juwencheng

# 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

package main

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

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

juwencheng

package main

import (
	"io/ioutil"
	"time"
)

func main() {
	counter := 0
	go func() {

juwencheng

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

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

juwencheng

// 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

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

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

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

# coding=utf-8

print("Hello World!")
print("你好!")

juwencheng

"""
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)