semi-supervised-paper

// Copyright (c) 2014, Jan Winkler <winkler@cs.uni-bremen.de>
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
//     * Redistributions of source code must retain the above copyright
//       notice, this list of conditions and the following disclaimer.
//     * Redistributions in binary form must reproduce the above copyright
//       notice, this list of conditions and the following disclaimer in the

semi-supervised-paper

import numpy as np 
from matplotlib import pyplot as plt

center_1 = np.array([1,1])
center_2 = np.array([5,5])
center_3 = np.array([8,1])

data_1 = np.random.randn(200, 2) + center_1
data_2 = np.random.randn(200,2) + center_2
data_3 = np.random.randn(200,2) + center_3

semi-supervised-paper

import site
import os

os.system('/usr/local/cuda/bin/nvcc --version')
print('site.getsitepackages() :', site.getsitepackages())
os.system('cd faiss-gpu-1.4.0-py36_cuda9.0.176_1/ && cp -r lib/python3.6/site-packages/* {} && pip install mkl && pip install --upgrade scikit-learn'.format(site.getsitepackages()[0]))

import mkl
mkl.get_max_threads()
import faiss

semi-supervised-paper

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

semi-supervised-paper

#!/bin/bash

# download and unzip dataset
#wget http://cs231n.stanford.edu/tiny-imagenet-200.zip
unzip tiny-imagenet-200.zip

current="$(pwd)/tiny-imagenet-200"

# training data
cd $current/train

semi-supervised-paper

coarse_label_names
0 aquatic_mammals
1 fish
2 flowers
3 food_containers
4 fruit_and_vegetables
5 household_electrical_devices
6 household_furniture
7 insects
8 large_carnivores

semi-supervised-paper

    from tensorboardX import SummaryWriter
    import torch
    with SummaryWriter(log_dir='./test', comment='test') as writer:
        writer.add_embedding(
            torch.autograd.Variable(torch.FloatTensor(np_embeddings)),
            metadata=np_labels.tolist(),
            global_step=0)

semi-supervised-paper

os.path.abspath(os.path.join(path, ".."))

semi-supervised-paper

func(){
echo $1 $2
}

for i in 0.01 0.03 0.05
do
func $i $(bc <<< "$i * 120.0") &
done
wait

semi-supervised-paper

import theano.tensor as T
import numpy as np
import theano
import os

x = T.fmatrix('x')
y = T.ivector('y')

true_dist = T.zeros_like(x)
true_dist = T.fill(true_dist, 0.1 / x.shape[1])