MNIST CNN Transfer Learning
Description
This example demonstrates 're-training' of a pre-trained model in the browser. The model was initially
training-mode in Python and converted to TensorFlow.js format. The model can then be further trained using data
from the browser. Re-training an already trained network is called transfer learning.
In this case the pretrained model has been trained on a subset of the MNIST data: only digits 0 - 4. The
data we'll use for transfer learning in the browser consists of the digits 5 - 9. This example shows that the
first several layers of a pretrained model can be used as feature extractors on new data during transfer
learning
and thus result in faster training on the new data.
Status
Load Model
Load hosted pretrained model
Load local pretrained model
Retrain Model
When retraining the model 3 different approaches are available.
Freeze feature layers: This is the most typical transfer learning procedure, we
freeze the first seven layers of the base model and only allow the final
5 layers to be trained on the new data.
Don't freeze feature layers: Choose this option to compare with the default option. A
few things to pay attention to include longer model convergence time and more larger shifts in weights (and
thus loss values) as the model trains.
Reinitialize weights: This will reset all the weights, effectively training the model
from scratch.
Training mode:
Freeze feature layers
Don't freeze feature layers
Reinitialize weights
Epochs
Retrain
Test Model
Below is an "ASCII" bit map of some test examples from the digits in the new data set for transfer learning:
digits 5 through 9. The numbers are the grayscale integer values from the image. You can edit the values below
to see the effect of editing pixel values on the classification probabilities output by the model below.
Select test example
5_1
5_2
5_3
5_4
5_5
5_6
5_7
5_8
5_9
5_10
6_1
6_2
6_3
6_4
6_5
6_6
6_7
6_8
6_9
6_10
7_1
7_2
7_3
7_4
7_5
7_6
7_7
7_8
7_9
7_10
8_1
8_2
8_3
8_4
8_5
8_6
8_7
8_8
8_9
8_10
9_1
9_2
9_3
9_4
9_5
9_6
9_7
9_8
9_9
9_10
Model Output
Output class: