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• Idiomatic vimrc (superseded by idiomatic-vimrc)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

This is a list of custom pseudo-text objects for Vim.

Note the use of "pseudo", here. Conceptually, text object are "special" motions but there is no proper mechanism dedicated to creating text objects, specifically. We are left with the generic mechanism succinctly described under the easy-to-miss :help omap-info.

Writing one's own pseudo-text objects and motions is a great way to extend Vim without perverting its nature ;-).

---

My Vim-related gists.

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