessly copied from @d
	git log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset' --abbrev-commit --date=relative

juwencheng / example_go_filepath_Walk.go

Created January 9, 2017 06:18

example of filepath.Walk function

	package main

	import (
	"fmt"
	"os"
	"path/filepath"
	)

	func main() {
	walfFn := func(path string, info os.FileInfo, err error) error {

juwencheng / test_overwrite_file.go

Last active January 10, 2017 07:49

将内容以覆盖的形式写入文件中

juwencheng / compress.go

Created January 10, 2017 13:19 — forked from iamralch/compress.go

ZIP archives in Golang

	import (
	"archive/zip"
	"io"
	"os"
	"path/filepath"
	"strings"
	)

	func zipit(source, target string) error {
	zipfile, err := os.Create(target)

juwencheng / measure_memory_with_go.go

Created January 12, 2017 03:02

通过pid查看消耗内存

	// ref: http://stackoverflow.com/questions/31879817/golang-os-exec-realtime-memory-usage?noredirect=1&lq=1
	func calculateMemory(pid int) (uint64, error) {

	f, err := os.Open(fmt.Sprintf("/proc/%d/smaps", pid))
	if err != nil {
	return 0, err
	}
	defer f.Close()

	res := uint64(0)

juwencheng / pickle_tutorial.py

Created February 16, 2017 01:13

通过Pickle保存和加载数据

	import pickle
	# save object to pickle
	file = open('data.pickle', 'wb') # `b` abbr of binary
	pickle.dump({'key':'value'}, file)
	file.close()

	# load object from pickle
	file = open('data.pickle', 'rb')
	dict = pickle.load(file)
	file.close()

juwencheng / deep_neural_network_with_hidden_layer.py

Last active May 11, 2017 06:39

Create a simple deep neural network just with one hidden layer. reached 95.1% accuracy

	from tensorflow.examples.tutorials.mnist import input_data

	# 如果 one_hot 设为 False , 标签则会输出数字
	mnist = input_data.read_data_sets("../MNIST_data", one_hot=True)
	import tensorflow as tf

	batch_size = 50
	display_step = 2
	learning_rate = 0.03
	training_epochs = 30

juwencheng / conv_network.py

Last active May 11, 2017 06:45

convolution network , input-conv-maxpool-fc-output

	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data

	# input
	def neural_input(image_shape, name="x"):
	return tf.placeholder(tf.float32, [None, *image_shape], name=name)

	def neural_label(n_classes, name="labels"):
	return tf.placeholder(tf.float32, [None, n_classes], name=name)

juwencheng / floyd-tutorial-hello.py

Last active May 12, 2017 01:51

floyd tutorial demo

juwencheng / min-char-rnn.py

Last active May 13, 2017 05:48 — forked from karpathy/min-char-rnn.py

Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)