- act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
- apk2vec https://arxiv.org/abs/1809.05693
- app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
- ast2vec https://arxiv.org/abs/2103.11614
- attribute2vec https://arxiv.org/abs/2004.01375
- author2vec http://dl.acm.org/citation.cfm?id=2889382
- baller2vec https://arxiv.org/abs/2102.03291
- bb2vec https://arxiv.org/abs/1809.09621
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# Steps to build and install tmux from source. | |
# Takes < 25 seconds on EC2 env [even on a low-end config instance]. | |
VERSION=2.7 | |
sudo yum -y remove tmux | |
sudo yum -y install wget tar libevent-devel ncurses-devel | |
wget https://github.com/tmux/tmux/releases/download/${VERSION}/tmux-${VERSION}.tar.gz | |
tar xzf tmux-${VERSION}.tar.gz | |
rm -f tmux-${VERSION}.tar.gz | |
cd tmux-${VERSION} |
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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# Context manager to generate batches in the background via a process pool | |
# Usage: | |
# | |
# def batch(seed): | |
# .... # generate minibatch | |
# return minibatch | |
# | |
# with BatchGenCM(batch) as bg: | |
# minibatch = next(bg) | |
# .... # do something with minibatch |
$ uname -r
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from keras.optimizers import Adam | |
from keras import backend as K | |
from keras.datasets import mnist | |
from keras.utils.np_utils import to_categorical | |
from keras.metrics import categorical_accuracy | |
from keras.initializations import glorot_uniform, zero | |
import numpy as np | |
# inputs and targets are placeholders | |
input_dim = 28*28 |