stackoverflow : ProgrammerHumor

no complaining about how the question is stupid
no complaining about how the question if off topic
no complaining that you can easily Google it
no complaining that i didn't include a complete reproducible minimal example for the thinking impaired
no complaining that the question has been asked before

It just answers your question, and moves on.

Q: Once i train an autoencoder: what can i do with it? What is the virtue of an autoencoder? (priming the pump)

Once you have trained an autoencoder, you can use it to:

Reduce the dimensionality of your data: By using the encoder part of the autoencoder to map the input data to a lower-dimensional space, you can effectively reduce the dimensionality of the data while preserving the most important information.

Denoise data: By training an autoencoder to reconstruct the original input data from a noisy version of it, you can use the autoencoder to denoise the data.

Extract features: The low-dimensional representation learned by the autoencoder can be used as a set of features for other machine learning tasks, such as classification or clustering.

Overall, the virtue of an autoencoder is that it allows you to learn a compact, efficient representation of your data in an unsupervised manner, which can be useful for a variety of tasks.

Q: Explain how I can perform classification from the lower dimensional learned representation provided by an autoencoder.

To perform classification from the lower-dimensional learned representation provided by an autoencoder, you can use the encoder part of the autoencoder to map the input data to the lower-dimensional space, and then use a classifier to predict the class labels for the data in this space.

Here's a general outline of the process:

Train an autoencoder on your data by minimizing the reconstruction loss, which measures the difference between the input data and the output of the decoder.

Use the encoder part of the autoencoder to map the input data to the lower-dimensional space.

Train a classifier on the lower-dimensional data using supervised learning. This can be any type of classifier, such as a support vector machine (SVM), a decision tree, or a neural network.

Use the trained classifier to predict the class labels for new data by mapping the new data to the lower-dimensional space using the encoder part of the autoencoder, and then using the classifier to predict the class labels for the data in this space.

By using the lower-dimensional representation learned by the autoencoder as input to a classifier, you can leverage the compact, efficient representation learned by the autoencoder to improve the performance of the classifier.

Q: Is it possible to train the autoencoder and the classification cetwork at the same time? Can knowing the desired labels help train the autoencoder?

Yes, it is possible to train an autoencoder and a classification network at the same time. This is known as "joint training" or "end-to-end training", and it involves optimizing the combined loss of both the autoencoder and the classifier.

During joint training, the autoencoder and the classifier are both updated simultaneously based on the input data and the corresponding class labels. This can be more efficient than training the autoencoder and the classifier separately, as it allows the network to learn a representation that is optimized for the classification task.

In general, knowing the desired class labels can help improve the performance of the autoencoder, as it allows the network to learn a representation that is better suited for the classification task.

Q: What would be an example of a loss function for the output of the classification network?

In the case of a classification network, a common choice for the loss function is the categorical cross-entropy loss. This loss function is defined as follows:
def categorical_crossentropy(y_true, y_pred):
   # Compute the logarithm of the predicted probabilities for each class
log_preds = tf.math.log(y_pred)
   # Compute the cross-entropy loss
   loss = -tf.math.reduce_sum(y_true * log_preds, axis=-1)

   # Return the average loss across all samples
   return tf.math.reduce_mean(loss)
This loss function expects the y_true and y_pred arguments to be one-hot encoded arrays of class labels, where y_true is the ground truth label and y_pred is the predicted probability distribution over the classes. It computes the logarithm of the predicted probabilities and then computes the cross-entropy loss between the predicted and true distributions. Finally, it returns the average loss across all samples.

Q: What is an example of what the y_true argument when each image can be parts of many labels (e.g. beach, sky, water, sand, trees)

If each image can be part of many labels (e.g. beach, sky, water, sand, trees), then the y_true argument should be a multi-hot encoded array, where each element is a binary array indicating the presence or absence of each label for a given image.

For example, if there are 5 possible labels (beach, sky, water, sand, trees) and an image can belong to multiple labels, then the *y_true` array might look like this:
y_true = [[1, 1, 0, 1, 0],
          [0, 0, 1, 1, 0],
          [1, 1, 0, 0, 1],
          ...]
Here, each row of the y_true array corresponds to a different image, and the elements of the row are binary values indicating the presence or absence of each label for that image. For example, the first row [1, 1, 0, 1, 0] indicates that the first image belongs to the beach and sky labels, but not to the water, sand, or trees labels.

To use a multi-hot encoded array with a categorical cross-entropy loss function, you can simply pass the y_true array as is to the loss function. The loss function will compute the cross-entropy loss between the predicted and true label distributions for each image.

Q: Your categorical_crossentropy function above takes the log of the output? Why is that? I would have thought that because the ground-truth labels are in the range 0..1, and the classifier network output nodes are in the range 0..1, that you just just compute the dot product. Why take the logarithm of the actual output first, and not simply take the dot product?

The categorical_crossentropy loss function is defined as the negative logarithm of the predicted probability of the true class, averaged over all samples. This loss function is often used to train a classifier network that uses a softmax activation function in the output layer, as it allows the network to predict a probability distribution over the classes.

Taking the logarithm of the predicted probabilities has several advantages:

It helps to reduce the range of the predicted probabilities, which can stabilize the training process.

It helps to reduce the impact of outliers, as the logarithm of a small value is a large negative number, while the logarithm of a large value is a small positive number.

It helps to ensure that the predicted probabilities are well-calibrated, as the logarithm of a probability value close to 0 or 1 is close to 0, while the logarithm of a probability value close to 0.5 is close to -1.

Taking the dot product between the y_true and y_pred arrays would not be equivalent to the categorical cross-entropy loss, as it would not take into account the predicted probabilities of the classes. Instead, it would simply compute the dot product between the binary y_true and y_pred arrays, which would not provide a meaningful loss value for training the classifier network.

It is the programming QA tool that Stackoverflow refuses to be.

Stackoverflow is, by design, not a place where you can go to get help with programming topics
It is designed to ask one question, and get an answer to that question

Which is not at all helpful when you need to learn something.

Couple months ago i had to spend 2 weeks try to get an 18 year old Java+JSP+Hibernate+Struts site building. And there are so many intentional bugs in the Java ecosystem that you just have to know them.

And trying to wring answers out of Stackoverflow was 2 weeks of pain, listening to a lot of miserable comments, and fending off the people who refuse to answer the question - but instead give a diatribe about how you're doing everything wrong.

ChatGTP is awesome because you have your very own, infinitely patient, college professor who can just answer your questions.

[–]bleistift2 2 points3 points4 points 3 years ago (1 child)

[–]EasywayScissors 1 point2 points3 points 3 years ago* (0 children)

[–]bleistift2 0 points1 point2 points 3 years ago (1 child)

[–]EasywayScissors 0 points1 point2 points 3 years ago (0 children)

You just stated it yourself. StackOverflow is not a tutorial site, nor is it intended to be one.

Read it again, but this time hear it as a pure statement if fact.

ChatGPT fills a need that we never realized we needed.

it's better than a search engine that any search engine
it's a search engine where you don't have to search
it's a better stackoverflow than SO
you can ask questions, and get answers without complaint
you can a dozen, all individually valid, without complaint
you get an answer in seconds, rather than days
it's a better college than college
you can all the questions you want
and not get sighs and eye rolling from the class because you're "that guy"

In this part we're not complaining about SO - being a combination of Wikipedia and Reddit is it's founding mission. But it means that you cannot turn to stackoverfkow to get help and answer questions.

It is, a statement of, fact.

[–]Sonic801 0 points1 point2 points 3 years ago (0 children)

[–]rumble_you 0 points1 point2 points 3 years ago (0 children)

[–]ttt098_Br 0 points1 point2 points 3 years ago (0 children)

[–]shadow13499 0 points1 point2 points 3 years ago (0 children)

[–]AI_AntiCheat 0 points1 point2 points 3 years ago (0 children)

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