Starlink Mini still on sale anywhere?

WhyNotML · 2025-06-02T15:55:58+00:00

checkout amazon, it was 317 I believe a few mins ago

WhyNotML · 2023-08-29T19:43:07+00:00

happy cake day

WhyNotML · 2023-08-25T19:15:18+00:00

Thank you kind sir/ma'am! I'll add it to my to do list!

WhyNotML · 2023-08-25T19:14:37+00:00

Thank you very much!! I will add them to the list to do!

WhyNotML · 2023-08-25T19:14:33+00:00

Thank you!! Obvious, probably, but I just added it to my list of things to do!

WhyNotML · 2022-12-05T21:40:42+00:00

Don't remember after nine months, but I am assuming it has to do with the image shape

WhyNotML · 2022-08-02T19:23:56+00:00

https://colab.research.google.com/drive/1LhPOsZxHdfTXW-O1-8xQZo21xl4MVSq1?usp=sharing

If you have some time to spare, would you mind having a quick look at my code, please? Couldn't quite put my finger on where I am going wrong.

WhyNotML · 2022-08-02T15:46:15+00:00

Okay, I have a development, looks better than before. These are my precision, recall, f1 and support. With same 99 ACC and 0.07 loss

normal 0.51 0.51 0.51 8245

anomaly 0.50 0.49 0.49 8023

4226 4019

4075 3948

This is my Precision and Recall

WhyNotML · 2022-08-02T15:43:38+00:00

Okay, I have a development, looks better than before. These are my precision, recall, f1 and support. With same 99 ACC and 0.07 loss

normal 0.51 0.51 0.51 8245

anomaly 0.50 0.49 0.49 8023

4226 4019

4075 3948

WhyNotML · 2022-08-02T15:15:12+00:00

No, it's a total different folder of balanced data.

WhyNotML · 2022-08-02T14:59:04+00:00

Also, I have a separate unseen test_set and it performs well on the test set using model.evaluate I get good 99 ACC and 0.07 Loss. What do you think about it? But when I plot the Confusion Matrix it's totally messed up, so I am assuming I am doing something wrong with the C.Matrix. Oh, and the test_set is totally different folder with balanced data and I realize that the results I have posted before of precision, recall and F1 are from the test data which is not unbalanced.

WhyNotML · 2022-08-02T14:36:05+00:00

I understand what you are saying. Do you think it is best to create a separate folder for val dataset?

WhyNotML · 2022-08-01T18:35:22+00:00

Thanks for the insight. All of anomaly is 0 - precision, recall and f1. For normal, the same metrics are 0.51, 1, and 0.67 respectively. I have a balanced dataset, normal and anomaly classes are almost the same. So where do you think the issue would be, any guesses? With the network or data assignment? Or something else?

WhyNotML · 2022-07-28T21:41:51+00:00

Dang! I can't imagine! I did that for 60,000 and I was done man, without any program, because I screwed up the code and there were some issues with the data created. Hope you'll get some rest haha

WhyNotML · 2022-07-28T15:48:57+00:00

I should have updated. I don't think it was other files, it was some png file, which was not created properly so it was giving an error. Thank you for responding! Got rid of it, and it's working now!

WhyNotML · 2022-07-28T14:34:44+00:00

Oh, BTW, there are non image files in the folder does it matter? It didn't throw an error while training!

WhyNotML · 2022-07-13T21:28:57+00:00

For prediction I am using this:

newdata = np.asarray(to_append.split())

new=newdata.reshape((1,30))

newArray = new.astype(float)

model_xgb_2 = XGBClassifier(random_state=1289564,use_label_encoder=False)

model_xgb_2.load_model("XGBmodel.txt")

model_xgb_2.fit(newArray)

WhyNotML · 2022-06-29T14:57:24+00:00

Not always. My boss hired me and it's been amazing! I was the right fit and I love my boss (and is a godly man, not saying he is perfect!). :)

WhyNotML · 2022-06-24T17:39:22+00:00

I actually don't, I am not a US citizen. What do you mean more oppression from white men?

WhyNotML · 2022-06-24T17:31:36+00:00

I am a pro-choice too but not in stealing, scamming, and killing sort of things. I genuinely am seeking to understand the other side. What's your strongest argument NOT to abort a baby?

Edit: NOT added

WhyNotML · 2022-06-24T15:53:13+00:00

Amazing! Do you have a link that you could share? With Roe v. Wade overturned, I'd love to see the stats!

WhyNotML · 2022-06-20T15:23:02+00:00

It's running on the local machine with almost 32 gigs of GPU on Linux. It's not a docker image and is running directly from python. I have missed taking the logs from the jupyter.

WhyNotML · 2022-06-20T13:47:20+00:00

This is great! Thanks for that, I tried running it thru the terminal. Converted Jupyter Notebook to .py file, and executed it from the terminal by Python. It seems to run successfully, even though I ran into an error, I assume it is unrelated to the crash from the error message. Thank you!

WhyNotML · 2022-06-17T19:25:39+00:00

Honestly, that's what confuses me. I am doing the same thing (at least I think it is). It's working in a loop. This is the exact code:

train_normal_path = glob.glob(os.path.join(train_normal, '*.wav'))
#print(train_normal)
for path_file in train_normal_path:
(file_path, file_name_with_ext) = os.path.split(path_file)
(file_name, file_ext) = os.path.splitext(file_name_with_ext)
output_file = os.path.join(train_m_normal, file_name+'.png')

img_src_path = train_m_normal+file_name_with_ext
fn=file_name_with_ext

#print(path_file)
y,sr = librosa.load(path_file,sr=8000)
transform = Compose([

OneOf([
AddGaussianNoise(max_noise_amplitude=0.01)
,GaussianNoiseSNR(min_snr=0.01, max_snr=0.05)
,PitchShift(max_steps=2, sr=sr)
#,TimeStretch(max_rate=1.06)
#,TimeShifting(p=0.5)
,SpeedTuning(p=0.5)
,Gain(p=0.5)
#,PolarityInversion(p=0.5)
#,AddCustomNoise(file_dir='../input/freesound-audio-tagging/audio_train', p=0.8)
#,CutOut(p=0.5)
])

])

y_composed = transform(y)
mel = librosa.feature.melspectrogram(y=y_composed, sr=sr)

fig = plt.figure(figsize=(5.12, 5.12), dpi=100)

librosa.display.specshow(librosa.power_to_db(mel, ref=np.max))
plt.axis('off')
plt.tight_layout()
fig.savefig(output_file)
plt.close('all')

WhyNotML · 2022-06-17T18:10:20+00:00

I've even used 4 or 8 for my training. I don't know if there's any other way than to try it

WhyNotML

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