Prajj prajjwal1

Summary of Fastai.ai's in-depth discussion of types of normalization

# simulated batch of images
x = torch.rand(64, 3, 224, 224)
# or some number of layers up the convolutional stack
x = torch.rand(64, 256, 32, 32)

	import torch
	import torch.nn.functional as F


	def maml_grad(model, inputs, outputs, lr, batch=1):
	"""
	Update a model's gradient using MAML.

	The gradient will point in the direction that
	improves the total loss across all inner-loop

	#!/usr/bin/env python3

	"""
	Example of a generic Mixout implementation. (Lee et al., 2019).
	https://arxiv.org/abs/1909.11299

	Implementation by Stephen Roller (https://stephenroller.com).

	Updated 2020-02-10 to include 1/(1 - p) correction term. Thanks to
	Cheolhyoung Lee for making this correction.

	# Copyright (c) 2019-present, Thomas Wolf.
	# All rights reserved. This source code is licensed under the MIT-style license.
	""" A very small and self-contained gist to train a GPT-2 transformer model on wikitext-103 """
	import os
	from collections import namedtuple
	from tqdm import tqdm
	import torch
	import torch.nn as nn
	from torch.utils.data import DataLoader
	from ignite.engine import Engine, Events