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)) |
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 |
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