I know how to extract Caffe feats / scores using the matcaffe_demo.m that is provided along with Caffe. However when using this file one has to provide a prototxt file that determines not only the network architecture but also the input dimensions including the batch_size.
Since I'm processing the frames of videos of variable sequence lenght I need a way to use matcaffe_demo.m along with a variable batch size.
Does anyone know how to do that?
It would probably involve changing this line from matcaffe_demo.m
% Initialize a network
net = caffe.Net(net_model, net_weights, phase);
to something that dynamically passes the current batch size needed dynamically to caffe
I ended up using the reshape function:
net = caffe.Net(net_model, net_weights, phase);
net.blobs('data').reshape([dim1 dim2 numChannels numFrames]);
scores = net.forward(inputData);
caffe.reset_all();
Related
I have a simulink file and I'm working on designing a controller.
The file contains several electronics converters connected in parallel and each converter has a controller. The converter measures voltage and current and pass them to the controller to control.
The simulink file generates the simout file after simulation and that simout file is being used to calculate the impedance of the electrical grid in a m script file as a transfer function. I need to use the transfer function of impedance from the workspace in the place of " Req +sLeq" as in the purple marked position of the picture.
In other words, in my controller design, I need to call that m-file transfer function into a block and use the transfer function (impedance) as active damping during the simulation.
I'm not sure how can I call the transfer function from the m file (generated from simout) back to the simulink block during simulation.
The simulation need to be done in one go as closed loop system.
Please help me for this.
I can think of two ways to solve this. First, you can try to use a Matlab function block:
https://www.mathworks.com/help/simulink/slref/matlabfunction.html
Otherwise, you can always split your routines. Split your simulations, obtain your transfer function -which now exists in the workspace- then run your control system simulation using a separate file.
Dummy main-file
simOut = sim('electricalSimulation'); % Your first simulation
output = simOut.output;
input = simOut.input;
electricalTf = obtainTF(output, input); % Your identification routine
sim('completeSystem');
I have this AlexNet model in MATLAB:
net = alexnet;
layers = net.Layers;
layers(end-2) = fullyConnectedLayer(numClasses);
layers(end) = classificationLayer;
I'm using it to learn features from sequencies of frames from videos of different classes. So i need to extract learned features from the 'fc7' layer of this model to save these features as a vector and pass it to an LSTM layer.
The training process of this model for transfer learning its ok, all right.
I divided my data set in a x_train and a x_test sets using splitEachLabel() in my imageDatastore(), and using the function augmentedImageSource() to resize all the images for the network. Everything ok!
But when i try yo use this snippet of code shown bellow to resize images from my imageDatastore to be readed by the function activations(), to save the features as a vector, i'm getting an error:
imageSize = [227 227 3];
auimds = augmentedImageSource(imageSize, imds, 'ColorPreprocessing', 'gray2rgb');
Function activations:
layer = 'fc7';
fclayer = activations(mynet, auimds, layer,'OutputAs','columns');
The error:
Error using SeriesNetwork>iDataDispatcher (line 1113)
For an image input layer, the input data for predict must be a single image, a 4D array of images, or an imageDatastore with the correct size.
Error in SeriesNetwork/activations (line 791)
dispatcher = iDataDispatcher( X, miniBatchSize, precision, ...
Someone help me, please!
Thanks for the support!
Did you check the input size of that layer? The error you are getting is related with the input size of the current layer. Can you check your mynet structure and its fc7 layer input's size in your workspace in Matlab?
I'm a super beginner in Simulink models and control systems.
I have .slx Simulink model for drone dynamics system.
It takes in two inputs (roll cmd, pitch cmd) and outputs velocity x, velocity y, position x, and position y.
From here, it seems like I can open the system by calling
open_system('myModel.slx', 'loadable');
But how do I put inputs and get output values?
Is there a way I can do this in a gui?
EDIT:
Here is the full layout of my model:
When I did
roll_CMD=10;
pitch_CMD=20;
I got a warning saying:
Input port 1 of 'SimpleDroneDynamics/...' is not connected.
How do I feed inputs using port numbers?
How do I get outputs with port numbers? I tried
[vx, vy, px, py] = sim('SimpleDroneDynamics.slx');
and got an error saying
Number of left-hand side argument doesn't match block diagram...
Is there a way to continuously feed inputs at every time step? This being controller module, I think I'm supposed to feed in different values based on output position and velocity.
EDIT2:
I'm using Matlab2017a
About the first two points of your question:
In simulink:
For the inputs you can use a constant block and when you double click the input block you can assign a value, which can be a workspace variable.
To get the outputs to your workspace you can use the simout block (make sure to put Save format to array).
Connect inputs to your simulink model
Connect outputs of your simulink model to the simout blocks.
MATLAB script
clc;
clear all;
roll = 10;
pitch = 20;
sim('/path_to_simulinkmodel.slx')
time = simout(:,1);
velocity_X = simout(:,2);
velocity_Y = simout(:,3);
position_X = simout(:,4);
position_Y = simout(:,5);
About the third point of your question
You can define the duration of your simulation in the block diagram editor. You can put a variable which is defined in the calling script. There are multiple ways of achieving time dependent input variables:
One option I personally don't recommend is using a for-loop and calling the simulink model with a different value of roll and pitch
for i = 1:numberOfTimesteps
roll = ...
...
sim('simulinkModel.slx')
end
A second and more efficient approach is changing the constant blocks to other source blocks like ramp signals or sinusoid signals
First of all Simulink model use main Matlab workspace. So you can change your variables values at command window (or just at your script) and run Simulink model.
There are several ways to initialize this constants for Simulink. One more useful way is to create script containing all your variables and load it at Simulink model starts. You can do it by adding script name in Simulink/Model Explorer/Callbacks. (There are different callbacks - on Loading, on Starting and etc.). Read more about this: here.
Now you can run your simulation using sim function:
sim('name_of_model')
name_of_model must contain path if model is not in the active MATLAB folder (active folder you can see in your matlab window just under the main menu).
There are different properties of sim function, read about them in help this can be useful for you. By the way: you can change some parameters of your model using sim. You even can find any block in your model and change it's properties. Read more about sim and about finding current blocks. Interesting that the last solution give you ability to change parameters during the simulation!
About getting output. After you run simulation you get tout variable in main workspace. It is an array of timesteps. But if you add outport block (like at my image) you also get another variable in workspace yout. yout is an Datasets. It contain all your outports values. For 2 outports for example:
yout
yout =
Simulink.SimulationData.Dataset
Package: Simulink.SimulationData
Characteristics:
Name: 'yout'
Total Elements: 2
Elements:
1 : ''
2 : ''
Get the values of any of outports:
yout.get(1).Values
it is a timeseries data type, so:
yout.get(1).Values.Time - give you times
yout.get(2).Values.Data - give you values of this outport at each time
We have one more method to take output values:
[t,x,y] = sim('model_name')
it returns double arrays. t- time array, y - matrix of all outports values (it already double and contain only values without times, but for each simulation time!)
So now you can create common Matlab GUI and work at this variables! There is no any difficulties. You can read more about GUI for Simulink here.
I created a MATLAB function that runs bootstrap regression based on your data and the sample size desired. The only inputs required are the Y data, X data and 'n' the bootstrap sample size required, e.g. boot(Y,X,10).
How can I create an input that will loop for numerous sample sizes? I.e. something like boot(Y,X,[10,30,100]).
This is important as bootstrap can take quite a while to run, so it is ideal if you can just enter the desired sample sizes and leave the computer while running instead of entering the same command three times.
This can be done easily as follows:
bootstrapSampleSize = [10 30 100];
for i=1:length(bootstrapSampleSize)
yourResult{i}=boot(Y,X,bootstrapSampleSize(i)); %stores the results in a cell array
end
This can be made completely interactive with the use of input command but I will leave that upto you.
I am trying to load feature vectors into classifiers such as a k-nearest neighbors classifier.
I have my code for GLCM, so I get contrast, correlation, energy, homogeneity in numbers (feature vectors).
My question is, how can I save every set of feature vectors from all the training images? I have seen somewhere that people had a .set file to load into classifiers (may be it is a special case for the particular classifier toolbox).
load 'mydata.set';
for example.
I suppose it does not have to be a .set file.
I'd just need a way to store all the feature vectors from all the training images in a separate file that can be loaded.
I've google,
and I found this that may be useful
but I am not entirely sure.
Thanks for your time and help in advance.
Regards.
If you arrange your feature vectors as the columns of an array called X, then just issue the command
save('some_description.mat','X');
Alternatively, if you want the save file to be readable, say in ASCII, then just use this instead:
save('some_description.txt', 'X', '-ASCII');
Later, when you want to re-use the data, just say
var = {'X'}; % <-- You can modify this if you want to load multiple variables.
load('some_description.mat', var{:});
load('some_description.txt', var{:}); % <-- Use this if you saved to .txt file.
Then the variable named 'X' will be loaded into the workspace and its columns will be the same feature vectors you computed before.
You will want to replace the some_description part of each file name above and instead use something that allows you to easily identify which data set's feature vectors are saved in the file (if you have multiple data sets). Your array of feature vectors may also be called something besides X, so you can change the name accordingly.