| /** | |
| * 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') |
| # 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() |
| # 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() |
| 7 | |
| 2 | |
| 1 | |
| 0 | |
| 4 | |
| 1 | |
| 4 | |
| 9 | |
| 5 | |
| 9 |
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 ;-).
| 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. |
This is a simple demonstration of displaying training configuration in a TensorBoard, using text summaries.
| # -*- 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 |
| brew options ffmpeg | |
| brew install ffmpeg \ | |
| --with-chromaprint \ | |
| --with-fdk-aac \ | |
| --with-fontconfig \ | |
| --with-freetype \ | |
| --with-frei0r \ | |
| --with-game-music-emu \ | |
| --with-libass \ |
