I want to make use of the class_weight parameter of the fit function in Keras to deal with unbalanced datasets.
The key inhere is that i want to specify the class_weights through a function based on the epoch that is running.
I though to use a callback function but I'm unable to change the class_weight parameter from there. Any help?
At this moment it is unfortunately not possible to change the class_weight parameter in a callback function. The parameter is not saved as an instance variable in the model, so you won't be able to access it.
You can however adjust the class weight parameter for each batch, if you use model.train_on_batch. You will be able to calculate the epoch number from the batch number. This only works for this particular fit method, though. So you will need to create a batch generator and modify the class weight parameter before passing on the batch of data to the fit function.
Related
I am wondering would the number of parameters in the models like ResNet18, Vgg16, and DenseNet201 would change if we change the input size to the model?
I did measure the number of parameters with the following command
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
Also, I have tried this snippet, and the number of parameters did not change for different input size
import torchvision.models as models
model= models.resnet18(pretrained = False)
model.cuda()
summary(model, (1,64,64))
No it would not. Parameters of a model have the purpose of processing the input as it propagates inside the network pipeline.
The parameters are mostly trained to serve their purpose, which is defined by the training task. Consider a increase in number of parameters based on the input? What would their values be? Would they be random? How would this new parameters with new values affect the inference of the model?
Such a sudden, random change to the fine-tuned, well-trained parameters of the model would be impractical. Maybe there are some other algorithms that I am unaware of, that change their parameter collection based on input. But the architectures that have been mentioned in question do not support such functionality.
Traninable parameters do not change with the change in input. If you see the weights in first layer of the model with the command list(model.parameters())[0].shape you can realize that it does not depend on the height and width of the input, but it depends on the number of channels(e.g Gray, RGB, HyperSpectral), which usually is very insignificant in bigger models. For further information about getting the input shape, you can see this toy example.
I am attempting to define the parameter of a model (block) as a variable. For example:
Real WallThickness = 0.5;
Real WallConductance = 10*WallThickness;
Modelica.Thermal.HeatTransfer.Components.ThermalConductor TopPanelConductor(G=WallConductance);
I would like to define "G" so that it remains constant throughout the simulation but the coefficient is updated prior to the simulation based on the other variable "WallThickness". When defining the ThermalConductor parameter "G" as a variable in the model, which is being calculated elsewhere, I get the error message:
The variability of the definition equation:
TopPanelConductor.G = WallConductance;
is higher than the declared variability of the variables.
I would like to define the parameters of a model as a variable. This allows me to create parametric definitions as the geometry of the all changes. Is there a way I can make this definition work?
You mean the geometry changes during simulation? If so, you'll have to rewrite the ThermalConductor model to work with a variable G, because a variable cannot be assigned to a parameter. A variable may vary during the course of simulation. A parameter is fixed at the start of simulation, but can be changed from run to run without recompiling the model, which allows for quicker iteration/design work.
Note that you can also calculate a parameter from other parameters that you define, e.g. to calculate a heat transfer coefficient from a given wall thickness (which you vary from simulation run to simulation run).
An alternative to re-writing the component models is to make the parameter study/variation outside the simulation model. There are at least three approaches:
Export your system model as an FMU (Co-simulation). Import it in Python w. PyFmi and write for loops that vary the parameter value for each iteration. See for example http://www.jmodelica.org/assimulo_home/pyfmi_1.0/pyfmi.examples.html. This is not as complicated as it might sound.
Make the parameter variation loop in a Modelica Script (mos file). I don't have much experience with this though.
If you are varying geometrical parameters in order to find an optimum of some kind you can use the Optimization Library which is shipped with Dymola (as of version 2017 FD01).
Using one of the above suggestions you can reuse all the components from MSL out of the box.
Best regards,
Rene Just Nielsen
There is a heirachery for varaibales/parameters that restrict their use. As you are now aware, parameters are not permitted to vary with within the simulations. Thus, you get the error stating that you are trying to define a parameter with a variable value or input variable.
If you need that functionality I would recommend duplicating the ThermalConductor and change the variable type:
parameter Modelica.SIunits.ThermalConductance G
"Constant thermal conductance of material";
to
input Modelica.SIunits.ThermalConductance G
"Constant thermal conductance of material" annotation (Dialog(group=”Input Variables”));
That all there is to it. Note the additional annotation on the input variable. By default inputs do not show up in the parameter GUI. The annotation will permit them to be seen just like parameters (be careful to clearly label it an input variable versus a parameter though!)
There is work underway that has completely redone the Thermal library but is not yet released and the most-straightforward approach would probably try what I have discussed.
I want to tune some parameters of a matlab function block in simulink with Dashboard Blocks like knobs. But when i click on the matlab function with the parameter I want to tune, it says that this block has no tunable parameters. How can I change this?
To my knowledge there is no way to get a tunable parameter in such a block. To get the functionality, I recommend make an input port out of the parameter you want to tune. Then connect it to a constant block and tune the constant instead.
My initial problem is that I have a continuous transfer function which coefficients change with time.
Currently the TF's coefficients are expressed in function of the block mask parameters. These parameters are tunable, and if I change the value in the mask parameters dialog during a simulation the response seems to react appropriately.
However how can I do just that in the code/block flow? Basically, I
have the block parameter 'maskParam' which is set using the mask
parameters dialog, and in the mask initialization commands:
'param=maskParam'. 'param' is used in the transfer function and I
would like to change it in real time (as param=maskParam*f(t)).
I have already looked around and found relevant solutions but either it's unbelievably complicated; or the only transfer function which we are allowed to modify at runtime is discrete and 1) I would like to avoid z-transforming my quite complex TF (I don't have the control toolbox) 2) The sampling time seems to be fixed.. None uses this "dirty" technique of updating parameters, maybe that's the way around?
To illustrate:
I am assuming that you want to change your sim parameters whilst the simulation is running?
A solution is that you run your simulation for inf period and use/change a workspace variable during the simulation period to make the changes take effect.
for Example:
If you look at the w block, you can set it's value in runtime, by doing this:
set_param('my_model_name/w', 'value', 100); % Will change to 100 immediately
You can do similar things with arrays (i.e. a list of coefficients in your case).
HINT FOR YOU
You are using discrete transfer function block. Try the following:
1) Give your block a name e.g. fcn_1
2) In your script, type set_param('your_model_name/fcn_1', 'numerator', '[1 2]'); This will set the numerator value to [1 2]. Do the same for denominator.
3) You should be able to understand, through this exercise, how to handle the property names etc. so that you can change/get them using set_param/get_param.
I leave you to investigate further.
The short answer is that Simulink blocks are not really designed to do this. By definition, a transfer function is Liner-Time Invariant, meaning its characteristics (read coefficients) do not vary with time.
Having said that, there are some workarounds, such as the ones you mentioned in your question. These are the correct way to approach the problem I'm afraid, other than the set_param method suggested by #ha9u63ar. See also this blog on the subject on the MathWorks web site.
I am working on a simulink model, in which i am getting a supply of a a variable at certain time intervals and i need to use the recent 10 values of that variable only.
how should i proceed with that?
It's a bit cheesy, but how about using a chain of Unit Delay blocks?