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	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

	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
	}