spsa | Simultaneous perturbation stochastic approximation Python | Data Manipulation library

I have build a model using the low-level tensorflow API that has only a couple of variables (about 10) that I want to optimize. Therefore I want to implement a custom loss function that produces noisy loss values (kind of like a simulation based optimization approach).

Usually I would a derivative free optimizer like the SPSA optimizer.

Is there a way to implement a loss function that is not differentiable and an optimizer like SPSA with the low-level tensorflow API?

PS: One might ask why use tensorflow for this? This is because I want to use tensorflow for convenient saving of the model and tensorboard for convenient and comprehensive visualization. Also I want to be able to switch and compare different models in a standardized framework.

i have a serious (and weird) problem when uploading files to a FTPS server.

I have to upload JSON files and some of them are really small (under 5kB). There is no problem with these files, I can upload them successfully.

But when uploading "larger" files (not really large, over 30kb) it shows this error:

I appreciate if you can answer my questions or provide me with useful resources.

Currently, I am working on a problem that I need to do alternating optimization. So, consider we have two decision variables x and y. In the first step I take the derivative of loss function wrt. x (for fixed y) and update x. On the second step, I need to take the derivative wrt. y. The issue is x is dependent on y implicitly and finding the closed form of cost function in a way to show the dependency of x on y is not feasible, so the gradients of cost function wrt. y are unknown.

1) My first question is whether "autodiff" method in reverse mode used in TensorFlow works for these problems where we do not have an explicit form of cost function wrt to one variable and we need the derivatives? Actually, the value of cost function is known but the dependency on decision variable is unknown via math.

2) From a general view, if I define a node as a "tf.Variable" and have an arbitrary intractable function(intractable via computation by hand) of that variable that evolves through code execution, is it possible to calculate the gradients via "tf.gradients"? If yes, how can I make sure that it is implemented correctly? Can I check it using TensorBoard?

My model is too complicated but a simplified form can be considered in this way: suppose the loss function for my model is L(x). I can code L(x) as a function of "x" during the construction phase in tensorflow. However, I have also another variable "k" that is initialized to zero. The dependency of L(x) on "k" shapes as the code runs so my loss function is L(x,k), actually. And more importantly, "x" is a function of "k" implicitly. (all the optimization is done using GradientDescent). The problem is I do not have L(x,k) as a closed form function but I have the value of L(x,k) at each step. I can use "numerical" methods like FDSA/SPSA but they are not exact. I just need to make sure as you said there is a path between "k" and L(x,k)but I do not know how!

I'm a novice Android developer experiencing some difficulties in cross-version support: I'm developing an app (the name is RECIPE) with minimum SDK version requirement of 21 (from Lollipop and on).

For now the app has just few features: it switches activities via intents, open the camera by sending an intent, allow the user to make a single photo with the camera module and then stores the photo and returns a preview back to the main app.

The problem occurs when the camera intent is called: in this case the app runs smooth if I run it on an emulator with a device with API 23 (Marshmallow; for 21 and 22 Lollipop's API now the app doesn't work because I have to do some permission managing); but unfortunately, the app crashes if I run it on a device with API 24 or 25 (Nougat).

If you want to reproduce the problem, after having installed the app (on a physical or emulated device with API 24 or 25) open it, then click on "GO TO SINGLE PHOTO SHOOTING MODE" and then on "TAKE PHOTO" to start the camera intent. Normally you will also be prompted of allowing writing permission to store the photo file.

I think that the bug comes from the writing permissions or from something regarding the camera intent.

Down here there is the code

MainMenu.java