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@mrry
mrry / tensorflow_self_check.py
Last active August 24, 2023 17:13
[DEPRECATED] TensorFlow on Windows self-check
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
@teamdandelion
teamdandelion / labels_1024.tsv
Last active February 6, 2024 08:33
TensorBoard: TF Dev Summit Tutorial
We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.
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@danijar
danijar / blog_tensorflow_scope_decorator.py
Last active January 17, 2023 01:58
TensorFlow Scope Decorator
# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def doublewrap(function):
"""
A decorator decorator, allowing to use the decorator to be used without
@vinhkhuc
vinhkhuc / min-char-rnn-tensorflow.py
Last active May 17, 2019 02:48
Vanilla Char-RNN using TensorFlow
"""
Vanilla Char-RNN using TensorFlow by Vinh Khuc (@knvinh).
Adapted from Karpathy's min-char-rnn.py
https://gist.github.com/karpathy/d4dee566867f8291f086
Requires tensorflow>=1.0
BSD License
"""
import random
import numpy as np
import tensorflow as tf
@hellerbarde
hellerbarde / latency.markdown
Created May 31, 2012 13:16 — forked from jboner/latency.txt
Latency numbers every programmer should know

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

@dupuy
dupuy / README.rst
Last active May 5, 2024 18:42
Common markup for Markdown and reStructuredText

Markdown and reStructuredText

GitHub supports several lightweight markup languages for documentation; the most popular ones (generally, not just at GitHub) are Markdown and reStructuredText. Markdown is sometimes considered easier to use, and is often preferred when the purpose is simply to generate HTML. On the other hand, reStructuredText is more extensible and powerful, with native support (not just embedded HTML) for tables, as well as things like automatic generation of tables of contents.