hjian42’s gists

wwbrannon / create-matlaber-env.sh

Last active November 17, 2022 18:56

Set up matlaber environment

	#!/bin/bash

	set -xe

	export PYTHON_VERSION=3.9
	export CUDA_VERSION=11.3
	export JUPYTER_PORT="$(id -u)"

	##
	## Make SSL certs

yuchenlin / gpt_sent_prob.py

Last active January 14, 2025 04:41

Compute sentence probability using GPT-2 with huggingface transformers

	import torch
	from transformers import OpenAIGPTTokenizer, OpenAIGPTLMHeadModel
	from transformers import GPT2Tokenizer, GPT2LMHeadModel
	import numpy as np
	from scipy.special import softmax

	def model_init(model_string, cuda):
	if model_string.startswith("gpt2"):
	tokenizer = GPT2Tokenizer.from_pretrained(model_string)
	model = GPT2LMHeadModel.from_pretrained(model_string)

HarshTrivedi / pad_packed_demo.py

Last active November 7, 2025 15:47 — forked from Tushar-N/pad_packed_demo.py

Minimal tutorial on packing (pack_padded_sequence) and unpacking (pad_packed_sequence) sequences in pytorch.

	import torch
	from torch import LongTensor
	from torch.nn import Embedding, LSTM
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
	#
	# Step 1: Construct Vocabulary
	# Step 2: Load indexed data (list of instances, where each instance is list of character indices)

ragulpr / py

Last active December 7, 2020 10:24

Keras masking example

	import keras
	from keras.layers import *
	from keras.models import Model
	import theano as T
	import tensorflow as tf
	print('theano ver.',T.__version__)
	print('tensorflow ver.',tf.__version__)
	print('keras ver.',keras.__version__)
	np.set_printoptions(precision=4)
	np.random.seed(1)

cbaziotis / AttentionWithContext.py

Last active April 25, 2022 14:37

Keras Layer that implements an Attention mechanism, with a context/query vector, for temporal data. Supports Masking. Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] "Hierarchical Attention Networks for Document Classification"

	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	x (): input
	kernel (): weights
	Returns:
	"""
	if K.backend() == 'tensorflow':

cbaziotis / Attention.py

Last active October 22, 2024 08:31

Keras Layer that implements an Attention mechanism for temporal data. Supports Masking. Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]


	from keras import backend as K, initializers, regularizers, constraints
	from keras.engine.topology import Layer


	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:

Orbifold / Embedding and Tokenizer in Keras.ipynb

Created January 10, 2017 07:41

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jerrybendy / index.html

Created January 5, 2017 03:33

一个使用 HTML5 录音的例子（网上看到的，收藏下）

	<!DOCTYPE html>
	<html xmlns="http://www.w3.org/1999/xhtml">
	<head>
	<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
	<title></title>
	</head>
	<body>
	<div>
	<audio controls autoplay></audio>
	<input onclick="startRecording()" type="button" value="录音" />

wassname / keras_attention_wrapper.py

Created November 1, 2016 08:06

A keras attention layer that wraps RNN layers.

	"""
	A keras attention layer that wraps RNN layers.

	Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506)
	and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

	date: 20161101
	author: wassname
	url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
	"""

mbollmann / attention_lstm.py

Last active August 22, 2024 07:06

My attempt at creating an LSTM with attention in Keras

	class AttentionLSTM(LSTM):
	"""LSTM with attention mechanism

	This is an LSTM incorporating an attention mechanism into its hidden states.
	Currently, the context vector calculated from the attended vector is fed
	into the model's internal states, closely following the model by Xu et al.
	(2016, Sec. 3.1.2), using a soft attention model following
	Bahdanau et al. (2014).

	The layer expects two inputs instead of the usual one:

Hang Jiang hjian42