dataloader | generic utility for batch data loading | Batch Processing library

I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)

Following my previous question , I have written this code to train an autoencoder and then extract the features. (There might be some changes in the variable names)

I am running the following code against the dataset of PV_Elec_Gas3.csv, the network architecture is designed as follows

I have a folder of images as such

So I have this dataloader that loads data from hdf5 but exits unexpectedly when I am using num_workers>0 (it works ok when 0). More strangely, it works okay with more workers on google colab, but not on my computer. On my computer I have the following error:

Traceback (most recent call last): File "C:\Users\Flavio Maia\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 986, in _try_get_data data = self._data_queue.get(timeout=timeout) File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\multiprocessing\queues.py", line 105, in get raise Empty _queue.Empty

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "", line 2, in File "C:\Users\Flavio Maia\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 517, in next data = self._next_data() File "C:\Users\Flavio Maia\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 1182, in _next_data idx, data = self._get_data() File "C:\Users\Flavio Maia\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 1148, in _get_data success, data = self._try_get_data() File "C:\Users\Flavio Maia\AppData\Roaming\Python\Python37\site-packages\torch\utils\data\dataloader.py", line 999, in _try_get_data raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e RuntimeError: DataLoader worker (pid(s) 12332) exited unexpectedly

Also, my getitem function is:

I am currently working on building a CNN for sound classification. The problem is relatively simple: I need my model to detect whether there is human speech on an audio record. I made a train / test set containing records of 3 seconds on which there is human speech (speech) or not (no_speech). From these 3 seconds fragments I get a mel-spectrogram of dimension 128 x 128 that is used to feed the model.

Since it is a simple binary problem I thought the a CNN would easily detect human speech but I may have been too cocky. However, it seems that after 1 or 2 epoch the model doesn’t learn anymore, i.e. the loss doesn’t decrease as if the weights do not update and the number of correct prediction stays roughly the same. I tried to play with the hyperparameters but the problem is still the same. I tried a learning rate of 0.1, 0.01 … until 1e-7. I also tried to use a more complex model but the same occur.

Then I thought it could be due to the script itself but I cannot find anything wrong: the loss is computed, the gradients are then computed with backward() and the weights should be updated. I would be glad you could have a quick look at the script and let me know what could go wrong! If you have other ideas of why this problem may occur I would also be glad to receive some advice on how to best train my CNN.

I based the script on the LunaTrainingApp from “Deep learning in PyTorch” by Stevens as I found the script to be elegant. Of course I modified it to match my problem, I added a way to compute the precision and recall and some other custom metrics such as the % of correct predictions.

Here is the script:

I'm working with two tensors, inputs and labels, and I want to have them together to train a model. I'm using torch 1.7, but I can't use the function TensorDataset() and then apply DataLoader(), due to some incompatibilities with other packages when I use TensorDataset(). There is another solution to my problem?

Summary:
2 Tensors --> DataLoader without using TensorDataset()

I am trying to implement a simple GAN in google collaboratory, After using transforms to normalize the images, I want to view it at the output end to display fake image generated by the generator and real image side by in the dataset once every batch iteration like a video.