Machine Learning

Today if you are preprocessing some machine learning data, maybe you need to convert PyTorch tensor to one-hot encoding type. There is a intuitive method that is convert TENSOR to NUMPY-ARRAY, and then convert NUMPY-ARRAY to one-hot encoding type, just like this article: [Python] Convert the value to one-hot type in Numpy

NLLLoss is a loss function commonly used in multi-classes classification tasks. Its meaning is to take log the probability value after softmax and add the probability value of the correct answer to the average.

Today I want to record how to use Deep generative Adversarial Network (DCGAN) to implement a simple generate picture model. I wanted to demo with delicious snack pictures, but the effect was not very good, I downloaded half a million snack pictures in vain.

Finally, I used the official demo CelebA dataset.

Just as torchvision is a module in PyTorch that specializes in processing pictures, torchaudio to be recorded today is a module in PyTorch that specializes in processing audio.

Today we challenged the classifiers of different data sets again. This time, CIFAR-19 is a more difficult problem than MNIST handwriting recognition. In addition to the size of the picture becoming 32×32, CIFAR-10 is no longer a pure grayscale value, but a picture with the three primary colors of RGB.

“Use a toy dataset to train a classification model” is a simplest deep learning practice.

Today I want to record how to use MNIST A HANDWRITTEN DIGIT RECOGNITION dataset to build a simple classifier in PyTorch.

The so-called Neural Network is the model architecture we want to build for deep learning. In official PyTorch document, the first sentence clearly states:

You can use torch.nn to build a neural network.

I believe everyone who is familiar with deep learning knows the importance of automatic derivation. In here, I will make a simple record.

The neural network operation of the deep learning model includes forward propagation and backward propagation.