I am learning the theory and the implementation of variational autoencoder by reading this.
In the documentation, it said optimize the following function: log{p(x|z)} + log{p(z)} - log{q(z|x)}. However, in the code, I am not able to understand why the implementation used cross-entropy to calculate log{p(x|z)}. Can someone please explain to me how cross-entropy is linked to log{p(x|z)}?
Thanks in advance.
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I'm training an EfficientDet V7 model using the V2 model zoo and have the following output in TensorBoard:
This is great, you can see that my classification and localisation losses are dropping to low levels (I'll worry about overfitting later if this is a separate issue) - but regularisation loss is still high and this is keeping my total loss at quite high levels. I can't seem to a) find a clear explanation (for a newbie) on what I'm looking at with the regularisaton loss (what does it represent in this context) and b) suggestions as to why it might be so high.
Usually, regularization loss is something like a L2 loss computed on the weights of your neural net. Minimization of this loss tends to shrink the values of the weights.
It is a regularization (hence the name) technique, which can help with such problems as over-fitting (maybe this article can help if you want to know more).
Bottom line: You don't have to do anything about it.
Is there a Tensorflow or Keras equivalent to fastai's interp.plot_top_losses? If not, how can I manually obtain the predictions with the greatest loss?
Thank you.
I found the answer, it is ktrain! Comes with learning rate finder, learning rate schedules, ready to used per-trained models and many more features inspired by fastai.
https://github.com/amaiya/ktrain
I followed the tutorial of adanet:
https://github.com/tensorflow/adanet/tree/master/adanet/examples/tutorials
and was able to apply adanet to my own binary classification problem.
But how can I predict using the train model? I have a very little knowledge of TensorFlow. Any helps would be really appreciated
You can immediately call estimator.predict on a new unlabeled example and get a prediction.
I'm trying to understand the algorithm of XGboost.
I have several qestions below:
What are the fundamental differences between XGboost and gradient boosting classifier(from scikit-learn)?
I learned that XGboost uses newton's method for optimization for loss function, but I don't understand what will happen in the case that hessian is nonpositive-definite.
I would be so happy if you help me.
Suppose we have LinearRegressor and DNNLinearRegressor models available in the Tensorflow estimator API.
However, the documentation does not clearly mention what the default optimizers, learning rate and activation functions are used by this API.
Please, let me know if you know the answer to this.
Thanks.
If you look at LinearRegressor for you will see the default optimizer is FTRL with yet undefined learning rate. Mean squared error is used as a loss function.
Moreover, DNNRegressor uses Adagrad optimizer with undefined learning rate and ReLU activations.
As you can see, everything is there. Hope that helps.