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I am trying to add tf.data pipeline to a regression task. The code starts with reading in the continues values using the csv file and inputting the images with cv2.imread. I split the data to train, test and validation using sklearn preprocessing.

I have modified VGG16 in pytorch to insert things like BN and dropout within the feature extractor. By chance I now noticed something strange when I changed the definition of the forward method from:

I have a problem with React and Typescript and it will be nice if I get some help from you guys!

I'm trying to assign an onclick event to my child box component but it isn't working, it doesn't trigger any error, just plainly doesn't work.

This his is the parent:

I need to draw a line between point in my analysis.

I have plotted a 2D histogram, and need to plot some points overlaying this histogram and draw a line between them. I already tried plt.plot() but neither the points nor lines appear in the plot. If I use plt.scatter() now the points appear, but I still need to connect the points with a line.

My plot is below:

Any tips in how can I connect those red dots? (I forgot to say it, but i just want to plot some points, in this case 200, not all of them).And the code i used is:

Can anyone tell me what is wrong with this code? It is from https://jakevdp.github.io/blog/2012/09/05/quantum-python/ . Everything in it worked out except the title of the plot.I can't figure it out.

It should look like this

but when the code is run, it polts this

Here is the code given:-

I am trying to learn a custom environment using the TFAgents package. I am following the Hands-on-ML book (Code in colab see cell 129). My aim is to use DQN agent on a custom-written grid world environment.

Grid-World environment:

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 using sigmoid and binary_crossentropy for multi-label classification. A very similar question asked here. And the following custom metric was suggested: