Performance of model parameters

I processed 1458 models in this spreadsheet (see models tab). As mentioned in my previous post, these are the parameters:

  • model_number – the identification number
  • batch_size – the size of the batch. 8 or 16
  • filters1 – the number of filters for layer 1. (possible values 32,64 or 128)
model.add(Conv2D(filters=filters1, 
  • dropout1 – Dropout (if greater than 0) (possible values 0,0.25,0.5)

if(dropout1>0):
model.add(Dropout(dropout1))

  • filters2 – the number of filters for layer 2. (32,64 or 128)
  • dropout2 – dropout for layer 2. (0,0.25,0.5)
  • filters3 – the number of filters for layer 3. (32,64 or 128)
  • dropout3 – dropout for layer 3. (0,0.25,0.5)
  • loss – the result of running the model.
  • accuracy – (as above.)

A review of the spreadsheet shows that many of the models I ran have poor accuracy, even as low as 1:3 (0.333333333333333) to predict a match between three coin obverse portraits (Elizabeth II, George VI and Abraham Lincoln). I did find some models with an accuracy above 80% yet I wanted to see if there were patterns I could use to improve my set of models. So I used a Seaborn heatmap of the models (below) for batch sizes of 8,16 and both together.

Heatmap of models 2 – 730 (batch size = 8). There is a slightly negative relationship between accuracy and dropout1. It is possible it would be more efficient to use dropout values of 0 or 0.25 and not 0.5.
Heatmap of models 731 – 1459 (batch size = 16).
Heatmap of models 2 – 1459 (batch sizes of 8,16).

The heatmap for loss and accuracy to the model parameters in the last two rows shows there is a slightly negative relationship between accuracy and dropout1. It is possible it would be more efficient to use dropout values of 0 or 0.25 and not 0.5 when running these models again. It also seems like there is a slightly positive relationship between batch size and accuracy possibly indicating that larger batch sizes may lead to more accurate models. I have been running a set of models with a batch size of 32 to see if this pattern becomes stronger. (same spreadsheet, models tab.) I am also going to validate my approach through additional personal (not machine) learning.