Thursday, 9 April 2015

Running time analysis

Measuring time

The main purpose of this project is to speedup the architecture selection process. In order to make my experiments representative, I run all the models on the same GPU with the same optimization method (RMSProp) with the same hyper parameters.


The model I used for this task is quite small and the majority of parameters are the parameters of the fully connected layer.  Nevertheless, fixing weights being random in first layers helps to speed up the training procedure. 

I conducted experiments for 2-5 fixed layers first and then after about a week I run a reference model and a model with 1 fixed layer. Seems, that something had changed within this week and two later models run much faster. I concluded, that for this small models the running time depends on other processes. I put in the table number of parameters for each model and time per epoch.
Fixed layers Trained parameters One epoch time, s
486,000 237
1 484,000 204
2 482,000 347
3 470,000 238
4 434,000 218
5 360,0000 183


  • Fixed random features help to speed up the learning 
  • The impact is not so big for small models
  • Optimizing may be more difficult for models with fixed weights

Wednesday, 8 April 2015


Experimental setup

As a reference model I used a comparably small network: 
Layer Structure Dimension
1 Convolution 10 filters 3x3
2 Convolution 20 filters 3x3, pooling 2x2
3 Convolution 64 filters 3x3, pooling 2x2
4 Convolution 64 filters 3x3, pooling 2x2
5 Convolution 128 filters 3x3, pooling 2x2
6 Fully connected 256
7 Fully connected 256

Then I trained six models: a reference one, then I fixed first layer, first and second and so forth. The idea is illustrated on the picture (grey colour means, that the weights are fixed):


I plotted training and validation error during training. 
We can see, that models with fixed parameters output reasonable results. Also it is interesting, that fixing weights sometimes gives regularizing effect (like on blue, magenta and yellow lines). It is not so amazing, because we are decreasing the capacity of the model.

Tuesday, 7 April 2015

Work on random features started

Previous work

In several works was mentioned that fixed convolutional weights perform not much worse than training the whole model, an example of these kind of works is Jarrett et al., 2009. Later, Saxe et al. investigated this phenomena from theoretical point of view and concluded, that fine tuning of the fully connected layers has the biggest effect on the training.


It is very tempting to use fixed weights for hyper parameter search since the training of the model with fixed weights should be easier and faster. I'm going to make several experiments to find advantages and drawbacks of this approach and analyze the behaviour of the training procedure in this setup.