Peter Goldsborough goldsborough

layout	title	summary	date	categories
post	A Promenade of PyTorch	A brief overview of the most popular dynamic-graph deep learning framework	2018-02-04 20-17-20	ml ai python

For the past two years, I've been quite heavily invested in TensorFlow, either writing papers about it, giving

PyTorch

PyTorch is, at its core, a Python library enabling GPU-accelerated tensor computation, similar to NumPy. On top of this, PyTorch provides a rich API for neural network and other machine learning workloads.

PyTorch differentiates itself from other machine learning frameworks in that it does not use static computational graphs -- defined once, ahead of time -- like TensorFlow. Instead, PyTorch computation graphs are dynamic and defined by run. This means that each invocation of a machine learning model's layers define a new computation graph, on the fly. The creation of this graph is implicit, in the sense that the library takes care of recording the flow of data

	#!/usr/bin/env python

	import argparse
	import json
	import re
	import subprocess
	import sys

	DEFAULT_FILE_PATTERN = r'.*\.[ch](pp)?'

	from __future__ import print_function
	from __future__ import division

	import argparse

	import matplotlib.pyplot as plot
	import numpy as np
	import torch.utils.data
	import torchvision.datasets
	from torch import nn, optim

	from __future__ import print_function
	from __future__ import division

	import argparse

	import matplotlib.pyplot as plot
	import numpy as np
	import torch.utils.data
	import torchvision.datasets
	from torch import nn, optim

	import argparse

	import numpy as np
	import torch.utils.data
	import torchvision.datasets
	from torch import nn, optim
	from torch.autograd import Variable
	from torchvision import transforms

	const float two_pi = 2.0f * 3.14159265358979323846f;

	for (int i = 0; i < kNumber; i += 16) {
	for (int j = 0; j < 8; ++j) {
	const float u1 = 1 - uniform[i + j];
	const float u2 = uniform[i + 8 + j];

	const float radius = std::sqrt(-2 * std::log(u1));
	const float theta = two_pi * u2;

	open Printf

	type token =
	\| Digit of int
	\| Operator of char
	\| LeftParen
	\| RightParen
	;;

	let token_to_string = function

	# Your keymap
	#
	# Atom keymaps work similarly to style sheets. Just as style sheets use
	# selectors to apply styles to elements, Atom keymaps use selectors to associate
	# keystrokes with events in specific contexts. Unlike style sheets however,
	# each selector can only be declared once.
	#
	# You can create a new keybinding in this file by typing "key" and then hitting
	# tab.
	#

	{
	"0.png": true,
	"1.png": false
	}