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EnisBerk / final_exam.ipynb
Created May 20, 2020
Final Exam Questions
View final_exam.ipynb
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View copy-of-dcgan.ipynb
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View sample.py
import random
print(random.rand(1))
View sample.py
import random
print(random.rand(1))
@EnisBerk
EnisBerk / colab_ssh.py
Last active Dec 31, 2019
open ssh connection to colab notebook
View colab_ssh.py
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
View alaska_results_v0.1.rtf
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
View results.txt
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
View tensor2tesnor.ipynb
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View model.py
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.)
View tf_to_pytorch_vggish_model.py
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