Peter Goldsborough goldsborough

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

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

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

	open Printf

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

	let token_to_string = function

	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;

	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

	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

	#!/usr/bin/env python

	import argparse
	import json
	import re
	import subprocess
	import sys

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

	import argparse
	import collections
	import datetime
	import math
	import numpy as np
	import os
	import sys
	import tensorflow as tf
	import time
	import warnings

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