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{ | |
"graph": [], | |
"links": [ | |
{"source": 0, "target": 1}, | |
{"source": 0, "target": 2}, | |
{"source": 0, "target": 3}, | |
{"source": 0, "target": 4}, | |
{"source": 0, "target": 5}, | |
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{"source": 1, "target": 3}, |
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import numpy | |
from keras.models import Model | |
from keras.layers import Input, Dropout, TimeDistributed | |
batch_size = 10 | |
timesteps = 5 | |
nb_features = 7 | |
dropout = 0.5 | |
input = Input(batch_shape=(batch_size, timesteps, nb_features)) |
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<!DOCTYPE html> | |
<html> | |
<body style="overflow:hidden; margin:0; text-align:center;"> | |
<img src="https://file-examples-com.github.io/uploads/2017/10/file_example_JPG_2500kB.jpg" style="height:100vh; max-width:100%; object-fit: contain;"> | |
</body> | |
</html> |
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# to run: | |
# python gsheet_fetcher.py | |
try: | |
import requests # faster - need to pip install requests | |
get = requests.get | |
req = True | |
except: | |
import urllib.request | |
get = urllib.request.urlopen |
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"""Usage: python3 delchunk.py BIG_FILE CHUNK_FILE_OR_FOLDER [OUT_FILE] | |
Given a large file BIG_FILE, delete all complete non-overlapping (and possibly large) chunks given by CHUNK_FILE_OR_FOLDER | |
While each chunk file is treated statelessly (AABABB-AB=AB), multiple chunk files are treated statefully (ACABDB-AB-CD=AB) | |
Multiple chunk files will be deleted from the largest to the smallest (and secondarily by chunk filename) | |
If OUT_FILE is omitted, will do a dryrun | |
""" | |
import mmap |
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