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| from keras.preprocessing.image import ImageDataGenerator | |
| # Rescaling all images by 1/255 | |
| train_datagen = ImageDataGenerator(rescale=1./255) | |
| test_datagen = ImageDataGenerator(rescale=1./255) | |
| train_generator = train_datagen.flow_from_directory( | |
| directory=TRAIN_DIR, | |
| target_size=(150, 150), # Resizing all images to 150 x 150 | |
| batch_size=20, |
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| from keras import models | |
| from keras import layers | |
| model = models.Sequential() | |
| model.add(layers.Conv2D( | |
| filters=32, | |
| kernel_size=(3, 3), | |
| strides=(1, 1), | |
| padding='valid', |
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| DATASET_DIR = 'your-dataset-directory' | |
| BASE_DIR = os.getcwd() + '\\trainset' | |
| os.mkdir(BASE_DIR) | |
| TRAIN_DIR = os.path.join(BASE_DIR, 'train') | |
| TRAIN_DOGS_DIR = os.path.join(TRAIN_DIR, 'dogs') | |
| TRAIN_CATS_DIR = os.path.join(TRAIN_DIR, 'cats') | |
| os.mkdir(TRAIN_DIR) |
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| server { | |
| listen 1222; | |
| listen [::]:1222; | |
| location / { | |
| proxy_pass http://0.0.0.0:8888; | |
| proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; | |
| proxy_set_header X-Real-IP $remote_addr; | |
| proxy_set_header Host $http_host; | |
| proxy_http_version 1.1; | |
| proxy_redirect off; |
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| [program:jupyter] | |
| directory=/home/ubuntu/jupyter-notebooks/ | |
| command=sudo jupyter notebook --allow-root --config=/home/ubuntu/.jupyter/jupyter_notebook_config.py | |
| autostart=true | |
| autorestart=true | |
| stderr_logfile=/var/log/jupyter.err.log | |
| stdout_logfile=/var/log/jupyter.out.log |
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| import threading | |
| import time | |
| import requests | |
| import json | |
| import subprocess | |
| def torrent(port): | |
| command = subprocess.Popen(['qbittorrent-nox', f'--webui-port={port}']) | |
| def ngrok(port): |
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| TOKEN = "YOUR TOKEN HERE" | |
| def install_ngrok(): | |
| import os | |
| from zipfile import ZipFile | |
| from urllib.request import urlretrieve | |
| url = 'https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip' | |
| urlretrieve(url, 'ngrok-amd64.zip') | |