tam17aki

# -*- coding: utf-8 -*-

# Copyright (C) 2017 by Akira TAMAMORI

# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT

wookayin

Displaying Configurations in TensorBoard

This is a simple demonstration of displaying training configuration in a TensorBoard, using text summaries.

Screenshot

romainl

My Vim-related gists

Long-form

• How to use Tim Pope's Pathogen
• Idiomatic vimrc (superseded by idiomatic-vimrc)
• Clipboard sharing on Mac OS X is easy
• Apprentice — The Making Of
• Vim: pushing built-in features beyond their limits
• The right way to override any highlighting if you don't want to edit the colorscheme file directly

swyoon

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

romainl

" 24 simple pseudo-text objects
" -----------------------------
" i_ i. i: i, i; i| i/ i\ i* i+ i- i#
" a_ a. a: a, a; a| a/ a\ a* a+ a- a#
" can take a count: 2i: 3a/
for char in [ '_', '.', ':', ',', ';', '<bar>', '/', '<bslash>', '*', '+', '-', '#' ]
	execute "xnoremap i" . char . " :<C-u>execute 'normal! ' . v:count1 . 'T" . char . "v' . (v:count1 + (v:count1 - 1)) . 't" . char . "'<CR>"
	execute "onoremap i" . char . " :normal vi" . char . "<CR>"
	execute "xnoremap a" . char . " :<C-u>execute 'normal! ' . v:count1 . 'F" . char . "v' . (v:count1 + (v:count1 - 1)) . 'f" . char . "'<CR>"
	execute "onoremap a" . char . " :normal va" . char . "<CR>"

teamdandelion

7
2
1
0
4
1
4
9
5
9

wookayin

# in a courtesy of Caffe's filter visualization example
# http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb

def imshow_grid(data, height=None, width=None, normalize=False, padsize=1, padval=0):
    '''
    Take an array of shape (N, H, W) or (N, H, W, C)
    and visualize each (H, W) image in a grid style (height x width).
    '''
    if normalize:
        data -= data.min()

wookayin

# use the following snippet in your ipython notebook shell
import argparse
import tensorflow as tf

tf.app.flags.FLAGS = tf.python.platform.flags._FlagValues()
tf.app.flags._global_parser = argparse.ArgumentParser()

marcorei

/**
 * Creates a menu entry in the Google Docs UI when the document is opened.
 *
 * @param {object} e The event parameter for a simple onOpen trigger. To
 *     determine which authorization mode (ScriptApp.AuthMode) the trigger is
 *     running in, inspect e.authMode.
 */
function onOpen(e) {
  DocumentApp.getUi().createAddonMenu()
      .addItem('Clean Table of Contents', 'cleanToC')

eddieespinal

    //This is the swift version of the following gist by @shexbeer https://gist.github.com/shexbeer/cb069d36ca8ec5edb515
    func cropCameraImage(original: UIImage, previewLayer: AVCaptureVideoPreviewLayer) -> UIImage? {
        
        var image = UIImage()
        
        let previewImageLayerBounds = previewLayer.bounds
        
        let originalWidth = original.size.width
        let originalHeight = original.size.height