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Note: This gist may be outdated, thanks to all contributors in comments.
adb is the Android CLI tool with which you can interact with your android device, from your PC
You must enable developer mode (tap 7 times on the build version in parameters) and install adb on your PC.
Don't hesitate to read comments, there is useful tips, thanks guys for this !
If you use git on the command-line, you'll eventually find yourself wanting aliases for your most commonly-used commands. It's incredibly useful to be able to explore your repos with only a few keystrokes that eventually get hardcoded into muscle memory.
Some people don't add aliases because they don't want to have to adjust to not having them on a remote server. Personally, I find that having aliases doesn't mean I that forget the underlying commands, and aliases provide such a massive improvement to my workflow that it would be crazy not to have them.
The simplest way to add an alias for a specific git command is to use a standard bash alias.
# .bashrc
| """ | |
| stable diffusion dreaming | |
| creates hypnotic moving videos by smoothly walking randomly through the sample space | |
| example way to run this script: | |
| $ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry | |
| to stitch together the images, e.g.: | |
| $ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4 |
| # Clone llama.cpp | |
| git clone https://github.com/ggerganov/llama.cpp.git | |
| cd llama.cpp | |
| # Build it | |
| make clean | |
| LLAMA_METAL=1 make | |
| # Download model | |
| export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin |
| """Script to illustrate usage of tf.estimator.Estimator in TF v1.3""" | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data as mnist_data | |
| from tensorflow.contrib import slim | |
| from tensorflow.contrib.learn import ModeKeys | |
| from tensorflow.contrib.learn import learn_runner | |
| # Show debugging output |
| /* | |
| * Copyright 2014 Chris Banes | |
| * | |
| * 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 |
You can use this class to realize a simple sectioned grid RecyclerView.Adapter without changing your code.
The RecyclerView has to use a GridLayoutManager.
This is a porting of the class SimpleSectionedListAdapter provided by Google
If you are looking for a sectioned list RecyclerView.Adapter you can take a look here
name: CaffeNet fine-tuned on the Oxford 102 category flower dataset
caffemodel: oxford102.caffemodel
caffemodel_url: https://s3.amazonaws.com/jgoode/oxford102.caffemodel
gist_id: 0179e52305ca768a601f
license: BSD-3
| from math import sqrt | |
| def put_kernels_on_grid (kernel, pad = 1): | |
| '''Visualize conv. filters as an image (mostly for the 1st layer). | |
| Arranges filters into a grid, with some paddings between adjacent filters. | |
| Args: | |
| kernel: tensor of shape [Y, X, NumChannels, NumKernels] | |
| pad: number of black pixels around each filter (between them) |
