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EnisBerk / index.html
Created February 19, 2014 07:57
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>D3: Setting path fills</title>
<script src="http://d3js.org/d3.v3.min.js"></script>
<script src="http://d3js.org/topojson.v1.min.js"></script>
<style type="text/css">
/* No style rules here yet */
</style>
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EnisBerk / saxpy.cu
Created November 7, 2016 11:34
#include<time.h>
#include <stdio.h>
cudaEvent_t start, stop;
float time2;
float total_time=0;
__global__
void saxpy(int n, float a, float *x, float *y)
{
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EnisBerk / run.py
Created October 31, 2018 17:00
"""run.py:"""
#!/usr/bin/env python
import os
import torch
import torch.distributed as dist
from torch.multiprocessing import Process
def run(rank, size):
# tensor = torch.zeros(1)
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EnisBerk / test.py
Created November 29, 2018 01:09
Testing shared file-system initialization. I used container: paperspace/fastai:1.0-CUDA9.2-base
import os
import torch
import torch.distributed as dist
from torch.multiprocessing import Process
import argparse
import socket
import time
import requests
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EnisBerk / tf_to_pytorch_vggish_model.py
Last active February 25, 2019 01:35
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
__weights_dict = dict()
def load_weights(weight_file):
if weight_file == None:
return
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EnisBerk / model.py
Last active March 16, 2019 00:56
Audio dataset classifier model from https://github.com/qiuqiangkong/audioset_classification/
import torch
import torch.nn as nn
import math
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
def init_bn(bn):
bn.weight.data.fill_(1.)
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EnisBerk / tensor2tesnor.ipynb
Created August 17, 2019 01:58
tensor2tesnor tests
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EnisBerk / results.txt
Created October 17, 2019 02:58
Average of 10 second, raw embeddings:
Tag N. Neighbors Linear SVM RBF SVM Gaussian P. Decision T. Random F. NN AdaBoost Naive B. QDA
------------- -------------- ------------ --------- ------------- ------------- ----------- ---- ---------- ---------- ------
Songbird 0.7 0.62 0.72 **0.74** 0.65 0.63 0.73 0.73 0.53 0.66
Water Bird 0.88 0.88 0.88 0.89 0.8 0.88**0.89** 0.87 0.4 0.88
Insect 0.81 0.84 0.89 0.87 0.8 0.85 0.87 0.88 0.44 0.84
Running Water 0.94 0.92 0.91 0.93 0.92 0.92 0.93 0.88 0.56 0.91
Rain 0.97 0.98 0.98 0.98 0.97 0.98 0.98 0.97 0.71 0.98
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EnisBerk / alaska_results_v0.1.rtf
Last active October 17, 2019 18:21
Raw embeddings and merged by Average
Tag Songbird Water Bird Insect Running Water Rain Cable Wind Vehicle Aircraft
------------ ---------- ------------ -------- --------------- ------ ------- ------ --------- ----------
N. Neighbors 0.72 0.53 0.53 0.71 0.82 0.8 0.91 -1 0.5
Linear SVM 0.89 0.75 0.74 0.91 0.82 0.84 0.87 -1 0.059
RBF SVM 0.83 0.67 0.48 0.74 0.81 0.84 0.9 -1 0.87
Gaussian P. 0.87 0.65 0.72 0.94 0.8 0.91 0.91 -1 0.77
Decision T. 0.7 0.57 0.58 0.85 0.64 0.8 0.76 -1 0.5
Random F. 0.74 0.62 0.67 0.68 0.86 0.75 0.86 -1 0.34
NN 0.88 0.72 0.7 0.94 0.82 0.9
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EnisBerk / colab_ssh.py
Last active December 31, 2019 19:37
open ssh connection to colab notebook
import random, string, urllib.request, json, getpass
#Generate root password
password = ''.join(random.choice(string.ascii_letters + string.digits) for i in range(20))
#Download ngrok
! wget -q -c -nc https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
! unzip -qq -n ngrok-stable-linux-amd64.zip
#Setup sshd