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andcarnivorous /
Created Oct 2, 2019
streaming image classification with mobilenet_v2 in pytorch
import torch
import cv2
import numpy as np
import json
from torchvision import transforms
from PIL import Image
model = torch.hub.load('pytorch/vision', 'mobilenet_v2', pretrained=True).cuda()
sudo apt-get update && sudo apt-get upgrade
sudo echo "Well, well, well... Look who fucked the system up again, reinstalling..."
echo "Installing cuda and cudnn"
sudo apt-get install -y system76-cuda-latest system76-cudnn-10.1 cmake
echo "installing python stuff"
sudo apt-get install -y python3-pip python3-pip
sudo pip3 install torch torchvision
sudo apt-get install -y python3-pandas python3-seaborn python3-matplotlib
echo "Installing Emacs and more..."
from matplotlib import cm as cm
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from nltk.tokenize import word_tokenize
import re
from scipy import sparse
def repetitionMatrix(_input, title = "", kind = False, cmap = "Reds"):
from nltk import pos_tag, word_tokenize
def yodinator(text):
text = word_tokenize(text)
tagged = pos_tag(text)
verbs = ("MD", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "RB", "RBR", "RBS")
andcarnivorous /
Last active Jan 5, 2019
Basic linear transformations
from math import cos,sin,radians
import matplotlib.pyplot as plt
import numpy as np
vec = np.array([-5,-8]) #YOUR VECTOR
k = 1 #decide K for shear
# Transformations
def custom_transf (vector, m1,m2,m3,m4):
andcarnivorous /
Created Sep 28, 2018
Memory game in python for terminals
import numpy as np
import random
def table_printer(matrix):
print(" 1 2 3 4")
print("1 ", matrix[0][:])
print("1 ", matrix[1][:])
print("1 ", matrix[2][:])
print("1 ", matrix[3][:])
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